Mode identification using stochastic hybrid models with applications to conflict detection and resolution

NASA Astrophysics Data System (ADS)

Naseri Kouzehgarani, Asal

2009-12-01

Most models of aircraft trajectories are non-linear and stochastic in nature; and their internal parameters are often poorly defined. The ability to model, simulate and analyze realistic air traffic management conflict detection scenarios in a scalable, composable, multi-aircraft fashion is an extremely difficult endeavor. Accurate techniques for aircraft mode detection are critical in order to enable the precise projection of aircraft conflicts, and for the enactment of altitude separation resolution strategies. Conflict detection is an inherently probabilistic endeavor; our ability to detect conflicts in a timely and accurate manner over a fixed time horizon is traded off against the increased human workload created by false alarms---that is, situations that would not develop into an actual conflict, or would resolve naturally in the appropriate time horizon-thereby introducing a measure of probabilistic uncertainty in any decision aid fashioned to assist air traffic controllers. The interaction of the continuous dynamics of the aircraft, used for prediction purposes, with the discrete conflict detection logic gives rise to the hybrid nature of the overall system. The introduction of the probabilistic element, common to decision alerting and aiding devices, places the conflict detection and resolution problem in the domain of probabilistic hybrid phenomena. A hidden Markov model (HMM) has two stochastic components: a finite-state Markov chain and a finite set of output probability distributions. In other words an unobservable stochastic process (hidden) that can only be observed through another set of stochastic processes that generate the sequence of observations. The problem of self separation in distributed air traffic management reduces to the ability of aircraft to communicate state information to neighboring aircraft, as well as model the evolution of aircraft trajectories between communications, in the presence of probabilistic uncertain dynamics as well as partially observable and uncertain data. We introduce the Hybrid Hidden Markov Modeling (HHMM) formalism to enable the prediction of the stochastic aircraft states (and thus, potential conflicts), by combining elements of the probabilistic timed input output automaton and the partially observable Markov decision process frameworks, along with the novel addition of a Markovian scheduler to remove the non-deterministic elements arising from the enabling of several actions simultaneously. Comparisons of aircraft in level, climbing/descending and turning flight are performed, and unknown flight track data is evaluated probabilistically against the tuned model in order to assess the effectiveness of the model in detecting the switch between multiple flight modes for a given aircraft. This also allows for the generation of probabilistic distribution over the execution traces of the hybrid hidden Markov model, which then enables the prediction of the states of aircraft based on partially observable and uncertain data. Based on the composition properties of the HHMM, we study a decentralized air traffic system where aircraft are moving along streams and can perform cruise, accelerate, climb and turn maneuvers. We develop a common decentralized policy for conflict avoidance with spatially distributed agents (aircraft in the sky) and assure its safety properties via correctness proofs.

Superposition-Based Analysis of First-Order Probabilistic Timed Automata

NASA Astrophysics Data System (ADS)

Fietzke, Arnaud; Hermanns, Holger; Weidenbach, Christoph

This paper discusses the analysis of first-order probabilistic timed automata (FPTA) by a combination of hierarchic first-order superposition-based theorem proving and probabilistic model checking. We develop the overall semantics of FPTAs and prove soundness and completeness of our method for reachability properties. Basically, we decompose FPTAs into their time plus first-order logic aspects on the one hand, and their probabilistic aspects on the other hand. Then we exploit the time plus first-order behavior by hierarchic superposition over linear arithmetic. The result of this analysis is the basis for the construction of a reachability equivalent (to the original FPTA) probabilistic timed automaton to which probabilistic model checking is finally applied. The hierarchic superposition calculus required for the analysis is sound and complete on the first-order formulas generated from FPTAs. It even works well in practice. We illustrate the potential behind it with a real-life DHCP protocol example, which we analyze by means of tool chain support.

Failed rib region prediction in a human body model during crash events with precrash braking.

PubMed

Guleyupoglu, B; Koya, B; Barnard, R; Gayzik, F S

2018-02-28

The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model. The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions. Kinematics between both methods were similar (peak max deviation: ΔX head = 17 mm; ΔZ head = 4 mm; ΔX thorax = 5 mm; ΔZ thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be predicted. Both the probabilistic and deterministic methods indicate similar trends with regards to the effect of precrash braking; however, there are tradeoffs. The deterministic failed region method is more spatially sensitive to failure and is more sensitive to belt loads. The probabilistic failed region method allows for increased capability in postprocessing with respect to age. The probabilistic failed region method predicted more failed regions than the deterministic failed region method due to force distribution differences.

A generative, probabilistic model of local protein structure.

PubMed

Boomsma, Wouter; Mardia, Kanti V; Taylor, Charles C; Ferkinghoff-Borg, Jesper; Krogh, Anders; Hamelryck, Thomas

2008-07-01

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

Modeling the Effect of Reward Amount on Probability Discounting

ERIC Educational Resources Information Center

Myerson, Joel; Green, Leonard; Morris, Joshua

2011-01-01

The present study with college students examined the effect of amount on the discounting of probabilistic monetary rewards. A hyperboloid function accurately described the discounting of hypothetical rewards ranging in amount from $20 to $10,000,000. The degree of discounting increased continuously with amount of probabilistic reward. This effect…

Understanding Rasch Measurement: Rasch Models Overview.

ERIC Educational Resources Information Center

Wright, Benjamin D.; Mok, Magdalena

2000-01-01

Presents an overview of Rasch measurement models that begins with a conceptualization of continuous experiences often captured as discrete observations. Discusses the mathematical properties of the Rasch family of models that allow the transformation of discrete deterministic counts into continuous probabilistic abstractions. Also discusses six of…

Effect of time dependence on probabilistic seismic-hazard maps and deaggregation for the central Apennines, Italy

USGS Publications Warehouse

Akinci, A.; Galadini, F.; Pantosti, D.; Petersen, M.; Malagnini, L.; Perkins, D.

2009-01-01

We produce probabilistic seismic-hazard assessments for the central Apennines, Italy, using time-dependent models that are characterized using a Brownian passage time recurrence model. Using aperiodicity parameters, ?? of 0.3, 0.5, and 0.7, we examine the sensitivity of the probabilistic ground motion and its deaggregation to these parameters. For the seismic source model we incorporate both smoothed historical seismicity over the area and geological information on faults. We use the maximum magnitude model for the fault sources together with a uniform probability of rupture along the fault (floating fault model) to model fictitious faults to account for earthquakes that cannot be correlated with known geologic structural segmentation.

The analysis of the possibility of using 10-minute rainfall series to determine the maximum rainfall amount with 5 minutes duration

NASA Astrophysics Data System (ADS)

Kaźmierczak, Bartosz; Wartalska, Katarzyna; Wdowikowski, Marcin; Kotowski, Andrzej

2017-11-01

Modern scientific research in the area of heavy rainfall analysis regarding to the sewerage design indicates the need to develop and use probabilistic rain models. One of the issues that remains to be resolved is the length of the shortest amount of rain to be analyzed. It is commonly believed that the best time is 5 minutes, while the least rain duration measured by the national services is often 10 or even 15 minutes. Main aim of this paper is to present the difference between probabilistic rainfall models results given from rainfall time series including and excluding 5 minutes rainfall duration. Analysis were made for long-time period from 1961-2010 on polish meteorological station Legnica. To develop best fitted to measurement rainfall data probabilistic model 4 probabilistic distributions were used. Results clearly indicates that models including 5 minutes rainfall duration remains more appropriate to use.

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

PubMed Central

Ehrenfeld, Stephan; Herbort, Oliver; Butz, Martin V.

2013-01-01

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control. PMID:24191151

Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. PMID:19888348

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced.more » The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Qin; Florita, Anthony R; Krishnan, Venkat K

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) ismore » analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.« less

Global/local methods for probabilistic structural analysis

NASA Technical Reports Server (NTRS)

Millwater, H. R.; Wu, Y.-T.

1993-01-01

A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.

Global/local methods for probabilistic structural analysis

NASA Astrophysics Data System (ADS)

Millwater, H. R.; Wu, Y.-T.

1993-04-01

A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.

Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Steinsland, Ingelin

2014-05-01

This study introduces a methodology for the construction of probabilistic inflow forecasts for multiple catchments and lead times, and investigates criterions for evaluation of multi-variate forecasts. A post-processing approach is used, and a Gaussian model is applied for transformed variables. The post processing model has two main components, the mean model and the dependency model. The mean model is used to estimate the marginal distributions for forecasted inflow for each catchment and lead time, whereas the dependency models was used to estimate the full multivariate distribution of forecasts, i.e. co-variances between catchments and lead times. In operational situations, it is a straightforward task to use the models to sample inflow ensembles which inherit the dependencies between catchments and lead times. The methodology was tested and demonstrated in the river systems linked to the Ulla-Førre hydropower complex in southern Norway, where simultaneous probabilistic forecasts for five catchments and ten lead times were constructed. The methodology exhibits sufficient flexibility to utilize deterministic flow forecasts from a numerical hydrological model as well as statistical forecasts such as persistent forecasts and sliding window climatology forecasts. It also deals with variation in the relative weights of these forecasts with both catchment and lead time. When evaluating predictive performance in original space using cross validation, the case study found that it is important to include the persistent forecast for the initial lead times and the hydrological forecast for medium-term lead times. Sliding window climatology forecasts become more important for the latest lead times. Furthermore, operationally important features in this case study such as heteroscedasticity, lead time varying between lead time dependency and lead time varying between catchment dependency are captured. Two criterions were used for evaluating the added value of the dependency model. The first one was the Energy score (ES) that is a multi-dimensional generalization of continuous rank probability score (CRPS). ES was calculated for all lead-times and catchments together, for each catchment across all lead times and for each lead time across all catchments. The second criterion was to use CRPS for forecasted inflows accumulated over several lead times and catchments. The results showed that ES was not very sensitive to correct covariance structure, whereas CRPS for accumulated flows where more suitable for evaluating the dependency model. This indicates that it is more appropriate to evaluate relevant univariate variables that depends on the dependency structure then to evaluate the multivariate forecast directly.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

PubMed

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

NASA Astrophysics Data System (ADS)

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow

NASA Astrophysics Data System (ADS)

Gupta, Atma Ram; Kumar, Ashwani

2017-12-01

Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.

Long-range dismount activity classification: LODAC

NASA Astrophysics Data System (ADS)

Garagic, Denis; Peskoe, Jacob; Liu, Fang; Cuevas, Manuel; Freeman, Andrew M.; Rhodes, Bradley J.

2014-06-01

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non-parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self-tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti-access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross-modal signal generation) and discovers the critical cross-modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non-parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Probabilistic modeling of discourse-aware sentence processing.

PubMed

Dubey, Amit; Keller, Frank; Sturt, Patrick

2013-07-01

Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. Copyright © 2013 Cognitive Science Society, Inc.

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Hochhalter, Jacob D.; Newman, John A.; Leser, William P.; Warner, James E.; Wawrzynek, Paul A.; Yuan, Fuh-Gwo

2015-01-01

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Trends in the predictive performance of raw ensemble weather forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

2015-04-01

Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near-surface wind speed, suggests that improvements to the atmospheric model have an effect quite different from what calibration by statistical post-processing is doing. That is, they are increasing potential skill. Thus this study indicates that (a) further model development is important even if one is just interested in point forecasts, and (b) statistical post-processing is important because it will keep adding skill in the foreseeable future.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

PubMed

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

PubMed Central

Pecevski, Dejan

2016-01-01

Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

Recent development and biomedical applications of probabilistic Boolean networks

PubMed Central

2013-01-01

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. PMID:23815817

Multiscale modelling and analysis of collective decision making in swarm robotics.

PubMed

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Probabilistic self-organizing maps for continuous data.

PubMed

Lopez-Rubio, Ezequiel

2010-10-01

The original self-organizing feature map did not define any probability distribution on the input space. However, the advantages of introducing probabilistic methodologies into self-organizing map models were soon evident. This has led to a wide range of proposals which reflect the current emergence of probabilistic approaches to computational intelligence. The underlying estimation theories behind them derive from two main lines of thought: the expectation maximization methodology and stochastic approximation methods. Here, we present a comprehensive view of the state of the art, with a unifying perspective of the involved theoretical frameworks. In particular, we examine the most commonly used continuous probability distributions, self-organization mechanisms, and learning schemes. Special emphasis is given to the connections among them and their relative advantages depending on the characteristics of the problem at hand. Furthermore, we evaluate their performance in two typical applications of self-organizing maps: classification and visualization.

A temporal-spatial postprocessing model for probabilistic run-off forecast. With a case study from Ulla-Førre with five catchments and ten lead times

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

This work is driven by the needs of next generation short term optimization methodology for hydro power production. Stochastic optimization are about to be introduced; i.e. optimizing when available resources (water) and utility (prices) are uncertain. In this paper we focus on the available resources, i.e. water, where uncertainty mainly comes from uncertainty in future runoff. When optimizing a water system all catchments and several lead times have to be considered simultaneously. Depending on the system of hydropower reservoirs, it might be a set of headwater catchments, a system of upstream /downstream reservoirs where water used from one catchment /dam arrives in a lower catchment maybe days later, or a combination of both. The aim of this paper is therefore to construct a simultaneous probabilistic forecast for several catchments and lead times, i.e. to provide a predictive distribution for the forecasts. Stochastic optimization methods need samples/ensembles of run-off forecasts as input. Hence, it should also be possible to sample from our probabilistic forecast. A post-processing approach is taken, and an error model based on Box- Cox transformation, power transform and a temporal-spatial copula model is used. It accounts for both between catchment and between lead time dependencies. In operational use it is strait forward to sample run-off ensembles from this models that inherits the catchment and lead time dependencies. The methodology is tested and demonstrated in the Ulla-Førre river system, and simultaneous probabilistic forecasts for five catchments and ten lead times are constructed. The methodology has enough flexibility to model operationally important features in this case study such as hetroscadasety, lead-time varying temporal dependency and lead-time varying inter-catchment dependency. Our model is evaluated using CRPS for marginal predictive distributions and energy score for joint predictive distribution. It is tested against deterministic run-off forecast, climatology forecast and a persistent forecast, and is found to be the better probabilistic forecast for lead time grater then two. From an operational point of view the results are interesting as the between catchment dependency gets stronger with longer lead-times.

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

NASA Astrophysics Data System (ADS)

Sun, Wei; Chang, K. C.

2005-05-01

Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.

A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation

PubMed Central

Eddy, Sean R.

2008-01-01

Sequence database searches require accurate estimation of the statistical significance of scores. Optimal local sequence alignment scores follow Gumbel distributions, but determining an important parameter of the distribution (λ) requires time-consuming computational simulation. Moreover, optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty (“Forward” scores), but the expected distribution of Forward scores remains unknown. Here, I conjecture that both expected score distributions have simple, predictable forms when full probabilistic modeling methods are used. For a probabilistic model of local sequence alignment, optimal alignment bit scores (“Viterbi” scores) are Gumbel-distributed with constant λ = log 2, and the high scoring tail of Forward scores is exponential with the same constant λ. Simulation studies support these conjectures over a wide range of profile/sequence comparisons, using 9,318 profile-hidden Markov models from the Pfam database. This enables efficient and accurate determination of expectation values (E-values) for both Viterbi and Forward scores for probabilistic local alignments. PMID:18516236

Extended applications of track irregularity probabilistic model and vehicle-slab track coupled model on dynamics of railway systems

NASA Astrophysics Data System (ADS)

Xu, Lei; Zhai, Wanming; Gao, Jianmin

2017-11-01

Track irregularities are inevitably in a process of stochastic evolution due to the uncertainty and continuity of wheel-rail interactions. For depicting the dynamic behaviours of vehicle-track coupling system caused by track random irregularities thoroughly, it is a necessity to develop a track irregularity probabilistic model to simulate rail surface irregularities with ergodic properties on amplitudes, wavelengths and probabilities, and to build a three-dimensional vehicle-track coupled model by properly considering the wheel-rail nonlinear contact mechanisms. In the present study, the vehicle-track coupled model is programmed by combining finite element method with wheel-rail coupling model firstly. Then, in light of the capability of power spectral density (PSD) in characterising amplitudes and wavelengths of stationary random signals, a track irregularity probabilistic model is presented to reveal and simulate the whole characteristics of track irregularity PSD. Finally, extended applications from three aspects, that is, extreme analysis, reliability analysis and response relationships between dynamic indices, are conducted to the evaluation and application of the proposed models.

Toward a Probabilistic Phenological Model for Wheat Growing Degree Days (GDD)

NASA Astrophysics Data System (ADS)

Rahmani, E.; Hense, A.

2017-12-01

Are there deterministic relations between phenological and climate parameters? The answer is surely `No'. This answer motivated us to solve the problem through probabilistic theories. Thus, we developed a probabilistic phenological model which has the advantage of giving additional information in terms of uncertainty. To that aim, we turned to a statistical analysis named survival analysis. Survival analysis deals with death in biological organisms and failure in mechanical systems. In survival analysis literature, death or failure is considered as an event. By event, in this research we mean ripening date of wheat. We will assume only one event in this special case. By time, we mean the growing duration from sowing to ripening as lifetime for wheat which is a function of GDD. To be more precise we will try to perform the probabilistic forecast for wheat ripening. The probability value will change between 0 and 1. Here, the survivor function gives the probability that the not ripened wheat survives longer than a specific time or will survive to the end of its lifetime as a ripened crop. The survival function at each station is determined by fitting a normal distribution to the GDD as the function of growth duration. Verification of the models obtained is done using CRPS skill score (CRPSS). The positive values of CRPSS indicate the large superiority of the probabilistic phonologic survival model to the deterministic models. These results demonstrate that considering uncertainties in modeling are beneficial, meaningful and necessary. We believe that probabilistic phenological models have the potential to help reduce the vulnerability of agricultural production systems to climate change thereby increasing food security.

Analysis of Phase-Type Stochastic Petri Nets With Discrete and Continuous Timing

NASA Technical Reports Server (NTRS)

Jones, Robert L.; Goode, Plesent W. (Technical Monitor)

2000-01-01

The Petri net formalism is useful in studying many discrete-state, discrete-event systems exhibiting concurrency, synchronization, and other complex behavior. As a bipartite graph, the net can conveniently capture salient aspects of the system. As a mathematical tool, the net can specify an analyzable state space. Indeed, one can reason about certain qualitative properties (from state occupancies) and how they arise (the sequence of events leading there). By introducing deterministic or random delays, the model is forced to sojourn in states some amount of time, giving rise to an underlying stochastic process, one that can be specified in a compact way and capable of providing quantitative, probabilistic measures. We formalize a new non-Markovian extension to the Petri net that captures both discrete and continuous timing in the same model. The approach affords efficient, stationary analysis in most cases and efficient transient analysis under certain restrictions. Moreover, this new formalism has the added benefit in modeling fidelity stemming from the simultaneous capture of discrete- and continuous-time events (as opposed to capturing only one and approximating the other). We show how the underlying stochastic process, which is non-Markovian, can be resolved into simpler Markovian problems that enjoy efficient solutions. Solution algorithms are provided that can be easily programmed.

Probabilistic assessment methodology for continuous-type petroleum accumulations

USGS Publications Warehouse

Crovelli, R.A.

2003-01-01

The analytic resource assessment method, called ACCESS (Analytic Cell-based Continuous Energy Spreadsheet System), was developed to calculate estimates of petroleum resources for the geologic assessment model, called FORSPAN, in continuous-type petroleum accumulations. The ACCESS method is based upon mathematical equations derived from probability theory in the form of a computer spreadsheet system. ?? 2003 Elsevier B.V. All rights reserved.

Integration of Advanced Probabilistic Analysis Techniques with Multi-Physics Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cetiner, Mustafa Sacit; none,; Flanagan, George F.

2014-07-30

An integrated simulation platform that couples probabilistic analysis-based tools with model-based simulation tools can provide valuable insights for reactive and proactive responses to plant operating conditions. The objective of this work is to demonstrate the benefits of a partial implementation of the Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Detailed Framework Specification through the coupling of advanced PRA capabilities and accurate multi-physics plant models. Coupling a probabilistic model with a multi-physics model will aid in design, operations, and safety by providing a more accurate understanding of plant behavior. This represents the first attempt at actually integrating these two typesmore » of analyses for a control system used for operations, on a faster than real-time basis. This report documents the development of the basic communication capability to exchange data with the probabilistic model using Reliability Workbench (RWB) and the multi-physics model using Dymola. The communication pathways from injecting a fault (i.e., failing a component) to the probabilistic and multi-physics models were successfully completed. This first version was tested with prototypic models represented in both RWB and Modelica. First, a simple event tree/fault tree (ET/FT) model was created to develop the software code to implement the communication capabilities between the dynamic-link library (dll) and RWB. A program, written in C#, successfully communicates faults to the probabilistic model through the dll. A systems model of the Advanced Liquid-Metal Reactor–Power Reactor Inherently Safe Module (ALMR-PRISM) design developed under another DOE project was upgraded using Dymola to include proper interfaces to allow data exchange with the control application (ConApp). A program, written in C+, successfully communicates faults to the multi-physics model. The results of the example simulation were successfully plotted.« less

Error Discounting in Probabilistic Category Learning

PubMed Central

Craig, Stewart; Lewandowsky, Stephan; Little, Daniel R.

2011-01-01

Some current theories of probabilistic categorization assume that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report two probabilistic-categorization experiments that investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results are indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning. PMID:21355666

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components

NASA Technical Reports Server (NTRS)

1991-01-01

The fourth year of technical developments on the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) system for Probabilistic Structural Analysis Methods is summarized. The effort focused on the continued expansion of the Probabilistic Finite Element Method (PFEM) code, the implementation of the Probabilistic Boundary Element Method (PBEM), and the implementation of the Probabilistic Approximate Methods (PAppM) code. The principal focus for the PFEM code is the addition of a multilevel structural dynamics capability. The strategy includes probabilistic loads, treatment of material, geometry uncertainty, and full probabilistic variables. Enhancements are included for the Fast Probability Integration (FPI) algorithms and the addition of Monte Carlo simulation as an alternate. Work on the expert system and boundary element developments continues. The enhanced capability in the computer codes is validated by applications to a turbine blade and to an oxidizer duct.

Reduction Continuous Rank Probability Score for Hydrological Ensemble Prediction System

NASA Astrophysics Data System (ADS)

Trinh, Nguyen Bao; Thielen Del-Pozo, Jutta; Pappenberger, Florian; Cloke, Hannah L.; Bogner, Konrad

2010-05-01

Ensemble Prediction System (EPS), calculated operationally by the weather services for various lead-times, are increasingly used as input to hydrological models to extend warning times from short- to medium and even long-range. Although the general skill of EPS has been demonstrated to increase continuously over the past decades, it remains comparatively low for precipitation, one of the driving forces of hydrological processes. Due to the non-linear integrating nature of river runoff and the complexities of catchment runoff processes, one cannot assume that the skill of the hydrological forecasts is necessarily similar to the skill of the meteorological predictions. Furthermore, due to the integrating nature of discharge, which accumulates effects from upstream catchment and slow-responding groundwater processes, commonly applied skill scores in meteorology may not be fully adapted to describe the skill of probabilistic discharge predictions. For example, while for hydrological applications it may be interesting to compare the forecast skill between upstream and downstream stations, meteorological applications focus more on climatologically relevant regions. In this paper, a range of widely used probabilistic skill scores for assessing reliability, spread-skill, sharpness and bias are calculated for a 12 months case study in the Danube river basin. The Continuous Rank Probability Score (CRPS) is demonstrated to have deficiencies when comparing skill of discharge forecast for different hydrological stations. Therefore, we propose a modified CRPS that allows this comparison and is therefore particularly useful for hydrological applications.

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

PubMed Central

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable. PMID:25369026

Envisioning Nano Release Dynamics in a Changing World: Using Dynamic Probabilistic Modeling to Assess Future Environmental Emissions of Engineered Nanomaterials.

PubMed

Sun, Tian Yin; Mitrano, Denise M; Bornhöft, Nikolaus A; Scheringer, Martin; Hungerbühler, Konrad; Nowack, Bernd

2017-03-07

The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental emissions. Material flow models (MFA) have been used to provide predicted environmental emissions but most current nano-MFA models consider neither the rapid development of ENM production nor the fact that a large proportion of ENM are entering an in-use stock and are released from products over time (i.e., have a lag phase). Here we use dynamic probabilistic material flow modeling to predict scenarios of the future flows of four ENM (nano-TiO 2 , nano-ZnO, nano-Ag and CNT) to environmental compartments and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. In these scenarios, we estimate likely future amounts if the use and distribution of ENM in products continues along current trends (i.e., a business-as-usual approach) and predict the effect of hypothetical trends in the market development of nanomaterials, such as the emergence of a new widely used product or the ban on certain substances, on the flows of nanomaterials to the environment in years to come. We show that depending on the scenario and the product type affected, significant changes of the flows occur over time, driven by the growth of stocks and delayed release dynamics.

Evolution of Particle Size Distributions in Fragmentation Over Time

NASA Astrophysics Data System (ADS)

Charalambous, C. A.; Pike, W. T.

2013-12-01

We present a new model of fragmentation based on a probabilistic calculation of the repeated fracture of a particle population. The resulting continuous solution, which is in closed form, gives the evolution of fragmentation products from an initial block, through a scale-invariant power-law relationship to a final comminuted powder. Models for the fragmentation of particles have been developed separately in mainly two different disciplines: the continuous integro-differential equations of batch mineral grinding (Reid, 1965) and the fractal analysis of geophysics (Turcotte, 1986) based on a discrete model with a single probability of fracture. The first gives a time-dependent development of the particle-size distribution, but has resisted a closed-form solution, while the latter leads to the scale-invariant power laws, but with no time dependence. Bird (2009) recently introduced a bridge between these two approaches with a step-wise iterative calculation of the fragmentation products. The development of the particle-size distribution occurs with discrete steps: during each fragmentation event, the particles will repeatedly fracture probabilistically, cascading down the length scales to a final size distribution reached after all particles have failed to further fragment. We have identified this process as the equivalent to a sequence of trials for each particle with a fixed probability of fragmentation. Although the resulting distribution is discrete, it can be reformulated as a continuous distribution in maturity over time and particle size. In our model, Turcotte's power-law distribution emerges at a unique maturation index that defines a regime boundary. Up to this index, the fragmentation is in an erosional regime with the initial particle size setting the scaling. Fragmentation beyond this index is in a regime of comminution with rebreakage of the particles down to the size limit of fracture. The maturation index can increment continuously, for example under grinding conditions, or as discrete steps, such as with impact events. In both cases our model gives the energy associated with the fragmentation in terms of the developing surface area of the population. We show the agreement of our model to the evolution of particle size distributions associated with episodic and continuous fragmentation and how the evolution of some popular fractals may be represented using this approach. C. A. Charalambous and W. T. Pike (2013). Multi-Scale Particle Size Distributions of Mars, Moon and Itokawa based on a time-maturation dependent fragmentation model. Abstract Submitted to the AGU 46th Fall Meeting. Bird, N. R. A., Watts, C. W., Tarquis, A. M., & Whitmore, A. P. (2009). Modeling dynamic fragmentation of soil. Vadose Zone Journal, 8(1), 197-201. Reid, K. J. (1965). A solution to the batch grinding equation. Chemical Engineering Science, 20(11), 953-963. Turcotte, D. L. (1986). Fractals and fragmentation. Journal of Geophysical Research: Solid Earth 91(B2), 1921-1926.

Event-Based Media Enrichment Using an Adaptive Probabilistic Hypergraph Model.

PubMed

Liu, Xueliang; Wang, Meng; Yin, Bao-Cai; Huet, Benoit; Li, Xuelong

2015-11-01

Nowadays, with the continual development of digital capture technologies and social media services, a vast number of media documents are captured and shared online to help attendees record their experience during events. In this paper, we present a method combining semantic inference and multimodal analysis for automatically finding media content to illustrate events using an adaptive probabilistic hypergraph model. In this model, media items are taken as vertices in the weighted hypergraph and the task of enriching media to illustrate events is formulated as a ranking problem. In our method, each hyperedge is constructed using the K-nearest neighbors of a given media document. We also employ a probabilistic representation, which assigns each vertex to a hyperedge in a probabilistic way, to further exploit the correlation among media data. Furthermore, we optimize the hypergraph weights in a regularization framework, which is solved as a second-order cone problem. The approach is initiated by seed media and then used to rank the media documents using a transductive inference process. The results obtained from validating the approach on an event dataset collected from EventMedia demonstrate the effectiveness of the proposed approach.

Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

What is the Value Added to Adaptation Planning by Probabilistic Projections of Climate Change?

NASA Astrophysics Data System (ADS)

Wilby, R. L.

2008-12-01

Probabilistic projections of climate change offer new sources of risk information to support regional impacts assessment and adaptation options appraisal. However, questions continue to surround how best to apply these scenarios in a practical context, and whether the added complexity and computational burden leads to more robust decision-making. This paper provides an overview of recent efforts in the UK to 'bench-test' frameworks for employing probabilistic projections ahead of the release of the next generation, UKCIP08 projections (in November 2008). This is involving close collaboration between government agencies, research and stakeholder communities. Three examples will be cited to illustrate how probabilistic projections are already informing decisions about future flood risk management in London, water resource planning in trial river basins, and assessments of risks from rising water temperatures to Atlantic salmon stocks in southern England. When compared with conventional deterministic scenarios, ensemble projections allow exploration of a wider range of management options and highlight timescales for implementing adaptation measures. Users of probabilistic scenarios must keep in mind that other uncertainties (e.g., due to impacts model structure and parameterisation) should be handled in an equally rigorous way to those arising from climate models and emission scenarios. Finally, it is noted that a commitment to long-term monitoring is also critical for tracking environmental change, testing model projections, and for evaluating the success (or not) of any scenario-led interventions.

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Jaworske, D. A.; Myers, J. G.; Goodenow, D.; Young, M.; Arellano, J. D.

2016-01-01

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

GENERAL: A modified weighted probabilistic cellular automaton traffic flow model

NASA Astrophysics Data System (ADS)

Zhuang, Qian; Jia, Bin; Li, Xin-Gang

2009-08-01

This paper modifies the weighted probabilistic cellular automaton model (Li X L, Kuang H, Song T, et al 2008 Chin. Phys. B 17 2366) which considered a diversity of traffic behaviors under real traffic situations induced by various driving characters and habits. In the new model, the effects of the velocity at the last time step and drivers' desire for acceleration are taken into account. The fundamental diagram, spatial-temporal diagram, and the time series of one-minute data are analyzed. The results show that this model reproduces synchronized flow. Finally, it simulates the on-ramp system with the proposed model. Some characteristics including the phase diagram are studied.

Cost-effectiveness of continuation maintenance pemetrexed after cisplatin and pemetrexed chemotherapy for advanced nonsquamous non-small-cell lung cancer: estimates from the perspective of the Chinese health care system.

PubMed

Zeng, Xiaohui; Peng, Liubao; Li, Jianhe; Chen, Gannong; Tan, Chongqing; Wang, Siying; Wan, Xiaomin; Ouyang, Lihui; Zhao, Ziying

2013-01-01

Continuation maintenance treatment with pemetrexed is approved by current clinical guidelines as a category 2A recommendation after induction therapy with cisplatin and pemetrexed chemotherapy (CP strategy) for patients with advanced nonsquamous non-small-cell lung cancer (NSCLC). However, the cost-effectiveness of the treatment remains unclear. We completed a trial-based assessment, from the perspective of the Chinese health care system, of the cost-effectiveness of maintenance pemetrexed treatment after a CP strategy for patients with advanced nonsquamous NSCLC. A Markov model was developed to estimate costs and benefits. It was based on a clinical trial that compared continuation maintenance pemetrexed therapy plus best supportive care (BSC) versus placebo plus BSC after a CP strategy for advanced nonsquamous NSCLC. Sensitivity analyses were conducted to assess the stability of the model. The model base case analysis suggested that continuation maintenance pemetrexed therapy after a CP strategy would increase benefits in a 1-, 2-, 5-, or 10-year time horizon, with incremental costs of $183,589.06, $126,353.16, $124,766.68, and $124,793.12 per quality-adjusted life-year gained, respectively. The most sensitive influential variable in the cost-effectiveness analysis was the utility of the progression-free survival state, followed by proportion of patients with postdiscontinuation therapy in both arms, proportion of BSC costs for PFS versus progressed survival state, and cost of pemetrexed. Probabilistic sensitivity analysis indicated that the cost-effective probability of adding continuation maintenance pemetrexed therapy to BSC was zero. One-way and probabilistic sensitivity analyses revealed that the Markov model was robust. Continuation maintenance of pemetrexed after a CP strategy for patients with advanced nonsquamous NSCLC is not cost-effective based on a recent clinical trial. Decreasing the price or adjusting the dosage of pemetrexed may be a better option for meeting the treatment demands of Chinese patients. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

NASA Technical Reports Server (NTRS)

Schumann, Johann; Rozier, Kristin Y.; Reinbacher, Thomas; Mengshoel, Ole J.; Mbaya, Timmy; Ippolito, Corey

2013-01-01

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

Real-time surveillance for abnormal events: the case of influenza outbreaks.

PubMed

Rao, Yao; McCabe, Brendan

2016-06-15

This paper introduces a method of surveillance using deviations from probabilistic forecasts. Realised observations are compared with probabilistic forecasts, and the "deviation" metric is based on low probability events. If an alert is declared, the algorithm continues to monitor until an all-clear is announced. Specifically, this article addresses the problem of syndromic surveillance for influenza (flu) with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu-like illness, and hence, the data are low counts. In accordance with the count properties of the observations, an integer-valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. An application to real flu data indicates that the ideas may have promise. The model estimated on a short run of training data did not declare false alarms when used with new observations deemed in control, ex post. The model easily detected the 2009 H1N1 outbreak. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion.

PubMed

Ehrenfeld, Stephan; Butz, Martin V

2013-02-01

Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.

PubMed

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.

Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks

PubMed Central

Dinov, Martin; Leech, Robert

2017-01-01

Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses. PMID:29163110

Incorporating seismic phase correlations into a probabilistic model of global-scale seismology

NASA Astrophysics Data System (ADS)

Arora, Nimar

2013-04-01

We present a probabilistic model of seismic phases whereby the attributes of the body-wave phases are correlated to those of the first arriving P phase. This model has been incorporated into NET-VISA (Network processing Vertically Integrated Seismic Analysis) a probabilistic generative model of seismic events, their transmission, and detection on a global seismic network. In the earlier version of NET-VISA, seismic phase were assumed to be independent of each other. Although this didn't affect the quality of the inferred seismic bulletin, for the most part, it did result in a few instances of anomalous phase association. For example, an S phase with a smaller slowness than the corresponding P phase. We demonstrate that the phase attributes are indeed highly correlated, for example the uncertainty in the S phase travel time is significantly reduced given the P phase travel time. Our new model exploits these correlations to produce better calibrated probabilities for the events, as well as fewer anomalous associations.

Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses

NASA Astrophysics Data System (ADS)

Serb, Alexander; Bill, Johannes; Khiat, Ali; Berdan, Radu; Legenstein, Robert; Prodromakis, Themis

2016-09-01

In an increasingly data-rich world the need for developing computing systems that cannot only process, but ideally also interpret big data is becoming continuously more pressing. Brain-inspired concepts have shown great promise towards addressing this need. Here we demonstrate unsupervised learning in a probabilistic neural network that utilizes metal-oxide memristive devices as multi-state synapses. Our approach can be exploited for processing unlabelled data and can adapt to time-varying clusters that underlie incoming data by supporting the capability of reversible unsupervised learning. The potential of this work is showcased through the demonstration of successful learning in the presence of corrupted input data and probabilistic neurons, thus paving the way towards robust big-data processors.

Cost-effectiveness of prucalopride in the treatment of chronic constipation in the Netherlands

PubMed Central

Nuijten, Mark J. C.; Dubois, Dominique J.; Joseph, Alain; Annemans, Lieven

2015-01-01

Objective: To assess the cost-effectiveness of prucalopride vs. continued laxative treatment for chronic constipation in patients in the Netherlands in whom laxatives have failed to provide adequate relief. Methods: A Markov model was developed to estimate the cost-effectiveness of prucalopride in patients with chronic constipation receiving standard laxative treatment from the perspective of Dutch payers in 2011. Data sources included published prucalopride clinical trials, published Dutch price/tariff lists, and national population statistics. The model simulated the clinical and economic outcomes associated with prucalopride vs. standard treatment and had a cycle length of 1 month and a follow-up time of 1 year. Response to treatment was defined as the proportion of patients who achieved “normal bowel function”. One-way and probabilistic sensitivity analyses were conducted to test the robustness of the base case. Results: In the base case analysis, the cost of prucalopride relative to continued laxative treatment was € 9015 per quality-adjusted life-year (QALY). Extensive sensitivity analyses and scenario analyses confirmed that the base case cost-effectiveness estimate was robust. One-way sensitivity analyses showed that the model was most sensitive in response to prucalopride; incremental cost-effectiveness ratios ranged from € 6475 to 15,380 per QALY. Probabilistic sensitivity analyses indicated that there is a greater than 80% probability that prucalopride would be cost-effective compared with continued standard treatment, assuming a willingness-to-pay threshold of € 20,000 per QALY from a Dutch societal perspective. A scenario analysis was performed for women only, which resulted in a cost-effectiveness ratio of € 7773 per QALY. Conclusion: Prucalopride was cost-effective in a Dutch patient population, as well as in a women-only subgroup, who had chronic constipation and who obtained inadequate relief from laxatives. PMID:25926794

Development of a Microsoft Excel tool for one-parameter Rasch model of continuous items: an application to a safety attitude survey.

PubMed

Chien, Tsair-Wei; Shao, Yang; Kuo, Shu-Chun

2017-01-10

Many continuous item responses (CIRs) are encountered in healthcare settings, but no one uses item response theory's (IRT) probabilistic modeling to present graphical presentations for interpreting CIR results. A computer module that is programmed to deal with CIRs is required. To present a computer module, validate it, and verify its usefulness in dealing with CIR data, and then to apply the model to real healthcare data in order to show how the CIR that can be applied to healthcare settings with an example regarding a safety attitude survey. Using Microsoft Excel VBA (Visual Basic for Applications), we designed a computer module that minimizes the residuals and calculates model's expected scores according to person responses across items. Rasch models based on a Wright map and on KIDMAP were demonstrated to interpret results of the safety attitude survey. The author-made CIR module yielded OUTFIT mean square (MNSQ) and person measures equivalent to those yielded by professional Rasch Winsteps software. The probabilistic modeling of the CIR module provides messages that are much more valuable to users and show the CIR advantage over classic test theory. Because of advances in computer technology, healthcare users who are familiar to MS Excel can easily apply the study CIR module to deal with continuous variables to benefit comparisons of data with a logistic distribution and model fit statistics.

Seasonal streamflow prediction using ensemble streamflow prediction technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

NASA Astrophysics Data System (ADS)

Singh, Shailesh Kumar

2014-05-01

Streamflow forecasts are essential for making critical decision for optimal allocation of water supplies for various demands that include irrigation for agriculture, habitat for fisheries, hydropower production and flood warning. The major objective of this study is to explore the Ensemble Streamflow Prediction (ESP) based forecast in New Zealand catchments and to highlights the present capability of seasonal flow forecasting of National Institute of Water and Atmospheric Research (NIWA). In this study a probabilistic forecast framework for ESP is presented. The basic assumption in ESP is that future weather pattern were experienced historically. Hence, past forcing data can be used with current initial condition to generate an ensemble of prediction. Small differences in initial conditions can result in large difference in the forecast. The initial state of catchment can be obtained by continuously running the model till current time and use this initial state with past forcing data to generate ensemble of flow for future. The approach taken here is to run TopNet hydrological models with a range of past forcing data (precipitation, temperature etc.) with current initial conditions. The collection of runs is called the ensemble. ESP give probabilistic forecasts for flow. From ensemble members the probability distributions can be derived. The probability distributions capture part of the intrinsic uncertainty in weather or climate. An ensemble stream flow prediction which provide probabilistic hydrological forecast with lead time up to 3 months is presented for Rangitata, Ahuriri, and Hooker and Jollie rivers in South Island of New Zealand. ESP based seasonal forecast have better skill than climatology. This system can provide better over all information for holistic water resource management.

Comparison of the performance and reliability of 18 lumped hydrological models driven by ECMWF rainfall ensemble forecasts: a case study on 29 French catchments

NASA Astrophysics Data System (ADS)

Velázquez, Juan Alberto; Anctil, François; Ramos, Maria-Helena; Perrin, Charles

2010-05-01

An ensemble forecasting system seeks to assess and to communicate the uncertainty of hydrological predictions by proposing, at each time step, an ensemble of forecasts from which one can estimate the probability distribution of the predictant (the probabilistic forecast), in contrast with a single estimate of the flow, for which no distribution is obtainable (the deterministic forecast). In the past years, efforts towards the development of probabilistic hydrological prediction systems were made with the adoption of ensembles of numerical weather predictions (NWPs). The additional information provided by the different available Ensemble Prediction Systems (EPS) was evaluated in a hydrological context on various case studies (see the review by Cloke and Pappenberger, 2009). For example, the European ECMWF-EPS was explored in case studies by Roulin et al. (2005), Bartholmes et al. (2005), Jaun et al. (2008), and Renner et al. (2009). The Canadian EC-EPS was also evaluated by Velázquez et al. (2009). Most of these case studies investigate the ensemble predictions of a given hydrological model, set up over a limited number of catchments. Uncertainty from weather predictions is assessed through the use of meteorological ensembles. However, uncertainty from the tested hydrological model and statistical robustness of the forecasting system when coping with different hydro-meteorological conditions are less frequently evaluated. The aim of this study is to evaluate and compare the performance and the reliability of 18 lumped hydrological models applied to a large number of catchments in an operational ensemble forecasting context. Some of these models were evaluated in a previous study (Perrin et al. 2001) for their ability to simulate streamflow. Results demonstrated that very simple models can achieve a level of performance almost as high (sometimes higher) as models with more parameters. In the present study, we focus on the ability of the hydrological models to provide reliable probabilistic forecasts of streamflow, based on ensemble weather predictions. The models were therefore adapted to run in a forecasting mode, i.e., to update initial conditions according to the last observed discharge at the time of the forecast, and to cope with ensemble weather scenarios. All models are lumped, i.e., the hydrological behavior is integrated over the spatial scale of the catchment, and run at daily time steps. The complexity of tested models varies between 3 and 13 parameters. The models are tested on 29 French catchments. Daily streamflow time series extend over 17 months, from March 2005 to July 2006. Catchment areas range between 1470 km2 and 9390 km2, and represent a variety of hydrological and meteorological conditions. The 12 UTC 10-day ECMWF rainfall ensemble (51 members) was used, which led to daily streamflow forecasts for a 9-day lead time. In order to assess the performance and reliability of the hydrological ensemble predictions, we computed the Continuous Ranked probability Score (CRPS) (Matheson and Winkler, 1976), as well as the reliability diagram (e.g. Wilks, 1995) and the rank histogram (Talagrand et al., 1999). Since the ECMWF deterministic forecasts are also available, the performance of the hydrological forecasting systems was also evaluated by comparing the deterministic score (MAE) with the probabilistic score (CRPS). The results obtained for the 18 hydrological models and the 29 studied catchments are discussed in the perspective of improving the operational use of ensemble forecasting in hydrology. References Bartholmes, J. and Todini, E.: Coupling meteorological and hydrological models for flood forecasting, Hydrol. Earth Syst. Sci., 9, 333-346, 2005. Cloke, H. and Pappenberger, F.: Ensemble Flood Forecasting: A Review. Journal of Hydrology 375 (3-4): 613-626, 2009. Jaun, S., Ahrens, B., Walser, A., Ewen, T., and Schär, C.: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Nat. Hazards Earth Syst. Sci., 8, 281-291, 2008. Matheson, J. E. and Winkler, R. L.: Scoring rules for continuous probability distributions, Manage Sci., 22, 1087-1096, 1976. Perrin, C., Michel C. and Andréassian,V. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments, J. Hydrol., 242, 275-301, 2001. Renner, M., Werner, M. G. F., Rademacher, S., and Sprokkereef, E.: Verification of ensemble flow forecast for the River Rhine, J. Hydrol., 376, 463-475, 2009. Roulin, E. and Vannitsem, S.: Skill of medium-range hydrological ensemble predictions, J. Hydrometeorol., 6, 729-744, 2005. Talagrand, O., Vautard, R., and Strauss, B.: Evaluation of the probabilistic prediction systems, in: Proceedings, ECMWF Workshop on Predictability, Shinfield Park, Reading, Berkshire, ECMWF, 1-25, 1999. Velázquez, J.A., Petit, T., Lavoie, A., Boucher M.-A., Turcotte R., Fortin V., and Anctil, F. : An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting, Hydrol. Earth Syst. Sci., 13, 2221-2231, 2009. Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, San Diego, CA, 465 pp., 1995.

Ensemble assimilation of ARGO temperature profile, sea surface temperature and Altimetric satellite data into an eddy permitting primitive equation model of the North Atlantic ocean

NASA Astrophysics Data System (ADS)

Yan, Yajing; Barth, Alexander; Beckers, Jean-Marie; Candille, Guillem; Brankart, Jean-Michel; Brasseur, Pierre

2015-04-01

Sea surface height, sea surface temperature and temperature profiles at depth collected between January and December 2005 are assimilated into a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. 60 ensemble members are generated by adding realistic noise to the forcing parameters related to the temperature. The ensemble is diagnosed and validated by comparison between the ensemble spread and the model/observation difference, as well as by rank histogram before the assimilation experiments. Incremental analysis update scheme is applied in order to reduce spurious oscillations due to the model state correction. The results of the assimilation are assessed according to both deterministic and probabilistic metrics with observations used in the assimilation experiments and independent observations, which goes further than most previous studies and constitutes one of the original points of this paper. Regarding the deterministic validation, the ensemble means, together with the ensemble spreads are compared to the observations in order to diagnose the ensemble distribution properties in a deterministic way. Regarding the probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centred random variable (RCRV) score in order to investigate the reliability properties of the ensemble forecast system. The improvement of the assimilation is demonstrated using these validation metrics. Finally, the deterministic validation and the probabilistic validation are analysed jointly. The consistency and complementarity between both validations are highlighted. High reliable situations, in which the RMS error and the CRPS give the same information, are identified for the first time in this paper.

From cyclone tracks to the costs of European winter storms: A probabilistic loss assessment model

NASA Astrophysics Data System (ADS)

Renggli, Dominik; Corti, Thierry; Reese, Stefan; Wueest, Marc; Viktor, Elisabeth; Zimmerli, Peter

2014-05-01

The quantitative assessment of the potential losses of European winter storms is essential for the economic viability of a global reinsurance company. For this purpose, reinsurance companies generally use probabilistic loss assessment models. This work presents an innovative approach to develop physically meaningful probabilistic events for Swiss Re's new European winter storm loss model. The meteorological hazard component of the new model is based on cyclone and windstorm tracks identified in the 20th Century Reanalysis data. The knowledge of the evolution of winter storms both in time and space allows the physically meaningful perturbation of properties of historical events (e.g. track, intensity). The perturbation includes a random element but also takes the local climatology and the evolution of the historical event into account. The low-resolution wind footprints taken from 20th Century Reanalysis are processed by a statistical-dynamical downscaling to generate high-resolution footprints of the historical and probabilistic winter storm events. Downscaling transfer functions are generated using ENSEMBLES regional climate model data. The result is a set of reliable probabilistic events representing thousands of years. The event set is then combined with country- and risk-specific vulnerability functions and detailed market- or client-specific exposure information to compute (re-)insurance risk premiums.

Probabilistic Reasoning for Plan Robustness

NASA Technical Reports Server (NTRS)

Schaffer, Steve R.; Clement, Bradley J.; Chien, Steve A.

2005-01-01

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.

A Point Rainfall Generator With Internal Storm Structure

NASA Astrophysics Data System (ADS)

Marien, J. L.; Vandewiele, G. L.

1986-04-01

A point rainfall generator is a probabilistic model for the time series of rainfall as observed in one geographical point. The main purpose of such a model is to generate long synthetic sequences of rainfall for simulation studies. The present generator is a continuous time model based on 13.5 years of 10-min point rainfalls observed in Belgium and digitized with a resolution of 0.1 mm. The present generator attempts to model all features of the rainfall time series which are important for flood studies as accurately as possible. The original aspects of the model are on the one hand the way in which storms are defined and on the other hand the theoretical model for the internal storm characteristics. The storm definition has the advantage that the important characteristics of successive storms are fully independent and very precisely modelled, even on time bases as small as 10 min. The model of the internal storm characteristics has a strong theoretical structure. This fact justifies better the extrapolation of this model to severe storms for which the data are very sparse. This can be important when using the model to simulate severe flood events.

The virtual enhancements - solar proton event radiation (VESPER) model

NASA Astrophysics Data System (ADS)

Aminalragia-Giamini, Sigiava; Sandberg, Ingmar; Papadimitriou, Constantinos; Daglis, Ioannis A.; Jiggens, Piers

2018-02-01

A new probabilistic model introducing a novel paradigm for the modelling of the solar proton environment at 1 AU is presented. The virtual enhancements - solar proton event radiation model (VESPER) uses the European space agency's solar energetic particle environment modelling (SEPEM) Reference Dataset and produces virtual time-series of proton differential fluxes. In this regard it fundamentally diverges from the approach of existing SPE models that are based on probabilistic descriptions of SPE macroscopic characteristics such as peak flux and cumulative fluence. It is shown that VESPER reproduces well the dataset characteristics it uses, and further comparisons with existing models are made with respect to their results. The production of time-series as the main output of the model opens a straightforward way for the calculation of solar proton radiation effects in terms of time-series and the pairing with effects caused by trapped radiation and galactic cosmic rays.

Continuous Shape Estimation of Continuum Robots Using X-ray Images

PubMed Central

Lobaton, Edgar J.; Fu, Jinghua; Torres, Luis G.; Alterovitz, Ron

2015-01-01

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot’s shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints. PMID:26279960

Continuous Shape Estimation of Continuum Robots Using X-ray Images.

PubMed

Lobaton, Edgar J; Fu, Jinghua; Torres, Luis G; Alterovitz, Ron

2013-05-06

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot's shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints.

Hidden Semi-Markov Models and Their Application

NASA Astrophysics Data System (ADS)

Beyreuther, M.; Wassermann, J.

2008-12-01

In the framework of detection and classification of seismic signals there are several different approaches. Our choice for a more robust detection and classification algorithm is to adopt Hidden Markov Models (HMM), a technique showing major success in speech recognition. HMM provide a powerful tool to describe highly variable time series based on a double stochastic model and therefore allow for a broader class description than e.g. template based pattern matching techniques. Being a fully probabilistic model, HMM directly provide a confidence measure of an estimated classification. Furthermore and in contrast to classic artificial neuronal networks or support vector machines, HMM are incorporating the time dependence explicitly in the models thus providing a adequate representation of the seismic signal. As the majority of detection algorithms, HMM are not based on the time and amplitude dependent seismogram itself but on features estimated from the seismogram which characterize the different classes. Features, or in other words characteristic functions, are e.g. the sonogram bands, instantaneous frequency, instantaneous bandwidth or centroid time. In this study we apply continuous Hidden Semi-Markov Models (HSMM), an extension of continuous HMM. The duration probability of a HMM is an exponentially decaying function of the time, which is not a realistic representation of the duration of an earthquake. In contrast HSMM use Gaussians as duration probabilities, which results in an more adequate model. The HSMM detection and classification system is running online as an EARTHWORM module at the Bavarian Earthquake Service. Here the signals that are to be classified simply differ in epicentral distance. This makes it possible to easily decide whether a classification is correct or wrong and thus allows to better evaluate the advantages and disadvantages of the proposed algorithm. The evaluation is based on several month long continuous data and the results are additionally compared to the previously published discrete HMM, continuous HMM and a classic STA/LTA. The intermediate evaluation results are very promising.

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Using probabilistic theory to develop interpretation guidelines for Y-STR profiles.

PubMed

Taylor, Duncan; Bright, Jo-Anne; Buckleton, John

2016-03-01

Y-STR profiling makes up a small but important proportion of forensic DNA casework. Often Y-STR profiles are used when autosomal profiling has failed to yield an informative result. Consequently Y-STR profiles are often from the most challenging samples. In addition to these points, Y-STR loci are linked, meaning that evaluation of haplotype probabilities are either based on overly simplified counting methods or computationally costly genetic models, neither of which extend well to the evaluation of mixed Y-STR data. For all of these reasons Y-STR data analysis has not seen the same advances as autosomal STR data. We present here a probabilistic model for the interpretation of Y-STR data. Due to the fact that probabilistic systems for Y-STR data are still some way from reaching active casework, we also describe how data can be analysed in a continuous way to generate interpretational thresholds and guidelines. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Multi-Hazard Advanced Seismic Probabilistic Risk Assessment Tools and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coleman, Justin L.; Bolisetti, Chandu; Veeraraghavan, Swetha

Design of nuclear power plant (NPP) facilities to resist natural hazards has been a part of the regulatory process from the beginning of the NPP industry in the United States (US), but has evolved substantially over time. The original set of approaches and methods was entirely deterministic in nature and focused on a traditional engineering margins-based approach. However, over time probabilistic and risk-informed approaches were also developed and implemented in US Nuclear Regulatory Commission (NRC) guidance and regulation. A defense-in-depth framework has also been incorporated into US regulatory guidance over time. As a result, today, the US regulatory framework incorporatesmore » deterministic and probabilistic approaches for a range of different applications and for a range of natural hazard considerations. This framework will continue to evolve as a result of improved knowledge and newly identified regulatory needs and objectives, most notably in response to the NRC activities developed in response to the 2011 Fukushima accident in Japan. Although the US regulatory framework has continued to evolve over time, the tools, methods and data available to the US nuclear industry to meet the changing requirements have not kept pace. Notably, there is significant room for improvement in the tools and methods available for external event probabilistic risk assessment (PRA), which is the principal assessment approach used in risk-informed regulations and risk-informed decision-making applied to natural hazard assessment and design. This is particularly true if PRA is applied to natural hazards other than seismic loading. Development of a new set of tools and methods that incorporate current knowledge, modern best practice, and state-of-the-art computational resources would lead to more reliable assessment of facility risk and risk insights (e.g., the SSCs and accident sequences that are most risk-significant), with less uncertainty and reduced conservatisms.« less

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

Leser, Patrick E.; Newman, John A.; Warner, James E.; Leser, William P.; Hochhalter, Jacob D.; Yuan, Fuh-Gwo

2016-01-01

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

Scaling properties of a rice-pile model: inertia and friction effects.

PubMed

Khfifi, M; Loulidi, M

2008-11-01

We present a rice-pile cellular automaton model that includes inertial and friction effects. This model is studied in one dimension, where the updating of metastable sites is done according to a stochastic dynamics governed by a probabilistic toppling parameter p that depends on the accumulated energy of moving grains. We investigate the scaling properties of the model using finite-size scaling analysis. The avalanche size, the lifetime, and the residence time distributions exhibit a power-law behavior. Their corresponding critical exponents, respectively, tau, y, and yr, are not universal. They present continuous variation versus the parameters of the system. The maximal value of the critical exponent tau that our model gives is very close to the experimental one, tau=2.02 [Frette, Nature (London) 379, 49 (1996)], and the probability distribution of the residence time is in good agreement with the experimental results. We note that the critical behavior is observed only in a certain range of parameter values of the system which correspond to low inertia and high friction.

Modeling Array Stations in SIG-VISA

NASA Astrophysics Data System (ADS)

Ding, N.; Moore, D.; Russell, S.

2013-12-01

We add support for array stations to SIG-VISA, a system for nuclear monitoring using probabilistic inference on seismic signals. Array stations comprise a large portion of the IMS network; they can provide increased sensitivity and more accurate directional information compared to single-component stations. Our existing model assumed that signals were independent at each station, which is false when lots of stations are close together, as in an array. The new model removes that assumption by jointly modeling signals across array elements. This is done by extending our existing Gaussian process (GP) regression models, also known as kriging, from a 3-dimensional single-component space of events to a 6-dimensional space of station-event pairs. For each array and each event attribute (including coda decay, coda height, amplitude transfer and travel time), we model the joint distribution across array elements using a Gaussian process that learns the correlation lengthscale across the array, thereby incorporating information of array stations into the probabilistic inference framework. To evaluate the effectiveness of our model, we perform ';probabilistic beamforming' on new events using our GP model, i.e., we compute the event azimuth having highest posterior probability under the model, conditioned on the signals at array elements. We compare the results from our probabilistic inference model to the beamforming currently performed by IMS station processing.

Comparison of Control Approaches in Genetic Regulatory Networks by Using Stochastic Master Equation Models, Probabilistic Boolean Network Models and Differential Equation Models and Estimated Error Analyzes

NASA Astrophysics Data System (ADS)

Caglar, Mehmet Umut; Pal, Ranadip

2011-03-01

Central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid''. However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of cell level data and probabilistic - nonlinear nature of interactions. Several models widely used to analyze and simulate these types of nonlinear interactions. Stochastic Master Equation (SME) models give probabilistic nature of the interactions in a detailed manner, with a high calculation cost. On the other hand Probabilistic Boolean Network (PBN) models give a coarse scale picture of the stochastic processes, with a less calculation cost. Differential Equation (DE) models give the time evolution of mean values of processes in a highly cost effective way. The understanding of the relations between the predictions of these models is important to understand the reliability of the simulations of genetic regulatory networks. In this work the success of the mapping between SME, PBN and DE models is analyzed and the accuracy and affectivity of the control policies generated by using PBN and DE models is compared.

Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

PubMed

Mahajan, Ruhi; Viangteeravat, Teeradache; Akbilgic, Oguz

2017-12-01

A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools. Copyright © 2017 Elsevier B.V. All rights reserved.

Probabilistic Design Storm Method for Improved Flood Estimation in Ungauged Catchments

NASA Astrophysics Data System (ADS)

Berk, Mario; Å pačková, Olga; Straub, Daniel

2017-12-01

The design storm approach with event-based rainfall-runoff models is a standard method for design flood estimation in ungauged catchments. The approach is conceptually simple and computationally inexpensive, but the underlying assumptions can lead to flawed design flood estimations. In particular, the implied average recurrence interval (ARI) neutrality between rainfall and runoff neglects uncertainty in other important parameters, leading to an underestimation of design floods. The selection of a single representative critical rainfall duration in the analysis leads to an additional underestimation of design floods. One way to overcome these nonconservative approximations is the use of a continuous rainfall-runoff model, which is associated with significant computational cost and requires rainfall input data that are often not readily available. As an alternative, we propose a novel Probabilistic Design Storm method that combines event-based flood modeling with basic probabilistic models and concepts from reliability analysis, in particular the First-Order Reliability Method (FORM). The proposed methodology overcomes the limitations of the standard design storm approach, while utilizing the same input information and models without excessive computational effort. Additionally, the Probabilistic Design Storm method allows deriving so-called design charts, which summarize representative design storm events (combinations of rainfall intensity and other relevant parameters) for floods with different return periods. These can be used to study the relationship between rainfall and runoff return periods. We demonstrate, investigate, and validate the method by means of an example catchment located in the Bavarian Pre-Alps, in combination with a simple hydrological model commonly used in practice.

Subseasonal to Seasonal Predictions of U.S. West Coast High Water Levels

NASA Astrophysics Data System (ADS)

Khouakhi, A.; Villarini, G.; Zhang, W.; Slater, L. J.

2017-12-01

Extreme sea levels pose a significant threat to coastal communities, ecosystems, and assets, as they are conducive to coastal flooding, coastal erosion and inland salt-water intrusion. As sea levels continue to rise, these sea level extremes - including occasional minor coastal flooding experienced during high tide (nuisance floods) - are of concern. Extreme sea levels are increasing at many locations around the globe and have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate processes such as the El Niño-Southern Oscillation (ENSO). Here, intra-seasonal to seasonal probabilistic forecasts of high water levels are computed at the Toke Point tide gage station on the US west coast. We first identify the main climate drivers that are responsible for high water levels and examine their predictability using General Circulation Models (GCMs) from the North American Multi-Model Ensemble (NMME). These drivers are then used to develop a probabilistic framework for the seasonal forecasting of high water levels. We focus on the climate controls on the frequency of high water levels using the number of exceedances above the 99.5th percentile and above the nuisance flood level established by the National Weather Service. Our findings indicate good forecast skill at the shortest lead time, with the skill that decreases as we increase the lead time. In general, these models aptly capture the year-to-year variability in the observational records.

Probabilistic objective functions for sensor management

NASA Astrophysics Data System (ADS)

Mahler, Ronald P. S.; Zajic, Tim R.

2004-08-01

This paper continues the investigation of a foundational and yet potentially practical basis for control-theoretic sensor management, using a comprehensive, intuitive, system-level Bayesian paradigm based on finite-set statistics (FISST). In this paper we report our most recent progress, focusing on multistep look-ahead -- i.e., allocation of sensor resources throughout an entire future time-window. We determine future sensor states in the time-window using a "probabilistically natural" sensor management objective function, the posterior expected number of targets (PENT). This objective function is constructed using a new "maxi-PIMS" optimization strategy that hedges against unknowable future observation-collections. PENT is used in conjuction with approximate multitarget filters: the probability hypothesis density (PHD) filter or the multi-hypothesis correlator (MHC) filter.

Assessment of food intake input distributions for use in probabilistic exposure assessments of food additives.

PubMed

Gilsenan, M B; Lambe, J; Gibney, M J

2003-11-01

A key component of a food chemical exposure assessment using probabilistic analysis is the selection of the most appropriate input distribution to represent exposure variables. The study explored the type of parametric distribution that could be used to model variability in food consumption data likely to be included in a probabilistic exposure assessment of food additives. The goodness-of-fit of a range of continuous distributions to observed data of 22 food categories expressed as average daily intakes among consumers from the North-South Ireland Food Consumption Survey was assessed using the BestFit distribution fitting program. The lognormal distribution was most commonly accepted as a plausible parametric distribution to represent food consumption data when food intakes were expressed as absolute intakes (16/22 foods) and as intakes per kg body weight (18/22 foods). Results from goodness-of-fit tests were accompanied by lognormal probability plots for a number of food categories. The influence on food additive intake of using a lognormal distribution to model food consumption input data was assessed by comparing modelled intake estimates with observed intakes. Results from the present study advise some level of caution about the use of a lognormal distribution as a mode of input for food consumption data in probabilistic food additive exposure assessments and the results highlight the need for further research in this area.

Probabilistic Survivability Versus Time Modeling

NASA Technical Reports Server (NTRS)

Joyner, James J., Sr.

2016-01-01

This presentation documents Kennedy Space Center's Independent Assessment work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer during key programmatic reviews and provided the GSDO Program with analyses of how egress time affects the likelihood of astronaut and ground worker survival during an emergency. For each assessment, a team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedy's Vehicle Assembly Building.

Scientific assessment of accuracy, skill and reliability of ocean probabilistic forecast products.

NASA Astrophysics Data System (ADS)

Wei, M.; Rowley, C. D.; Barron, C. N.; Hogan, P. J.

2016-02-01

As ocean operational centers are increasingly adopting and generating probabilistic forecast products for their customers with valuable forecast uncertainties, how to assess and measure these complicated probabilistic forecast products objectively is challenging. The first challenge is how to deal with the huge amount of the data from the ensemble forecasts. The second one is how to describe the scientific quality of probabilistic products. In fact, probabilistic forecast accuracy, skills, reliability, resolutions are different attributes of a forecast system. We briefly introduce some of the fundamental metrics such as the Reliability Diagram, Reliability, Resolution, Brier Score (BS), Brier Skill Score (BSS), Ranked Probability Score (RPS), Ranked Probability Skill Score (RPSS), Continuous Ranked Probability Score (CRPS), and Continuous Ranked Probability Skill Score (CRPSS). The values and significance of these metrics are demonstrated for the forecasts from the US Navy's regional ensemble system with different ensemble members. The advantages and differences of these metrics are studied and clarified.

Probabilistic computer model of optimal runway turnoffs

NASA Technical Reports Server (NTRS)

Schoen, M. L.; Preston, O. W.; Summers, L. G.; Nelson, B. A.; Vanderlinden, L.; Mcreynolds, M. C.

1985-01-01

Landing delays are currently a problem at major air carrier airports and many forecasters agree that airport congestion will get worse by the end of the century. It is anticipated that some types of delays can be reduced by an efficient optimal runway exist system allowing increased approach volumes necessary at congested airports. A computerized Probabilistic Runway Turnoff Model which locates exits and defines path geometry for a selected maximum occupancy time appropriate for each TERPS aircraft category is defined. The model includes an algorithm for lateral ride comfort limits.

Diffusion tensor tractography of the arcuate fasciculus in patients with brain tumors: Comparison between deterministic and probabilistic models

PubMed Central

Li, Zhixi; Peck, Kyung K.; Brennan, Nicole P.; Jenabi, Mehrnaz; Hsu, Meier; Zhang, Zhigang; Holodny, Andrei I.; Young, Robert J.

2014-01-01

Purpose The purpose of this study was to compare the deterministic and probabilistic tracking methods of diffusion tensor white matter fiber tractography in patients with brain tumors. Materials and Methods We identified 29 patients with left brain tumors <2 cm from the arcuate fasciculus who underwent pre-operative language fMRI and DTI. The arcuate fasciculus was reconstructed using a deterministic Fiber Assignment by Continuous Tracking (FACT) algorithm and a probabilistic method based on an extended Monte Carlo Random Walk algorithm. Tracking was controlled using two ROIs corresponding to Broca’s and Wernicke’s areas. Tracts in tumoraffected hemispheres were examined for extension between Broca’s and Wernicke’s areas, anterior-posterior length and volume, and compared with the normal contralateral tracts. Results Probabilistic tracts displayed more complete anterior extension to Broca’s area than did FACT tracts on the tumor-affected and normal sides (p < 0.0001). The median length ratio for tumor: normal sides was greater for probabilistic tracts than FACT tracts (p < 0.0001). The median tract volume ratio for tumor: normal sides was also greater for probabilistic tracts than FACT tracts (p = 0.01). Conclusion Probabilistic tractography reconstructs the arcuate fasciculus more completely and performs better through areas of tumor and/or edema. The FACT algorithm tends to underestimate the anterior-most fibers of the arcuate fasciculus, which are crossed by primary motor fibers. PMID:25328583

The integration of familiarity and recollection information in short-term recognition: modeling speed-accuracy trade-off functions.

PubMed

Göthe, Katrin; Oberauer, Klaus

2008-05-01

Dual process models postulate familiarity and recollection as the basis of the recognition process. We investigated the time-course of integration of the two information sources to one recognition judgment in a working memory task. We tested 24 subjects with a response signal variant of the modified Sternberg recognition task (Oberauer, 2001) to isolate the time course of three different probe types indicating different combinations of familiarity and source information. We compared two mathematical models implementing different ways of integrating familiarity and recollection. Within each model, we tested three assumptions about the nature of the familiarity signal, with familiarity having (a) only positive values, indicating similarity of the probe with the memory list, (b) only negative values, indicating novelty, or (c) both positive and negative values. Both models provided good fits to the data. A model combining the outputs of both processes additively (Integration Model) gave an overall better fit to the data than a model based on a continuous familiarity signal and a probabilistic all-or-none recollection process (Dominance Model).

A Probabilistic Tsunami Hazard Study of the Auckland Region, Part II: Inundation Modelling and Hazard Assessment

NASA Astrophysics Data System (ADS)

Lane, E. M.; Gillibrand, P. A.; Wang, X.; Power, W.

2013-09-01

Regional source tsunamis pose a potentially devastating hazard to communities and infrastructure on the New Zealand coast. But major events are very uncommon. This dichotomy of infrequent but potentially devastating hazards makes realistic assessment of the risk challenging. Here, we describe a method to determine a probabilistic assessment of the tsunami hazard by regional source tsunamis with an "Average Recurrence Interval" of 2,500-years. The method is applied to the east Auckland region of New Zealand. From an assessment of potential regional tsunamigenic events over 100,000 years, the inundation of the Auckland region from the worst 100 events is modelled using a hydrodynamic model and probabilistic inundation depths on a 2,500-year time scale were determined. Tidal effects on the potential inundation were included by coupling the predicted wave heights with the probability density function of tidal heights at the inundation site. Results show that the more exposed northern section of the east coast and outer islands in the Hauraki Gulf face the greatest hazard from regional tsunamis in the Auckland region. Incorporating tidal effects into predictions of inundation reduced the predicted hazard compared to modelling all the tsunamis arriving at high tide giving a more accurate hazard assessment on the specified time scale. This study presents the first probabilistic analysis of dynamic modelling of tsunami inundation for the New Zealand coast and as such provides the most comprehensive assessment of tsunami inundation of the Auckland region from regional source tsunamis available to date.

Using Response Times for Item Selection in Adaptive Testing

ERIC Educational Resources Information Center

van der Linden, Wim J.

2008-01-01

Response times on items can be used to improve item selection in adaptive testing provided that a probabilistic model for their distribution is available. In this research, the author used a hierarchical modeling framework with separate first-level models for the responses and response times and a second-level model for the distribution of the…

Analytical resource assessment method for continuous (unconventional) oil and gas accumulations - The "ACCESS" Method

USGS Publications Warehouse

Crovelli, Robert A.; revised by Charpentier, Ronald R.

2012-01-01

The U.S. Geological Survey (USGS) periodically assesses petroleum resources of areas within the United States and the world. The purpose of this report is to explain the development of an analytic probabilistic method and spreadsheet software system called Analytic Cell-Based Continuous Energy Spreadsheet System (ACCESS). The ACCESS method is based upon mathematical equations derived from probability theory. The ACCESS spreadsheet can be used to calculate estimates of the undeveloped oil, gas, and NGL (natural gas liquids) resources in a continuous-type assessment unit. An assessment unit is a mappable volume of rock in a total petroleum system. In this report, the geologic assessment model is defined first, the analytic probabilistic method is described second, and the spreadsheet ACCESS is described third. In this revised version of Open-File Report 00-044 , the text has been updated to reflect modifications that were made to the ACCESS program. Two versions of the program are added as appendixes.

Is there a basis for preferring characteristic earthquakes over a Gutenberg–Richter distribution in probabilistic earthquake forecasting?

USGS Publications Warehouse

Parsons, Thomas E.; Geist, Eric L.

2009-01-01

The idea that faults rupture in repeated, characteristic earthquakes is central to most probabilistic earthquake forecasts. The concept is elegant in its simplicity, and if the same event has repeated itself multiple times in the past, we might anticipate the next. In practice however, assembling a fault-segmented characteristic earthquake rupture model can grow into a complex task laden with unquantified uncertainty. We weigh the evidence that supports characteristic earthquakes against a potentially simpler model made from extrapolation of a Gutenberg–Richter magnitude-frequency law to individual fault zones. We find that the Gutenberg–Richter model satisfies key data constraints used for earthquake forecasting equally well as a characteristic model. Therefore, judicious use of instrumental and historical earthquake catalogs enables large-earthquake-rate calculations with quantifiable uncertainty that should get at least equal weighting in probabilistic forecasting.

Survey Design Recommendations.

ERIC Educational Resources Information Center

Fisher, William P., Jr.

2000-01-01

Presents 17 rules of thumb to create surveys that are likely to provide data of high enough quality to meet the requirements for measurement specified in a probabilistic conjoint measurement model. Use of these steps should allow the survey to be joined with others measuring the same variable to ensure continued equating with a single reference…

A Probabilistic Approach to Network Event Formation from Pre-Processed Waveform Data

NASA Astrophysics Data System (ADS)

Kohl, B. C.; Given, J.

2017-12-01

The current state of the art for seismic event detection still largely depends on signal detection at individual sensor stations, including picking accurate arrivals times and correctly identifying phases, and relying on fusion algorithms to associate individual signal detections to form event hypotheses. But increasing computational capability has enabled progress toward the objective of fully utilizing body-wave recordings in an integrated manner to detect events without the necessity of previously recorded ground truth events. In 2011-2012 Leidos (then SAIC) operated a seismic network to monitor activity associated with geothermal field operations in western Nevada. We developed a new association approach for detecting and quantifying events by probabilistically combining pre-processed waveform data to deal with noisy data and clutter at local distance ranges. The ProbDet algorithm maps continuous waveform data into continuous conditional probability traces using a source model (e.g. Brune earthquake or Mueller-Murphy explosion) to map frequency content and an attenuation model to map amplitudes. Event detection and classification is accomplished by combining the conditional probabilities from the entire network using a Bayesian formulation. This approach was successful in producing a high-Pd, low-Pfa automated bulletin for a local network and preliminary tests with regional and teleseismic data show that it has promise for global seismic and nuclear monitoring applications. The approach highlights several features that we believe are essential to achieving low-threshold automated event detection: Minimizes the utilization of individual seismic phase detections - in traditional techniques, errors in signal detection, timing, feature measurement and initial phase ID compound and propagate into errors in event formation, Has a formalized framework that utilizes information from non-detecting stations, Has a formalized framework that utilizes source information, in particular the spectral characteristics of events of interest, Is entirely model-based, i.e. does not rely on a priori's - particularly important for nuclear monitoring, Does not rely on individualized signal detection thresholds - it's the network solution that matters.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

Dual Roles for Spike Signaling in Cortical Neural Populations

PubMed Central

Ballard, Dana H.; Jehee, Janneke F. M.

2011-01-01

A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

Language acquisition and use: learning and applying probabilistic constraints.

PubMed

Seidenberg, M S

1997-03-14

What kinds of knowledge underlie the use of language and how is this knowledge acquired? Linguists equate knowing a language with knowing a grammar. Classic "poverty of the stimulus" arguments suggest that grammar identification is an intractable inductive problem and that acquisition is possible only because children possess innate knowledge of grammatical structure. An alternative view is emerging from studies of statistical and probabilistic aspects of language, connectionist models, and the learning capacities of infants. This approach emphasizes continuity between how language is acquired and how it is used. It retains the idea that innate capacities constrain language learning, but calls into question whether they include knowledge of grammatical structure.

Bayesian explorations of fault slip evolution over the earthquake cycle

NASA Astrophysics Data System (ADS)

Duputel, Z.; Jolivet, R.; Benoit, A.; Gombert, B.

2017-12-01

The ever-increasing amount of geophysical data continuously opens new perspectives on fundamental aspects of the seismogenic behavior of active faults. In this context, the recent fleet of SAR satellites including Sentinel-1 and COSMO-SkyMED permits the use of InSAR for time-dependent slip modeling with unprecedented resolution in time and space. However, existing time-dependent slip models rely on spatial smoothing regularization schemes, which can produce unrealistically smooth slip distributions. In addition, these models usually do not include uncertainty estimates thereby reducing the utility of such estimates. Here, we develop an entirely new approach to derive probabilistic time-dependent slip models. This Markov-Chain Monte Carlo method involves a series of transitional steps to predict and update posterior Probability Density Functions (PDFs) of slip as a function of time. We assess the viability of our approach using various slow-slip event scenarios. Using a dense set of SAR images, we also use this method to quantify the spatial distribution and temporal evolution of slip along a creeping segment of the North Anatolian Fault. This allows us to track a shallow aseismic slip transient lasting for about a month with a maximum slip of about 2 cm.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

Development of Testing Methodologies for the Mechanical Properties of MEMS

NASA Technical Reports Server (NTRS)

Ekwaro-Osire, Stephen

2003-01-01

This effort is to investigate and design testing strategies to determine the mechanical properties of MicroElectroMechanical Systems (MEMS) as well as investigate the development of a MEMS Probabilistic Design Methodology (PDM). One item of potential interest is the design of a test for the Weibull size effect in pressure membranes. The Weibull size effect is a consequence of a stochastic strength response predicted from the Weibull distribution. Confirming that MEMS strength is controlled by the Weibull distribution will enable the development of a probabilistic design methodology for MEMS - similar to the GRC developed CARES/Life program for bulk ceramics. However, the primary area of investigation will most likely be analysis and modeling of material interfaces for strength as well as developing a strategy to handle stress singularities at sharp corners, filets, and material interfaces. This will be a continuation of the previous years work. The ultimate objective of this effort is to further develop and verify the ability of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) code to predict the time-dependent reliability of MEMS structures subjected to multiple transient loads.

Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

PubMed

She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

2015-01-01

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

Development of optimization-based probabilistic earthquake scenarios for the city of Tehran

NASA Astrophysics Data System (ADS)

Zolfaghari, M. R.; Peyghaleh, E.

2016-01-01

This paper presents the methodology and practical example for the application of optimization process to select earthquake scenarios which best represent probabilistic earthquake hazard in a given region. The method is based on simulation of a large dataset of potential earthquakes, representing the long-term seismotectonic characteristics in a given region. The simulation process uses Monte-Carlo simulation and regional seismogenic source parameters to generate a synthetic earthquake catalogue consisting of a large number of earthquakes, each characterized with magnitude, location, focal depth and fault characteristics. Such catalogue provides full distributions of events in time, space and size; however, demands large computation power when is used for risk assessment, particularly when other sources of uncertainties are involved in the process. To reduce the number of selected earthquake scenarios, a mixed-integer linear program formulation is developed in this study. This approach results in reduced set of optimization-based probabilistic earthquake scenario, while maintaining shape of hazard curves and full probabilistic picture by minimizing the error between hazard curves driven by full and reduced sets of synthetic earthquake scenarios. To test the model, the regional seismotectonic and seismogenic characteristics of northern Iran are used to simulate a set of 10,000-year worth of events consisting of some 84,000 earthquakes. The optimization model is then performed multiple times with various input data, taking into account probabilistic seismic hazard for Tehran city as the main constrains. The sensitivity of the selected scenarios to the user-specified site/return period error-weight is also assessed. The methodology could enhance run time process for full probabilistic earthquake studies like seismic hazard and risk assessment. The reduced set is the representative of the contributions of all possible earthquakes; however, it requires far less computation power. The authors have used this approach for risk assessment towards identification of effectiveness-profitability of risk mitigation measures, using optimization model for resource allocation. Based on the error-computation trade-off, 62-earthquake scenarios are chosen to be used for this purpose.

A spatio-temporal model for probabilistic seismic hazard zonation of Tehran

NASA Astrophysics Data System (ADS)

Hashemi, Mahdi; Alesheikh, Ali Asghar; Zolfaghari, Mohammad Reza

2013-08-01

A precondition for all disaster management steps, building damage prediction, and construction code developments is a hazard assessment that shows the exceedance probabilities of different ground motion levels at a site considering different near- and far-field earthquake sources. The seismic sources are usually categorized as time-independent area sources and time-dependent fault sources. While the earlier incorporates the small and medium events, the later takes into account only the large characteristic earthquakes. In this article, a probabilistic approach is proposed to aggregate the effects of time-dependent and time-independent sources on seismic hazard. The methodology is then applied to generate three probabilistic seismic hazard maps of Tehran for 10%, 5%, and 2% exceedance probabilities in 50 years. The results indicate an increase in peak ground acceleration (PGA) values toward the southeastern part of the study area and the PGA variations are mostly controlled by the shear wave velocities across the city. In addition, the implementation of the methodology takes advantage of GIS capabilities especially raster-based analyses and representations. During the estimation of the PGA exceedance rates, the emphasis has been placed on incorporating the effects of different attenuation relationships and seismic source models by using a logic tree.

Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making

PubMed Central

Drugowitsch, Jan; Pouget, Alexandre

2012-01-01

Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal evidence suggests that humans and animals follow such a probabilitistic decision strategy, although its neural implementation has yet to be fully characterized. Here we show that that diffusion decision models and attractor network models provide an approximation to the optimal strategy only under certain circumstances. In particular, neither model type is sufficiently flexible to encode the reliability of both the momentary and the accumulated evidence, which is a pre-requisite to accumulate evidence of time-varying reliability. Probabilistic population codes, in contrast, can encode these quantities and, as a consequence, have the potential to implement the optimal strategy accurately. PMID:22884815

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

A framework for the probabilistic analysis of meteotsunamis

USGS Publications Warehouse

Geist, Eric L.; ten Brink, Uri S.; Gove, Matthew D.

2014-01-01

A probabilistic technique is developed to assess the hazard from meteotsunamis. Meteotsunamis are unusual sea-level events, generated when the speed of an atmospheric pressure or wind disturbance is comparable to the phase speed of long waves in the ocean. A general aggregation equation is proposed for the probabilistic analysis, based on previous frameworks established for both tsunamis and storm surges, incorporating different sources and source parameters of meteotsunamis. Parameterization of atmospheric disturbances and numerical modeling is performed for the computation of maximum meteotsunami wave amplitudes near the coast. A historical record of pressure disturbances is used to establish a continuous analytic distribution of each parameter as well as the overall Poisson rate of occurrence. A demonstration study is presented for the northeast U.S. in which only isolated atmospheric pressure disturbances from squall lines and derechos are considered. For this study, Automated Surface Observing System stations are used to determine the historical parameters of squall lines from 2000 to 2013. The probabilistic equations are implemented using a Monte Carlo scheme, where a synthetic catalog of squall lines is compiled by sampling the parameter distributions. For each entry in the catalog, ocean wave amplitudes are computed using a numerical hydrodynamic model. Aggregation of the results from the Monte Carlo scheme results in a meteotsunami hazard curve that plots the annualized rate of exceedance with respect to maximum event amplitude for a particular location along the coast. Results from using multiple synthetic catalogs, resampled from the parent parameter distributions, yield mean and quantile hazard curves. Further refinements and improvements for probabilistic analysis of meteotsunamis are discussed.

Global Infrasound Association Based on Probabilistic Clutter Categorization

NASA Astrophysics Data System (ADS)

Arora, Nimar; Mialle, Pierrick

2016-04-01

The IDC advances its methods and continuously improves its automatic system for the infrasound technology. The IDC focuses on enhancing the automatic system for the identification of valid signals and the optimization of the network detection threshold by identifying ways to refine signal characterization methodology and association criteria. An objective of this study is to reduce the number of associated infrasound arrivals that are rejected from the automatic bulletins when generating the reviewed event bulletins. Indeed, a considerable number of signal detections are due to local clutter sources such as microbaroms, waterfalls, dams, gas flares, surf (ocean breaking waves) etc. These sources are either too diffuse or too local to form events. Worse still, the repetitive nature of this clutter leads to a large number of false event hypotheses due to the random matching of clutter at multiple stations. Previous studies, for example [1], have worked on categorization of clutter using long term trends on detection azimuth, frequency, and amplitude at each station. In this work we continue the same line of reasoning to build a probabilistic model of clutter that is used as part of NETVISA [2], a Bayesian approach to network processing. The resulting model is a fusion of seismic, hydroacoustic and infrasound processing built on a unified probabilistic framework. References: [1] Infrasound categorization Towards a statistics based approach. J. Vergoz, P. Gaillard, A. Le Pichon, N. Brachet, and L. Ceranna. ITW 2011 [2] NETVISA: Network Processing Vertically Integrated Seismic Analysis. N. S. Arora, S. Russell, and E. Sudderth. BSSA 2013

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

The Epistemic Representation of Information Flow Security in Probabilistic Systems

DTIC Science & Technology

1995-06-01

The new characterization also means that our security crite- rion is expressible in a simpler logic and model. 1 Introduction Multilevel security is...ber generator) during its execution. Such probabilistic choices are useful in a multilevel security context for Supported by grants HKUST 608/94E from... 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and

Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics.

PubMed

Chung, Ming-Hua; Wang, Yuping; Tang, Hailin; Zou, Wen; Basinger, John; Xu, Xiaowei; Tong, Weida

2015-01-01

The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the application of text mining tools, like probabilistic topic models, to explore hidden patterns of genomic data and to the extension of altered biological functions. In this paper, we developed a generalized probabilistic topic model to analyze a toxicogenomics dataset that consists of a large number of gene expression data from the rat livers treated with drugs in multiple dose and time-points. We discovered the hidden patterns in gene expression associated with the effect of doses and time-points of treatment. Finally, we illustrated the ability of our model to identify the evidence of potential reduction of animal use.

Model reduction of multiscale chemical langevin equations: a numerical case study.

PubMed

Sotiropoulos, Vassilios; Contou-Carrere, Marie-Nathalie; Daoutidis, Prodromos; Kaznessis, Yiannis N

2009-01-01

Two very important characteristics of biological reaction networks need to be considered carefully when modeling these systems. First, models must account for the inherent probabilistic nature of systems far from the thermodynamic limit. Often, biological systems cannot be modeled with traditional continuous-deterministic models. Second, models must take into consideration the disparate spectrum of time scales observed in biological phenomena, such as slow transcription events and fast dimerization reactions. In the last decade, significant efforts have been expended on the development of stochastic chemical kinetics models to capture the dynamics of biomolecular systems, and on the development of robust multiscale algorithms, able to handle stiffness. In this paper, the focus is on the dynamics of reaction sets governed by stiff chemical Langevin equations, i.e., stiff stochastic differential equations. These are particularly challenging systems to model, requiring prohibitively small integration step sizes. We describe and illustrate the application of a semianalytical reduction framework for chemical Langevin equations that results in significant gains in computational cost.

A Proposed Probabilistic Extension of the Halpern and Pearl Definition of ‘Actual Cause’

PubMed Central

2017-01-01

ABSTRACT Joseph Halpern and Judea Pearl ([2005]) draw upon structural equation models to develop an attractive analysis of ‘actual cause’. Their analysis is designed for the case of deterministic causation. I show that their account can be naturally extended to provide an elegant treatment of probabilistic causation. 1Introduction2Preemption3Structural Equation Models4The Halpern and Pearl Definition of ‘Actual Cause’5Preemption Again6The Probabilistic Case7Probabilistic Causal Models8A Proposed Probabilistic Extension of Halpern and Pearl’s Definition9Twardy and Korb’s Account10Probabilistic Fizzling11Conclusion PMID:29593362

CalTOX (registered trademark), A multimedia total exposure model spreadsheet user's guide. Version 4.0(Beta)

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKone, T.E.; Enoch, K.G.

2002-08-01

CalTOX has been developed as a set of spreadsheet models and spreadsheet data sets to assist in assessing human exposures from continuous releases to multiple environmental media, i.e. air, soil, and water. It has also been used for waste classification and for setting soil clean-up levels at uncontrolled hazardous wastes sites. The modeling components of CalTOX include a multimedia transport and transformation model, multi-pathway exposure scenario models, and add-ins to quantify and evaluate uncertainty and variability. All parameter values used as inputs to CalTOX are distributions, described in terms of mean values and a coefficient of variation, rather than asmore » point estimates or plausible upper values such as most other models employ. This probabilistic approach allows both sensitivity and uncertainty analyses to be directly incorporated into the model operation. This manual provides CalTOX users with a brief overview of the CalTOX spreadsheet model and provides instructions for using the spreadsheet to make deterministic and probabilistic calculations of source-dose-risk relationships.« less

A probabilistic verification score for contours demonstrated with idealized ice-edge forecasts

NASA Astrophysics Data System (ADS)

Goessling, Helge; Jung, Thomas

2017-04-01

We introduce a probabilistic verification score for ensemble-based forecasts of contours: the Spatial Probability Score (SPS). Defined as the spatial integral of local (Half) Brier Scores, the SPS can be considered the spatial analog of the Continuous Ranked Probability Score (CRPS). Applying the SPS to idealized seasonal ensemble forecasts of the Arctic sea-ice edge in a global coupled climate model, we demonstrate that the SPS responds properly to ensemble size, bias, and spread. When applied to individual forecasts or ensemble means (or quantiles), the SPS is reduced to the 'volume' of mismatch, in case of the ice edge corresponding to the Integrated Ice Edge Error (IIEE).

Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net

NASA Technical Reports Server (NTRS)

Lerner, Uri; Moses, Brooks; Scott, Maricia; McIlraith, Sheila; Keller, Daphne

2005-01-01

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

MrLavaLoba: A new probabilistic model for the simulation of lava flows as a settling process

NASA Astrophysics Data System (ADS)

de'Michieli Vitturi, Mattia; Tarquini, Simone

2018-01-01

A new code to simulate lava flow spread, MrLavaLoba, is presented. In the code, erupted lava is itemized in parcels having an elliptical shape and prescribed volume. New parcels bud from existing ones according to a probabilistic law influenced by the local steepest slope direction and by tunable input settings. MrLavaLoba must be accounted among the probabilistic codes for the simulation of lava flows, because it is not intended to mimic the actual process of flowing or to provide directly the progression with time of the flow field, but rather to guess the most probable inundated area and final thickness of the lava deposit. The code's flexibility allows it to produce variable lava flow spread and emplacement according to different dynamics (e.g. pahoehoe or channelized-'a'ā). For a given scenario, it is shown that model outputs converge, in probabilistic terms, towards a single solution. The code is applied to real cases in Hawaii and Mt. Etna, and the obtained maps are shown. The model is written in Python and the source code is available at http://demichie.github.io/MrLavaLoba/.

Bayesian Probabilistic Projections of Life Expectancy for All Countries

PubMed Central

Raftery, Adrian E.; Chunn, Jennifer L.; Gerland, Patrick; Ševčíková, Hana

2014-01-01

We propose a Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world from the present to 2100. Such forecasts would be an input to the production of probabilistic population projections for all countries, which is currently being considered by the United Nations. To evaluate the method, we did an out-of-sample cross-validation experiment, fitting the model to the data from 1950–1995, and using the estimated model to forecast for the subsequent ten years. The ten-year predictions had a mean absolute error of about 1 year, about 40% less than the current UN methodology. The probabilistic forecasts were calibrated, in the sense that (for example) the 80% prediction intervals contained the truth about 80% of the time. We illustrate our method with results from Madagascar (a typical country with steadily improving life expectancy), Latvia (a country that has had a mortality crisis), and Japan (a leading country). We also show aggregated results for South Asia, a region with eight countries. Free publicly available R software packages called bayesLife and bayesDem are available to implement the method. PMID:23494599

Tracing information flow on a global scale using Internet chain-letter data

PubMed Central

Liben-Nowell, David; Kleinberg, Jon

2008-01-01

Although information, news, and opinions continuously circulate in the worldwide social network, the actual mechanics of how any single piece of information spreads on a global scale have been a mystery. Here, we trace such information-spreading processes at a person-by-person level using methods to reconstruct the propagation of massively circulated Internet chain letters. We find that rather than fanning out widely, reaching many people in very few steps according to “small-world” principles, the progress of these chain letters proceeds in a narrow but very deep tree-like pattern, continuing for several hundred steps. This suggests a new and more complex picture for the spread of information through a social network. We describe a probabilistic model based on network clustering and asynchronous response times that produces trees with this characteristic structure on social-network data. PMID:18353985

Quantitative Risk Modeling of Fire on the International Space Station

NASA Technical Reports Server (NTRS)

Castillo, Theresa; Haught, Megan

2014-01-01

The International Space Station (ISS) Program has worked to prevent fire events and to mitigate their impacts should they occur. Hardware is designed to reduce sources of ignition, oxygen systems are designed to control leaking, flammable materials are prevented from flying to ISS whenever possible, the crew is trained in fire response, and fire response equipment improvements are sought out and funded. Fire prevention and mitigation are a top ISS Program priority - however, programmatic resources are limited; thus, risk trades are made to ensure an adequate level of safety is maintained onboard the ISS. In support of these risk trades, the ISS Probabilistic Risk Assessment (PRA) team has modeled the likelihood of fire occurring in the ISS pressurized cabin, a phenomenological event that has never before been probabilistically modeled in a microgravity environment. This paper will discuss the genesis of the ISS PRA fire model, its enhancement in collaboration with fire experts, and the results which have informed ISS programmatic decisions and will continue to be used throughout the life of the program.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Evaluation of properties over phylogenetic trees using stochastic logics.

PubMed

Requeno, José Ignacio; Colom, José Manuel

2016-06-14

Model checking has been recently introduced as an integrated framework for extracting information of the phylogenetic trees using temporal logics as a querying language, an extension of modal logics that imposes restrictions of a boolean formula along a path of events. The phylogenetic tree is considered a transition system modeling the evolution as a sequence of genomic mutations (we understand mutation as different ways that DNA can be changed), while this kind of logics are suitable for traversing it in a strict and exhaustive way. Given a biological property that we desire to inspect over the phylogeny, the verifier returns true if the specification is satisfied or a counterexample that falsifies it. However, this approach has been only considered over qualitative aspects of the phylogeny. In this paper, we repair the limitations of the previous framework for including and handling quantitative information such as explicit time or probability. To this end, we apply current probabilistic continuous-time extensions of model checking to phylogenetics. We reinterpret a catalog of qualitative properties in a numerical way, and we also present new properties that couldn't be analyzed before. For instance, we obtain the likelihood of a tree topology according to a mutation model. As case of study, we analyze several phylogenies in order to obtain the maximum likelihood with the model checking tool PRISM. In addition, we have adapted the software for optimizing the computation of maximum likelihoods. We have shown that probabilistic model checking is a competitive framework for describing and analyzing quantitative properties over phylogenetic trees. This formalism adds soundness and readability to the definition of models and specifications. Besides, the existence of model checking tools hides the underlying technology, omitting the extension, upgrade, debugging and maintenance of a software tool to the biologists. A set of benchmarks justify the feasibility of our approach.

Integrating Ecosystem Carbon Dynamics into State-and-Transition Simulation Models of Land Use/Land Cover Change

NASA Astrophysics Data System (ADS)

Sleeter, B. M.; Daniel, C.; Frid, L.; Fortin, M. J.

2016-12-01

State-and-transition simulation models (STSMs) provide a general approach for incorporating uncertainty into forecasts of landscape change. Using a Monte Carlo approach, STSMs generate spatially-explicit projections of the state of a landscape based upon probabilistic transitions defined between states. While STSMs are based on the basic principles of Markov chains, they have additional properties that make them applicable to a wide range of questions and types of landscapes. A current limitation of STSMs is that they are only able to track the fate of discrete state variables, such as land use/land cover (LULC) classes. There are some landscape modelling questions, however, for which continuous state variables - for example carbon biomass - are also required. Here we present a new approach for integrating continuous state variables into spatially-explicit STSMs. Specifically we allow any number of continuous state variables to be defined for each spatial cell in our simulations; the value of each continuous variable is then simulated forward in discrete time as a stochastic process based upon defined rates of change between variables. These rates can be defined as a function of the realized states and transitions of each cell in the STSM, thus providing a connection between the continuous variables and the dynamics of the landscape. We demonstrate this new approach by (1) developing a simple IPCC Tier 3 compliant model of ecosystem carbon biomass, where the continuous state variables are defined as terrestrial carbon biomass pools and the rates of change as carbon fluxes between pools, and (2) integrating this carbon model with an existing LULC change model for the state of Hawaii, USA.

Probability or Reasoning: Current Thinking and Realistic Strategies for Improved Medical Decisions

PubMed Central

2017-01-01

A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy. PMID:29209469

Probability or Reasoning: Current Thinking and Realistic Strategies for Improved Medical Decisions.

PubMed

Nantha, Yogarabindranath Swarna

2017-11-01

A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy.

Discrete-Slots Models of Visual Working-Memory Response Times

PubMed Central

Donkin, Christopher; Nosofsky, Robert M.; Gold, Jason M.; Shiffrin, Richard M.

2014-01-01

Much recent research has aimed to establish whether visual working memory (WM) is better characterized by a limited number of discrete all-or-none slots or by a continuous sharing of memory resources. To date, however, researchers have not considered the response-time (RT) predictions of discrete-slots versus shared-resources models. To complement the past research in this field, we formalize a family of mixed-state, discrete-slots models for explaining choice and RTs in tasks of visual WM change detection. In the tasks under investigation, a small set of visual items is presented, followed by a test item in 1 of the studied positions for which a change judgment must be made. According to the models, if the studied item in that position is retained in 1 of the discrete slots, then a memory-based evidence-accumulation process determines the choice and the RT; if the studied item in that position is missing, then a guessing-based accumulation process operates. Observed RT distributions are therefore theorized to arise as probabilistic mixtures of the memory-based and guessing distributions. We formalize an analogous set of continuous shared-resources models. The model classes are tested on individual subjects with both qualitative contrasts and quantitative fits to RT-distribution data. The discrete-slots models provide much better qualitative and quantitative accounts of the RT and choice data than do the shared-resources models, although there is some evidence for “slots plus resources” when memory set size is very small. PMID:24015956

A time-dependent probabilistic seismic-hazard model for California

USGS Publications Warehouse

Cramer, C.H.; Petersen, M.D.; Cao, T.; Toppozada, Tousson R.; Reichle, M.

2000-01-01

For the purpose of sensitivity testing and illuminating nonconsensus components of time-dependent models, the California Department of Conservation, Division of Mines and Geology (CDMG) has assembled a time-dependent version of its statewide probabilistic seismic hazard (PSH) model for California. The model incorporates available consensus information from within the earth-science community, except for a few faults or fault segments where consensus information is not available. For these latter faults, published information has been incorporated into the model. As in the 1996 CDMG/U.S. Geological Survey (USGS) model, the time-dependent models incorporate three multisegment ruptures: a 1906, an 1857, and a southern San Andreas earthquake. Sensitivity tests are presented to show the effect on hazard and expected damage estimates of (1) intrinsic (aleatory) sigma, (2) multisegment (cascade) vs. independent segment (no cascade) ruptures, and (3) time-dependence vs. time-independence. Results indicate that (1) differences in hazard and expected damage estimates between time-dependent and independent models increase with decreasing intrinsic sigma, (2) differences in hazard and expected damage estimates between full cascading and not cascading are insensitive to intrinsic sigma, (3) differences in hazard increase with increasing return period (decreasing probability of occurrence), and (4) differences in moment-rate budgets increase with decreasing intrinsic sigma and with the degree of cascading, but are within the expected uncertainty in PSH time-dependent modeling and do not always significantly affect hazard and expected damage estimates.

Feasibility study on the use of probabilistic migration modeling in support of exposure assessment from food contact materials.

PubMed

Poças, Maria F; Oliveira, Jorge C; Brandsch, Rainer; Hogg, Timothy

2010-07-01

The use of probabilistic approaches in exposure assessments of contaminants migrating from food packages is of increasing interest but the lack of concentration or migration data is often referred as a limitation. Data accounting for the variability and uncertainty that can be expected in migration, for example, due to heterogeneity in the packaging system, variation of the temperature along the distribution chain, and different time of consumption of each individual package, are required for probabilistic analysis. The objective of this work was to characterize quantitatively the uncertainty and variability in estimates of migration. A Monte Carlo simulation was applied to a typical solution of the Fick's law with given variability in the input parameters. The analysis was performed based on experimental data of a model system (migration of Irgafos 168 from polyethylene into isooctane) and illustrates how important sources of variability and uncertainty can be identified in order to refine analyses. For long migration times and controlled conditions of temperature the affinity of the migrant to the food can be the major factor determining the variability in the migration values (more than 70% of variance). In situations where both the time of consumption and temperature can vary, these factors can be responsible, respectively, for more than 60% and 20% of the variance in the migration estimates. The approach presented can be used with databases from consumption surveys to yield a true probabilistic estimate of exposure.

META 2f: Probabilistic, Compositional, Multi-dimension Model-Based Verification (PROMISE)

DTIC Science & Technology

2011-10-01

Equational Logic, Rewriting Logic, and Maude ................................................ 52  5.3  Results and Discussion...and its discrete transitions are left unchanged. However, the differential equations describing the continuous dynamics (in each mode) are replaced by...by replacing hard-to-analyze differential equations by discrete transitions. In principle, predicate and qualitative abstraction can be used on a

A Response-Time Approach to Comparing Generalized Rational and Take-the-Best Models of Decision Making

ERIC Educational Resources Information Center

Bergert, F. Bryan; Nosofsky, Robert M.

2007-01-01

The authors develop and test generalized versions of take-the-best (TTB) and rational (RAT) models of multiattribute paired-comparison inference. The generalized models make allowances for subjective attribute weighting, probabilistic orders of attribute inspection, and noisy decision making. A key new test involves a response-time (RT)…

Probabilistic Modeling of the Renal Stone Formation Module

NASA Technical Reports Server (NTRS)

Best, Lauren M.; Myers, Jerry G.; Goodenow, Debra A.; McRae, Michael P.; Jackson, Travis C.

2013-01-01

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

A Software Tool for Quantitative Seismicity Analysis - ZMAP

NASA Astrophysics Data System (ADS)

Wiemer, S.; Gerstenberger, M.

2001-12-01

Earthquake catalogs are probably the most basic product of seismology, and remain arguably the most useful for tectonic studies. Modern seismograph networks can locate up to 100,000 earthquakes annually, providing a continuous and sometime overwhelming stream of data. ZMAP is a set of tools driven by a graphical user interface (GUI), designed to help seismologists analyze catalog data. ZMAP is primarily a research tool suited to the evaluation of catalog quality and to addressing specific hypotheses; however, it can also be useful in routine network operations. Examples of ZMAP features include catalog quality assessment (artifacts, completeness, explosion contamination), interactive data exploration, mapping transients in seismicity (rate changes, b-values, p-values), fractal dimension analysis and stress tensor inversions. Roughly 100 scientists worldwide have used the software at least occasionally. About 30 peer-reviewed publications have made use of ZMAP. ZMAP code is open source, written in the commercial software language Matlab by the Mathworks, a widely used software in the natural sciences. ZMAP was first published in 1994, and has continued to grow over the past 7 years. Recently, we released ZMAP v.6. The poster will introduce the features of ZMAP. We will specifically focus on ZMAP features related to time-dependent probabilistic hazard assessment. We are currently implementing a ZMAP based system that computes probabilistic hazard maps, which combine the stationary background hazard as well as aftershock and foreshock hazard into a comprehensive time dependent probabilistic hazard map. These maps will be displayed in near real time on the Internet. This poster is also intended as a forum for ZMAP users to provide feedback and discuss the future of ZMAP.

Developing an Event-Tree Probabilistic Tsunami Inundation Model for NE Atlantic Coasts: Application to a Case Study

NASA Astrophysics Data System (ADS)

Omira, R.; Matias, L.; Baptista, M. A.

2016-12-01

This study constitutes a preliminary assessment of probabilistic tsunami inundation in the NE Atlantic region. We developed an event-tree approach to calculate the likelihood of tsunami flood occurrence and exceedance of a specific near-shore wave height for a given exposure time. Only tsunamis of tectonic origin are considered here, taking into account local, regional, and far-field sources. The approach used here consists of an event-tree method that gathers probability models for seismic sources, tsunami numerical modeling, and statistical methods. It also includes a treatment of aleatoric uncertainties related to source location and tidal stage. Epistemic uncertainties are not addressed in this study. The methodology is applied to the coastal test-site of Sines located in the NE Atlantic coast of Portugal. We derive probabilistic high-resolution maximum wave amplitudes and flood distributions for the study test-site considering 100- and 500-year exposure times. We find that the probability that maximum wave amplitude exceeds 1 m somewhere along the Sines coasts reaches about 60 % for an exposure time of 100 years and is up to 97 % for an exposure time of 500 years. The probability of inundation occurrence (flow depth >0 m) varies between 10 % and 57 %, and from 20 % up to 95 % for 100- and 500-year exposure times, respectively. No validation has been performed here with historical tsunamis. This paper illustrates a methodology through a case study, which is not an operational assessment.

Probabilistic Risk Model for Organ Doses and Acute Health Effects of Astronauts on Lunar Missions

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Hu, Shaowen; Nounu, Hatem N.; Cucinotta, Francis A.

2009-01-01

Exposure to large solar particle events (SPEs) is a major concern during EVAs on the lunar surface and in Earth-to-Lunar transit. 15% of crew times may be on EVA with minimal radiation shielding. Therefore, an accurate assessment of SPE occurrence probability is required for the mission planning by NASA. We apply probabilistic risk assessment (PRA) for radiation protection of crews and optimization of lunar mission planning.

Evaluation of feature-based 3-d registration of probabilistic volumetric scenes

NASA Astrophysics Data System (ADS)

Restrepo, Maria I.; Ulusoy, Ali O.; Mundy, Joseph L.

2014-12-01

Automatic estimation of the world surfaces from aerial images has seen much attention and progress in recent years. Among current modeling technologies, probabilistic volumetric models (PVMs) have evolved as an alternative representation that can learn geometry and appearance in a dense and probabilistic manner. Recent progress, in terms of storage and speed, achieved in the area of volumetric modeling, opens the opportunity to develop new frameworks that make use of the PVM to pursue the ultimate goal of creating an entire map of the earth, where one can reason about the semantics and dynamics of the 3-d world. Aligning 3-d models collected at different time-instances constitutes an important step for successful fusion of large spatio-temporal information. This paper evaluates how effectively probabilistic volumetric models can be aligned using robust feature-matching techniques, while considering different scenarios that reflect the kind of variability observed across aerial video collections from different time instances. More precisely, this work investigates variability in terms of discretization, resolution and sampling density, errors in the camera orientation, and changes in illumination and geographic characteristics. All results are given for large-scale, outdoor sites. In order to facilitate the comparison of the registration performance of PVMs to that of other 3-d reconstruction techniques, the registration pipeline is also carried out using Patch-based Multi-View Stereo (PMVS) algorithm. Registration performance is similar for scenes that have favorable geometry and the appearance characteristics necessary for high quality reconstruction. In scenes containing trees, such as a park, or many buildings, such as a city center, registration performance is significantly more accurate when using the PVM.

Bayesian population decoding of spiking neurons.

PubMed

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

PubMed Central

Truccolo, Wilson

2017-01-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

The $10 trillion value of better information about the transient climate response.

PubMed

Hope, Chris

2015-11-13

How much is better information about climate change worth? Here, I use PAGE09, a probabilistic integrated assessment model, to find the optimal paths of CO(2) emissions over time and to calculate the value of better information about one aspect of climate change, the transient climate response (TCR). Approximately halving the uncertainty range for TCR has a net present value of about $10.3 trillion (year 2005 US$) if accomplished in time for emissions to be adjusted in 2020, falling to $9.7 trillion if accomplished by 2030. Probabilistic integrated assessment modelling is the only method we have for making estimates like these for the value of better information about the science and impacts of climate change. © 2015 The Author(s).

Minimum time search in uncertain dynamic domains with complex sensorial platforms.

PubMed

Lanillos, Pablo; Besada-Portas, Eva; Lopez-Orozco, Jose Antonio; de la Cruz, Jesus Manuel

2014-08-04

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models.

Minimum Time Search in Uncertain Dynamic Domains with Complex Sensorial Platforms

PubMed Central

Lanillos, Pablo; Besada-Portas, Eva; Lopez-Orozco, Jose Antonio; de la Cruz, Jesus Manuel

2014-01-01

The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models. PMID:25093345

Effects of shipping on marine acoustic habitats in Canadian Arctic estimated via probabilistic modeling and mapping.

PubMed

Aulanier, Florian; Simard, Yvan; Roy, Nathalie; Gervaise, Cédric; Bandet, Marion

2017-12-15

Canadian Arctic and Subarctic regions experience a rapid decrease of sea ice accompanied with increasing shipping traffic. The resulting time-space changes in shipping noise are studied for four key regions of this pristine environment, for 2013 traffic conditions and a hypothetical tenfold traffic increase. A probabilistic modeling and mapping framework, called Ramdam, which integrates the intrinsic variability and uncertainties of shipping noise and its effects on marine habitats, is developed and applied. A substantial transformation of soundscapes is observed in areas where shipping noise changes from present occasional-transient contributor to a dominant noise source. Examination of impacts on low-frequency mammals within ecologically and biologically significant areas reveals that shipping noise has the potential to trigger behavioral responses and masking in the future, although no risk of temporary or permanent hearing threshold shifts is noted. Such probabilistic modeling and mapping is strategic in marine spatial planning of this emerging noise issues. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

On the probabilistic structure of water age: Probabilistic Water Age

DOE Office of Scientific and Technical Information (OSTI.GOV)

Porporato, Amilcare; Calabrese, Salvatore

We report the age distribution of water in hydrologic systems has received renewed interest recently, especially in relation to watershed response to rainfall inputs. The purpose of this contribution is first to draw attention to existing theories of age distributions in population dynamics, fluid mechanics and stochastic groundwater, and in particular to the McKendrick-von Foerster equation and its generalizations and solutions. A second and more important goal is to clarify that, when hydrologic fluxes are modeled by means of time-varying stochastic processes, the age distributions must themselves be treated as random functions. Once their probabilistic structure is obtained, it canmore » be used to characterize the variability of age distributions in real systems and thus help quantify the inherent uncertainty in the field determination of water age. Finally, we illustrate these concepts with reference to a stochastic storage model, which has been used as a minimalist model of soil moisture and streamflow dynamics.« less

On the probabilistic structure of water age: Probabilistic Water Age

DOE PAGES

Porporato, Amilcare; Calabrese, Salvatore

2015-04-23

We report the age distribution of water in hydrologic systems has received renewed interest recently, especially in relation to watershed response to rainfall inputs. The purpose of this contribution is first to draw attention to existing theories of age distributions in population dynamics, fluid mechanics and stochastic groundwater, and in particular to the McKendrick-von Foerster equation and its generalizations and solutions. A second and more important goal is to clarify that, when hydrologic fluxes are modeled by means of time-varying stochastic processes, the age distributions must themselves be treated as random functions. Once their probabilistic structure is obtained, it canmore » be used to characterize the variability of age distributions in real systems and thus help quantify the inherent uncertainty in the field determination of water age. Finally, we illustrate these concepts with reference to a stochastic storage model, which has been used as a minimalist model of soil moisture and streamflow dynamics.« less

Sustainable Odds: Towards Quantitative Decision Support when Relevant Probabilities are not Available

NASA Astrophysics Data System (ADS)

Smith, L. A.

2012-04-01

There is, at present, no attractive foundation for quantitative probabilistic decision support in the face of model inadequacy, or given ambiguity (deep uncertainty) regarding the relative likelihood of various outcomes, known or unknown. True model error arguably precludes the extraction of objective probabilities from an ensemble of model runs drawn from an available (inadequate) model class, while the acknowledgement of incomplete understanding precludes the justified use of (if not the very formation of) an individual's subjective probabilities. An alternative approach based on Sustainable Odds is proposed and investigated. Sustainable Odds differ from "fair odds" (and are easily distinguished any claim which implying well defined probabilities) as the probabilities implied by sustainable odds summed over all outcomes is expected to exceed one. Traditionally, a person's fair odds are found by identifying the probability level at which one would happily accept either side of a bet, thus the probabilities implied by fair odds always sum to one. Knowing that one has incomplete information and perhaps even erroneous beliefs, there is no compelling reason a rational agent should accept the constraint implied by "fair odds" in any bet. Rather, a rational agent might insist on longer odds both on the event and against the event in order to account for acknowledged ignorance. Let probabilistic odds imply any set of odds for which the implied probabilities sum to one; once model error is acknowledged can one rationally demand non-probabilistic odds? The danger of using fair odds (or probabilities) in decision making is illustrated by considering the risk of ruin a cooperative insurance scheme using probabilistic odds is exposed to. Cases where knowing merely that the insurer's model is imperfect, and nothing else, is sufficient to place bets which drive the insurer to an unexpectedly early ruin are presented. Methodologies which allow the insurer to avoid this early ruin are explored; those which prevent early ruin are said to provide "sustainable odds", and it is suggested that these must be non-probabilistic. The aim here is not for the insurance cooperative to make a profit in the long run (or to form a book in any one round) but rather to increase the chance that the cooperative will not go bust, merely breaking even in the long run and thereby continuing to provide a service. In the perfect model scenario, with complete knowledge of all uncertainties and unlimited computational resources, fair odds may prove to be sustainable. The implications these results hold in the case of games against nature, which is perhaps a more relevant context for decision makers concerned with geophysical systems, are discussed. The claim that acknowledged model error makes fair (probabilistic) odds an irrational aim is considered, as are the challenges of working within the framework of sustainable (but non-probabilistic) odds.

A Probabilistic Model of Meter Perception: Simulating Enculturation.

PubMed

van der Weij, Bastiaan; Pearce, Marcus T; Honing, Henkjan

2017-01-01

Enculturation is known to shape the perception of meter in music but this is not explicitly accounted for by current cognitive models of meter perception. We hypothesize that the induction of meter is a result of predictive coding: interpreting onsets in a rhythm relative to a periodic meter facilitates prediction of future onsets. Such prediction, we hypothesize, is based on previous exposure to rhythms. As such, predictive coding provides a possible explanation for the way meter perception is shaped by the cultural environment. Based on this hypothesis, we present a probabilistic model of meter perception that uses statistical properties of the relation between rhythm and meter to infer meter from quantized rhythms. We show that our model can successfully predict annotated time signatures from quantized rhythmic patterns derived from folk melodies. Furthermore, we show that by inferring meter, our model improves prediction of the onsets of future events compared to a similar probabilistic model that does not infer meter. Finally, as a proof of concept, we demonstrate how our model can be used in a simulation of enculturation. From the results of this simulation, we derive a class of rhythms that are likely to be interpreted differently by enculturated listeners with different histories of exposure to rhythms.

A Bimodal Hybrid Model for Time-Dependent Probabilistic Seismic Hazard Analysis

NASA Astrophysics Data System (ADS)

Yaghmaei-Sabegh, Saman; Shoaeifar, Nasser; Shoaeifar, Parva

2018-03-01

The evaluation of evidence provided by geological studies and historical catalogs indicates that in some seismic regions and faults, multiple large earthquakes occur in cluster. Then, the occurrences of large earthquakes confront with quiescence and only the small-to-moderate earthquakes take place. Clustering of large earthquakes is the most distinguishable departure from the assumption of constant hazard of random occurrence of earthquakes in conventional seismic hazard analysis. In the present study, a time-dependent recurrence model is proposed to consider a series of large earthquakes that occurs in clusters. The model is flexible enough to better reflect the quasi-periodic behavior of large earthquakes with long-term clustering, which can be used in time-dependent probabilistic seismic hazard analysis with engineering purposes. In this model, the time-dependent hazard results are estimated by a hazard function which comprises three parts. A decreasing hazard of last large earthquake cluster and an increasing hazard of the next large earthquake cluster, along with a constant hazard of random occurrence of small-to-moderate earthquakes. In the final part of the paper, the time-dependent seismic hazard of the New Madrid Seismic Zone at different time intervals has been calculated for illustrative purpose.

Prevention 0f Unwanted Free-Declaration of Static Obstacles in Probability Occupancy Grids

NASA Astrophysics Data System (ADS)

Krause, Stefan; Scholz, M.; Hohmann, R.

2017-10-01

Obstacle detection and avoidance are major research fields in unmanned aviation. Map based obstacle detection approaches often use discrete world representations such as probabilistic grid maps to fuse incremental environment data from different views or sensors to build a comprehensive representation. The integration of continuous measurements into a discrete representation can result in rounding errors which, in turn, leads to differences between the artificial model and real environment. The cause of these deviations is a low spatial resolution of the world representation comparison to the used sensor data. Differences between artificial representations which are used for path planning or obstacle avoidance and the real world can lead to unexpected behavior up to collisions with unmapped obstacles. This paper presents three approaches to the treatment of errors that can occur during the integration of continuous laser measurement in the discrete probabilistic grid. Further, the quality of the error prevention and the processing performance are compared with real sensor data.

Asteroid Risk Assessment: A Probabilistic Approach.

PubMed

Reinhardt, Jason C; Chen, Xi; Liu, Wenhao; Manchev, Petar; Paté-Cornell, M Elisabeth

2016-02-01

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy-making communities. It reminded the world that impacts from near-Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low-probability, high-consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability-but not the consequences-of an impact with global effects ("cataclysm"). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk-reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth. © 2015 Society for Risk Analysis.

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

PubMed Central

Campbell, Kieran R.

2016-01-01

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852

Development and application of a probabilistic method for wildfire suppression cost modeling

Treesearch

Matthew P. Thompson; Jessica R. Haas; Mark A. Finney; David E. Calkin; Michael S. Hand; Mark J. Browne; Martin Halek; Karen C. Short; Isaac C. Grenfell

2015-01-01

Wildfire activity and escalating suppression costs continue to threaten the financial health of federal land management agencies. In order to minimize and effectively manage the cost of financial risk, agencies need the ability to quantify that risk. A fundamental aim of this research effort, therefore, is to develop a process for generating risk-based metrics for...

Uncertainty Quantification of Evapotranspiration and Infiltration from Modeling and Historic Time Series at the Savannah River F-Area

NASA Astrophysics Data System (ADS)

Faybishenko, B.; Flach, G. P.

2012-12-01

The objectives of this presentation are: (a) to illustrate the application of Monte Carlo and fuzzy-probabilistic approaches for uncertainty quantification (UQ) in predictions of potential evapotranspiration (PET), actual evapotranspiration (ET), and infiltration (I), using uncertain hydrological or meteorological time series data, and (b) to compare the results of these calculations with those from field measurements at the U.S. Department of Energy Savannah River Site (SRS), near Aiken, South Carolina, USA. The UQ calculations include the evaluation of aleatory (parameter uncertainty) and epistemic (model) uncertainties. The effect of aleatory uncertainty is expressed by assigning the probability distributions of input parameters, using historical monthly averaged data from the meteorological station at the SRS. The combined effect of aleatory and epistemic uncertainties on the UQ of PET, ET, and Iis then expressed by aggregating the results of calculations from multiple models using a p-box and fuzzy numbers. The uncertainty in PETis calculated using the Bair-Robertson, Blaney-Criddle, Caprio, Hargreaves-Samani, Hamon, Jensen-Haise, Linacre, Makkink, Priestly-Taylor, Penman, Penman-Monteith, Thornthwaite, and Turc models. Then, ET is calculated from the modified Budyko model, followed by calculations of I from the water balance equation. We show that probabilistic and fuzzy-probabilistic calculations using multiple models generate the PET, ET, and Idistributions, which are well within the range of field measurements. We also show that a selection of a subset of models can be used to constrain the uncertainty quantification of PET, ET, and I.

Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

OConnor, A.; Kirtman, B. P.; Harrison, S.; Gorman, J.

2016-02-01

Current US Navy forecasting systems cannot easily incorporate extended-range forecasts that can improve mission readiness and effectiveness; ensure safety; and reduce cost, labor, and resource requirements. If Navy operational planners had systems that incorporated these forecasts, they could plan missions using more reliable and longer-term weather and climate predictions. Further, using multi-model forecast ensembles instead of single forecasts would produce higher predictive performance. Extended-range multi-model forecast ensembles, such as those available in the North American Multi-Model Ensemble (NMME), are ideal for system integration because of their high skill predictions; however, even higher skill predictions can be produced if forecast model ensembles are combined correctly. While many methods for weighting models exist, the best method in a given environment requires expert knowledge of the models and combination methods.We present an innovative approach that uses machine learning to combine extended-range predictions from multi-model forecast ensembles and generate a probabilistic forecast for any region of the globe up to 12 months in advance. Our machine-learning approach uses 30 years of hindcast predictions to learn patterns of forecast model successes and failures. Each model is assigned a weight for each environmental condition, 100 km2 region, and day given any expected environmental information. These weights are then applied to the respective predictions for the region and time of interest to effectively stitch together a single, coherent probabilistic forecast. Our experimental results demonstrate the benefits of our approach to produce extended-range probabilistic forecasts for regions and time periods of interest that are superior, in terms of skill, to individual NMME forecast models and commonly weighted models. The probabilistic forecast leverages the strengths of three NMME forecast models to predict environmental conditions for an area spanning from San Diego, CA to Honolulu, HI, seven months in-advance. Key findings include: weighted combinations of models are strictly better than individual models; machine-learned combinations are especially better; and forecasts produced using our approach have the highest rank probability skill score most often.

Astrobiological complexity with probabilistic cellular automata.

PubMed

Vukotić, Branislav; Ćirković, Milan M

2012-08-01

The search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling the astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous space of the input parameters. We perform a simple clustering analysis of typical astrobiological histories with "Copernican" choice of input parameters and discuss the relevant boundary conditions of practical importance for planning and guiding empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.

A Heuristic Probabilistic Approach to Estimating Size-Dependent Mobility of Nonuniform Sediment

NASA Astrophysics Data System (ADS)

Woldegiorgis, B. T.; Wu, F. C.; van Griensven, A.; Bauwens, W.

2017-12-01

Simulating the mechanism of bed sediment mobility is essential for modelling sediment dynamics. Despite the fact that many studies are carried out on this subject, they use complex mathematical formulations that are computationally expensive, and are often not easy for implementation. In order to present a simple and computationally efficient complement to detailed sediment mobility models, we developed a heuristic probabilistic approach to estimating the size-dependent mobilities of nonuniform sediment based on the pre- and post-entrainment particle size distributions (PSDs), assuming that the PSDs are lognormally distributed. The approach fits a lognormal probability density function (PDF) to the pre-entrainment PSD of bed sediment and uses the threshold particle size of incipient motion and the concept of sediment mixture to estimate the PSDs of the entrained sediment and post-entrainment bed sediment. The new approach is simple in physical sense and significantly reduces the complexity and computation time and resource required by detailed sediment mobility models. It is calibrated and validated with laboratory and field data by comparing to the size-dependent mobilities predicted with the existing empirical lognormal cumulative distribution function (CDF) approach. The novel features of the current approach are: (1) separating the entrained and non-entrained sediments by a threshold particle size, which is a modified critical particle size of incipient motion by accounting for the mixed-size effects, and (2) using the mixture-based pre- and post-entrainment PSDs to provide a continuous estimate of the size-dependent sediment mobility.

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

ERIC Educational Resources Information Center

Chen, Xin

2012-01-01

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madankan, R.; Pouget, S.; Singla, P., E-mail: psingla@buffalo.edu

Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions – height, profile of particle location, volcanic vent parameters – are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This papermore » presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain source parameters, based on observed satellite imagery. The April 2010 eruption of the Eyjafjallajökull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14–16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.« less

The Use of Probabilistic Methods to Evaluate the Systems Impact of Component Design Improvements on Large Turbofan Engines

NASA Technical Reports Server (NTRS)

Packard, Michael H.

2002-01-01

Probabilistic Structural Analysis (PSA) is now commonly used for predicting the distribution of time/cycles to failure of turbine blades and other engine components. These distributions are typically based on fatigue/fracture and creep failure modes of these components. Additionally, reliability analysis is used for taking test data related to particular failure modes and calculating failure rate distributions of electronic and electromechanical components. How can these individual failure time distributions of structural, electronic and electromechanical component failure modes be effectively combined into a top level model for overall system evaluation of component upgrades, changes in maintenance intervals, or line replaceable unit (LRU) redesign? This paper shows an example of how various probabilistic failure predictions for turbine engine components can be evaluated and combined to show their effect on overall engine performance. A generic model of a turbofan engine was modeled using various Probabilistic Risk Assessment (PRA) tools (Quantitative Risk Assessment Software (QRAS) etc.). Hypothetical PSA results for a number of structural components along with mitigation factors that would restrict the failure mode from propagating to a Loss of Mission (LOM) failure were used in the models. The output of this program includes an overall failure distribution for LOM of the system. The rank and contribution to the overall Mission Success (MS) is also given for each failure mode and each subsystem. This application methodology demonstrates the effectiveness of PRA for assessing the performance of large turbine engines. Additionally, the effects of system changes and upgrades, the application of different maintenance intervals, inclusion of new sensor detection of faults and other upgrades were evaluated in determining overall turbine engine reliability.

An operational hydrological ensemble prediction system for the city of Zurich (Switzerland): assessing the added value of probabilistic forecasts

NASA Astrophysics Data System (ADS)

Addor, N.; Jaun, S.; Fundel, F.; Zappa, M.

2012-04-01

The Sihl River flows through Zurich, Switzerland's most populated city, for which it represents the largest flood threat. To anticipate extreme discharge events and provide decision support in case of flood risk, a hydrometeorological ensemble prediction system (HEPS) was launched operationally in 2008. This model chain relies on deterministic (COSMO-7) and probabilistic (COSMO-LEPS) atmospheric forecasts, which are used to force a semi-distributed hydrological model (PREVAH) coupled to a hydraulic model (FLORIS). The resulting hydrological forecasts are eventually communicated to the stakeholders involved in the Sihl discharge management. This fully operational setting provides a real framework with which we assessed the potential of deterministic and probabilistic discharge forecasts for flood mitigation. To study the suitability of HEPS for small-scale basins and to quantify the added value conveyed by the probability information, a 31-month reforecast was produced for the Sihl catchment (336 km2). Several metrics support the conclusion that the performance gain is of up to 2 days lead time for the catchment considered. Brier skill scores show that probabilistic hydrological forecasts outperform their deterministic counterparts for all the lead times and event intensities considered. The small size of the Sihl catchment does not prevent skillful discharge forecasts, but makes them particularly dependent on correct precipitation forecasts. Our evaluation stresses that the capacity of the model to provide confident and reliable mid-term probability forecasts for high discharges is limited. We finally highlight challenges for making decisions on the basis of hydrological predictions, and discuss the need for a tool to be used in addition to forecasts to compare the different mitigation actions possible in the Sihl catchment.

Unifying Model-Based and Reactive Programming within a Model-Based Executive

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Gupta, Vineet; Norvig, Peter (Technical Monitor)

1999-01-01

Real-time, model-based, deduction has recently emerged as a vital component in AI's tool box for developing highly autonomous reactive systems. Yet one of the current hurdles towards developing model-based reactive systems is the number of methods simultaneously employed, and their corresponding melange of programming and modeling languages. This paper offers an important step towards unification. We introduce RMPL, a rich modeling language that combines probabilistic, constraint-based modeling with reactive programming constructs, while offering a simple semantics in terms of hidden state Markov processes. We introduce probabilistic, hierarchical constraint automata (PHCA), which allow Markov processes to be expressed in a compact representation that preserves the modularity of RMPL programs. Finally, a model-based executive, called Reactive Burton is described that exploits this compact encoding to perform efficIent simulation, belief state update and control sequence generation.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Efficient Probabilistic Diagnostics for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Probabilistic durability assessment of concrete structures in marine environments: Reliability and sensitivity analysis

NASA Astrophysics Data System (ADS)

Yu, Bo; Ning, Chao-lie; Li, Bing

2017-03-01

A probabilistic framework for durability assessment of concrete structures in marine environments was proposed in terms of reliability and sensitivity analysis, which takes into account the uncertainties under the environmental, material, structural and executional conditions. A time-dependent probabilistic model of chloride ingress was established first to consider the variations in various governing parameters, such as the chloride concentration, chloride diffusion coefficient, and age factor. Then the Nataf transformation was adopted to transform the non-normal random variables from the original physical space into the independent standard Normal space. After that the durability limit state function and its gradient vector with respect to the original physical parameters were derived analytically, based on which the first-order reliability method was adopted to analyze the time-dependent reliability and parametric sensitivity of concrete structures in marine environments. The accuracy of the proposed method was verified by comparing with the second-order reliability method and the Monte Carlo simulation. Finally, the influences of environmental conditions, material properties, structural parameters and execution conditions on the time-dependent reliability of concrete structures in marine environments were also investigated. The proposed probabilistic framework can be implemented in the decision-making algorithm for the maintenance and repair of deteriorating concrete structures in marine environments.

An Integrated Probabilistic-Fuzzy Assessment of Uncertainty Associated with Human Health Risk to MSW Landfill Leachate Contamination

NASA Astrophysics Data System (ADS)

Mishra, H.; Karmakar, S.; Kumar, R.

2016-12-01

Risk assessment will not remain simple when it involves multiple uncertain variables. Uncertainties in risk assessment majorly results from (1) the lack of knowledge of input variable (mostly random), and (2) data obtained from expert judgment or subjective interpretation of available information (non-random). An integrated probabilistic-fuzzy health risk approach has been proposed for simultaneous treatment of random and non-random uncertainties associated with input parameters of health risk model. The LandSim 2.5, a landfill simulator, has been used to simulate the Turbhe landfill (Navi Mumbai, India) activities for various time horizons. Further the LandSim simulated six heavy metals concentration in ground water have been used in the health risk model. The water intake, exposure duration, exposure frequency, bioavailability and average time are treated as fuzzy variables, while the heavy metals concentration and body weight are considered as probabilistic variables. Identical alpha-cut and reliability level are considered for fuzzy and probabilistic variables respectively and further, uncertainty in non-carcinogenic human health risk is estimated using ten thousand Monte-Carlo simulations (MCS). This is the first effort in which all the health risk variables have been considered as non-deterministic for the estimation of uncertainty in risk output. The non-exceedance probability of Hazard Index (HI), summation of hazard quotients, of heavy metals of Co, Cu, Mn, Ni, Zn and Fe for male and female population have been quantified and found to be high (HI>1) for all the considered time horizon, which evidently shows possibility of adverse health effects on the population residing near Turbhe landfill.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442

Probabilistic safety analysis for urgent situations following the accidental release of a pollutant in the atmosphere

NASA Astrophysics Data System (ADS)

Armand, P.; Brocheton, F.; Poulet, D.; Vendel, F.; Dubourg, V.; Yalamas, T.

2014-10-01

This paper is an original contribution to uncertainty quantification in atmospheric transport & dispersion (AT&D) at the local scale (1-10 km). It is proposed to account for the imprecise knowledge of the meteorological and release conditions in the case of an accidental hazardous atmospheric emission. The aim is to produce probabilistic risk maps instead of a deterministic toxic load map in order to help the stakeholders making their decisions. Due to the urge attached to such situations, the proposed methodology is able to produce such maps in a limited amount of time. It resorts to a Lagrangian particle dispersion model (LPDM) using wind fields interpolated from a pre-established database that collects the results from a computational fluid dynamics (CFD) model. This enables a decoupling of the CFD simulations from the dispersion analysis, thus a considerable saving of computational time. In order to make the Monte-Carlo-sampling-based estimation of the probability field even faster, it is also proposed to recourse to the use of a vector Gaussian process surrogate model together with high performance computing (HPC) resources. The Gaussian process (GP) surrogate modelling technique is coupled with a probabilistic principal component analysis (PCA) for reducing the number of GP predictors to fit, store and predict. The design of experiments (DOE) from which the surrogate model is built, is run over a cluster of PCs for making the total production time as short as possible. The use of GP predictors is validated by comparing the results produced by this technique with those obtained by crude Monte Carlo sampling.

Interacting with an artificial partner: modeling the role of emotional aspects.

PubMed

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N Alberto

2008-12-01

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

DOE PAGES

Safta, C.; Ricciuto, Daniel M.; Sargsyan, Khachik; ...

2015-07-01

In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employedmore » in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.« less

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Probabilistic Finite Element Analysis & Design Optimization for Structural Designs

NASA Astrophysics Data System (ADS)

Deivanayagam, Arumugam

This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed.

Costing the satellite power system

NASA Technical Reports Server (NTRS)

Hazelrigg, G. A., Jr.

1978-01-01

The paper presents a methodology for satellite power system costing, places approximate limits on the accuracy possible in cost estimates made at this time, and outlines the use of probabilistic cost information in support of the decision-making process. Reasons for using probabilistic costing or risk analysis procedures instead of standard deterministic costing procedures are considered. Components of cost, costing estimating relationships, grass roots costing, and risk analysis are discussed. Risk analysis using a Monte Carlo simulation model is used to estimate future costs.

On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners.

PubMed

Chen, Kevin T; Izquierdo-Garcia, David; Poynton, Clare B; Chonde, Daniel B; Catana, Ciprian

2017-03-01

To propose an MR-based method for generating continuous-valued head attenuation maps and to assess its accuracy and reproducibility. Demonstrating that novel MR-based photon attenuation correction methods are both accurate and reproducible is essential prior to using them routinely in research and clinical studies on integrated PET/MR scanners. Continuous-valued linear attenuation coefficient maps ("μ-maps") were generated by combining atlases that provided the prior probability of voxel positions belonging to a certain tissue class (air, soft tissue, or bone) and an MR intensity-based likelihood classifier to produce posterior probability maps of tissue classes. These probabilities were used as weights to generate the μ-maps. The accuracy of this probabilistic atlas-based continuous-valued μ-map ("PAC-map") generation method was assessed by calculating the voxel-wise absolute relative change (RC) between the MR-based and scaled CT-based attenuation-corrected PET images. To assess reproducibility, we performed pair-wise comparisons of the RC values obtained from the PET images reconstructed using the μ-maps generated from the data acquired at three time points. The proposed method produced continuous-valued μ-maps that qualitatively reflected the variable anatomy in patients with brain tumor and agreed well with the scaled CT-based μ-maps. The absolute RC comparing the resulting PET volumes was 1.76 ± 2.33 %, quantitatively demonstrating that the method is accurate. Additionally, we also showed that the method is highly reproducible, the mean RC value for the PET images reconstructed using the μ-maps obtained at the three visits being 0.65 ± 0.95 %. Accurate and highly reproducible continuous-valued head μ-maps can be generated from MR data using a probabilistic atlas-based approach.

Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios

NASA Astrophysics Data System (ADS)

Gelfan, Alexander; Moreydo, Vsevolod; Motovilov, Yury; Solomatine, Dimitri P.

2018-04-01

A long-term forecasting ensemble methodology, applied to water inflows into the Cheboksary Reservoir (Russia), is presented. The methodology is based on a version of the semi-distributed hydrological model ECOMAG (ECOlogical Model for Applied Geophysics) that allows for the calculation of an ensemble of inflow hydrographs using two different sets of weather ensembles for the lead time period: observed weather data, constructed on the basis of the Ensemble Streamflow Prediction methodology (ESP-based forecast), and synthetic weather data, simulated by a multi-site weather generator (WG-based forecast). We have studied the following: (1) whether there is any advantage of the developed ensemble forecasts in comparison with the currently issued operational forecasts of water inflow into the Cheboksary Reservoir, and (2) whether there is any noticeable improvement in probabilistic forecasts when using the WG-simulated ensemble compared to the ESP-based ensemble. We have found that for a 35-year period beginning from the reservoir filling in 1982, both continuous and binary model-based ensemble forecasts (issued in the deterministic form) outperform the operational forecasts of the April-June inflow volume actually used and, additionally, provide acceptable forecasts of additional water regime characteristics besides the inflow volume. We have also demonstrated that the model performance measures (in the verification period) obtained from the WG-based probabilistic forecasts, which are based on a large number of possible weather scenarios, appeared to be more statistically reliable than the corresponding measures calculated from the ESP-based forecasts based on the observed weather scenarios.

Applying probabilistic well-performance parameters to assessments of shale-gas resources

USGS Publications Warehouse

Charpentier, Ronald R.; Cook, Troy

2010-01-01

In assessing continuous oil and gas resources, such as shale gas, it is important to describe not only the ultimately producible volumes, but also the expected well performance. This description is critical to any cost analysis or production scheduling. A probabilistic approach facilitates (1) the inclusion of variability in well performance within a continuous accumulation, and (2) the use of data from developed accumulations as analogs for the assessment of undeveloped accumulations. In assessing continuous oil and gas resources of the United States, the U.S. Geological Survey analyzed production data from many shale-gas accumulations. Analyses of four of these accumulations (the Barnett, Woodford, Fayetteville, and Haynesville shales) are presented here as examples of the variability of well performance. For example, the distribution of initial monthly production rates for Barnett vertical wells shows a noticeable change with time, first increasing because of improved completion practices, then decreasing from a combination of decreased reservoir pressure (in infill wells) and drilling in less productive areas. Within a partially developed accumulation, historical production data from that accumulation can be used to estimate production characteristics of undrilled areas. An understanding of the probabilistic relations between variables, such as between initial production and decline rates, can improve estimates of ultimate production. Time trends or spatial trends in production data can be clarified by plots and maps. The data can also be divided into subsets depending on well-drilling or well-completion techniques, such as vertical in relation to horizontal wells. For hypothetical or lightly developed accumulations, one can either make comparisons to a specific well-developed accumulation or to the entire range of available developed accumulations. Comparison of the distributions of initial monthly production rates of the four shale-gas accumulations that were studied shows substantial overlap. However, because of differences in decline rates among them, the resulting estimated ultimate recovery (EUR) distributions are considerably different.

The neural dynamics of song syntax in songbirds

NASA Astrophysics Data System (ADS)

Jin, Dezhe

2010-03-01

Songbird is ``the hydrogen atom'' of the neuroscience of complex, learned vocalizations such as human speech. Songs of Bengalese finch consist of sequences of syllables. While syllables are temporally stereotypical, syllable sequences can vary and follow complex, probabilistic syntactic rules, which are rudimentarily similar to grammars in human language. Songbird brain is accessible to experimental probes, and is understood well enough to construct biologically constrained, predictive computational models. In this talk, I will discuss the structure and dynamics of neural networks underlying the stereotypy of the birdsong syllables and the flexibility of syllable sequences. Recent experiments and computational models suggest that a syllable is encoded in a chain network of projection neurons in premotor nucleus HVC (proper name). Precisely timed spikes propagate along the chain, driving vocalization of the syllable through downstream nuclei. Through a computational model, I show that that variable syllable sequences can be generated through spike propagations in a network in HVC in which the syllable-encoding chain networks are connected into a branching chain pattern. The neurons mutually inhibit each other through the inhibitory HVC interneurons, and are driven by external inputs from nuclei upstream of HVC. At a branching point that connects the final group of a chain to the first groups of several chains, the spike activity selects one branch to continue the propagation. The selection is probabilistic, and is due to the winner-take-all mechanism mediated by the inhibition and noise. The model predicts that the syllable sequences statistically follow partially observable Markov models. Experimental results supporting this and other predictions of the model will be presented. We suggest that the syntax of birdsong syllable sequences is embedded in the connection patterns of HVC projection neurons.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

The pdf approach to turbulent polydispersed two-phase flows

NASA Astrophysics Data System (ADS)

Minier, Jean-Pierre; Peirano, Eric

2001-10-01

The purpose of this paper is to develop a probabilistic approach to turbulent polydispersed two-phase flows. The two-phase flows considered are composed of a continuous phase, which is a turbulent fluid, and a dispersed phase, which represents an ensemble of discrete particles (solid particles, droplets or bubbles). Gathering the difficulties of turbulent flows and of particle motion, the challenge is to work out a general modelling approach that meets three requirements: to treat accurately the physically relevant phenomena, to provide enough information to address issues of complex physics (combustion, polydispersed particle flows, …) and to remain tractable for general non-homogeneous flows. The present probabilistic approach models the statistical dynamics of the system and consists in simulating the joint probability density function (pdf) of a number of fluid and discrete particle properties. A new point is that both the fluid and the particles are included in the pdf description. The derivation of the joint pdf model for the fluid and for the discrete particles is worked out in several steps. The mathematical properties of stochastic processes are first recalled. The various hierarchies of pdf descriptions are detailed and the physical principles that are used in the construction of the models are explained. The Lagrangian one-particle probabilistic description is developed first for the fluid alone, then for the discrete particles and finally for the joint fluid and particle turbulent systems. In the case of the probabilistic description for the fluid alone or for the discrete particles alone, numerical computations are presented and discussed to illustrate how the method works in practice and the kind of information that can be extracted from it. Comments on the current modelling state and propositions for future investigations which try to link the present work with other ideas in physics are made at the end of the paper.

Scaling in the Donangelo-Sneppen model for evolution of money

NASA Astrophysics Data System (ADS)

Stauffer, Dietrich; P. Radomski, Jan

2001-03-01

The evolution of money from unsuccessful barter attempts, as modeled by Donangelo and Sneppen, is modified by a deterministic instead of a probabilistic selection of the most desired product as money. We check in particular the characteristic times of the model as a function of system size.

INTEGRATED PROBABILISTIC AND DETERMINISTIC MODELING TECHNIQUES IN ESTIMATING EXPOSURE TO WATER-BORNE CONTAMINANTS: PART 2 PHARMACOKINETIC MODELING

EPA Science Inventory

The Total Exposure Model (TEM) uses deterministic and stochastic methods to estimate the exposure of a person performing daily activities of eating, drinking, showering, and bathing. There were 250 time histories generated, by subject with activities, for the three exposure ro...

Determination of Economic Lot Size between Suppliers and Manufacturers for Imperfect Production System with Probabilistic Demand

NASA Astrophysics Data System (ADS)

Yuniar, S.; Wangsaputra, R.; Sinaga, A. T.

2018-03-01

This study aims to develop a combined economical lot size model between supplier and manufacturer for imperfect production processes with probabilistic demand patterns and constant lead times. The supplier side produces the product within a certain time interval then sent to the manufacturer with a certain amount of lot size. Imperfect supplier production systems are characterized by the probability of defective product (γ). The model decision variables are the lot size of the manufacturer's ordering, supplier lot size, and the reorder point of the manufacturer. The optimal decision variables are obtained by minimizing the total expected cost of the combined costs between the suppliers and the manufacturers borne by both parties. The model is built compared to the transactional partnership model, in which the supplier does not participate in the efficiency of its inventory system. A numerical example is given as an illustration of the JELS model and the transactional partnership model. Sensitivity analysis of the model is done by changing the parameters aimed at analyzing the behavior of the developed model.

Stochastic methods for analysis of power flow in electric networks

NASA Astrophysics Data System (ADS)

1982-09-01

The modeling and effects of probabilistic behavior on steady state power system operation were analyzed. A solution to the steady state network flow equations which adhere both to Kirchoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques was obtained. The development of sound techniques for producing meaningful data to serve as input is examined. Electric demand modeling, equipment failure analysis, and algorithm development are investigated. Two major development areas are described: a decomposition of stochastic processes which gives stationarity, ergodicity, and even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

Comparison of different incremental analysis update schemes in a realistic assimilation system with Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Yan, Y.; Barth, A.; Beckers, J. M.; Brankart, J. M.; Brasseur, P.; Candille, G.

2017-07-01

In this paper, three incremental analysis update schemes (IAU 0, IAU 50 and IAU 100) are compared in the same assimilation experiments with a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. The difference between the three IAU schemes lies on the position of the increment update window. The relevance of each IAU scheme is evaluated through analyses on both thermohaline and dynamical variables. The validation of the assimilation results is performed according to both deterministic and probabilistic metrics against different sources of observations. For deterministic validation, the ensemble mean and the ensemble spread are compared to the observations. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centred random variable (RCRV) score. The obtained results show that 1) the IAU 50 scheme has the same performance as the IAU 100 scheme 2) the IAU 50/100 schemes outperform the IAU 0 scheme in error covariance propagation for thermohaline variables in relatively stable region, while the IAU 0 scheme outperforms the IAU 50/100 schemes in dynamical variables estimation in dynamically active region 3) in case with sufficient number of observations and good error specification, the impact of IAU schemes is negligible. The differences between the IAU 0 scheme and the IAU 50/100 schemes are mainly due to different model integration time and different instability (density inversion, large vertical velocity, etc.) induced by the increment update. The longer model integration time with the IAU 50/100 schemes, especially the free model integration, on one hand, allows for better re-establishment of the equilibrium model state, on the other hand, smooths the strong gradients in dynamically active region.

Evaluation of Lithofacies Up-Scaling Methods for Probabilistic Prediction of Carbon Dioxide Behavior

NASA Astrophysics Data System (ADS)

Park, J. Y.; Lee, S.; Lee, Y. I.; Kihm, J. H.; Kim, J. M.

2017-12-01

Behavior of carbon dioxide injected into target reservoir (storage) formations is highly dependent on heterogeneities of geologic lithofacies and properties. These heterogeneous lithofacies and properties basically have probabilistic characteristics. Thus, their probabilistic evaluation has to be implemented properly into predicting behavior of injected carbon dioxide in heterogeneous storage formations. In this study, a series of three-dimensional geologic modeling is performed first using SKUA-GOCAD (ASGA and Paradigm) to establish lithofacies models of the Janggi Conglomerate in the Janggi Basin, Korea within a modeling domain. The Janggi Conglomerate is composed of mudstone, sandstone, and conglomerate, and it has been identified as a potential reservoir rock (clastic saline formation) for geologic carbon dioxide storage. Its lithofacies information are obtained from four boreholes and used in lithofacies modeling. Three different up-scaling methods (i.e., nearest to cell center, largest proportion, and random) are applied, and lithofacies modeling is performed 100 times for each up-scaling method. The lithofacies models are then compared and analyzed with the borehole data to evaluate the relative suitability of the three up-scaling methods. Finally, the lithofacies models are converted into coarser lithofacies models within the same modeling domain with larger grid blocks using the three up-scaling methods, and a series of multiphase thermo-hydrological numerical simulation is performed using TOUGH2-MP (Zhang et al., 2008) to predict probabilistically behavior of injected carbon dioxide. The coarser lithofacies models are also compared and analyzed with the borehole data and finer lithofacies models to evaluate the relative suitability of the three up-scaling methods. Three-dimensional geologic modeling, up-scaling, and multiphase thermo-hydrological numerical simulation as linked methodologies presented in this study can be utilized as a practical probabilistic evaluation tool to predict behavior of injected carbon dioxide and even to analyze its leakage risk. This work was supported by the Korea CCS 2020 Project of the Korea Carbon Capture and Sequestration R&D Center (KCRC) funded by the National Research Foundation (NRF), Ministry of Science and ICT (MSIT), Korea.

Probabilistic material degradation model for aerospace materials subjected to high temperature, mechanical and thermal fatigue, and creep

NASA Technical Reports Server (NTRS)

Boyce, L.

1992-01-01

A probabilistic general material strength degradation model has been developed for structural components of aerospace propulsion systems subjected to diverse random effects. The model has been implemented in two FORTRAN programs, PROMISS (Probabilistic Material Strength Simulator) and PROMISC (Probabilistic Material Strength Calibrator). PROMISS calculates the random lifetime strength of an aerospace propulsion component due to as many as eighteen diverse random effects. Results are presented in the form of probability density functions and cumulative distribution functions of lifetime strength. PROMISC calibrates the model by calculating the values of empirical material constants.

Agent autonomy approach to probabilistic physics-of-failure modeling of complex dynamic systems with interacting failure mechanisms

NASA Astrophysics Data System (ADS)

Gromek, Katherine Emily

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007.

Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling

PubMed Central

Franke, Michael; Degen, Judith

2016-01-01

Recent advances in probabilistic pragmatics have achieved considerable success in modeling speakers’ and listeners’ pragmatic reasoning as probabilistic inference. However, these models are usually applied to population-level data, and so implicitly suggest a homogeneous population without individual differences. Here we investigate potential individual differences in Theory-of-Mind related depth of pragmatic reasoning in so-called reference games that require drawing ad hoc Quantity implicatures of varying complexity. We show by Bayesian model comparison that a model that assumes a heterogenous population is a better predictor of our data, especially for comprehension. We discuss the implications for the treatment of individual differences in probabilistic models of language use. PMID:27149675

System Risk Assessment and Allocation in Conceptual Design

NASA Technical Reports Server (NTRS)

Mahadevan, Sankaran; Smith, Natasha L.; Zang, Thomas A. (Technical Monitor)

2003-01-01

As aerospace systems continue to evolve in addressing newer challenges in air and space transportation, there exists a heightened priority for significant improvement in system performance, cost effectiveness, reliability, and safety. Tools, which synthesize multidisciplinary integration, probabilistic analysis, and optimization, are needed to facilitate design decisions allowing trade-offs between cost and reliability. This study investigates tools for probabilistic analysis and probabilistic optimization in the multidisciplinary design of aerospace systems. A probabilistic optimization methodology is demonstrated for the low-fidelity design of a reusable launch vehicle at two levels, a global geometry design and a local tank design. Probabilistic analysis is performed on a high fidelity analysis of a Navy missile system. Furthermore, decoupling strategies are introduced to reduce the computational effort required for multidisciplinary systems with feedback coupling.

Fully probabilistic control design in an adaptive critic framework.

PubMed

Herzallah, Randa; Kárný, Miroslav

2011-12-01

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem; in particular, very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic control algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this paper. Copyright © 2011 Elsevier Ltd. All rights reserved.

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

PubMed Central

Marković, Dimitrije; Gläscher, Jan; Bossaerts, Peter; O’Doherty, John; Kiebel, Stefan J.

2015-01-01

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects. PMID:26495984

Ensemble assimilation of ARGO temperature profile, sea surface temperature, and altimetric satellite data into an eddy permitting primitive equation model of the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Yan, Y.; Barth, A.; Beckers, J. M.; Candille, G.; Brankart, J. M.; Brasseur, P.

2015-07-01

Sea surface height, sea surface temperature, and temperature profiles at depth collected between January and December 2005 are assimilated into a realistic eddy permitting primitive equation model of the North Atlantic Ocean using the Ensemble Kalman Filter. Sixty ensemble members are generated by adding realistic noise to the forcing parameters related to the temperature. The ensemble is diagnosed and validated by comparison between the ensemble spread and the model/observation difference, as well as by rank histogram before the assimilation experiments. An incremental analysis update scheme is applied in order to reduce spurious oscillations due to the model state correction. The results of the assimilation are assessed according to both deterministic and probabilistic metrics with independent/semiindependent observations. For deterministic validation, the ensemble means, together with the ensemble spreads are compared to the observations, in order to diagnose the ensemble distribution properties in a deterministic way. For probabilistic validation, the continuous ranked probability score (CRPS) is used to evaluate the ensemble forecast system according to reliability and resolution. The reliability is further decomposed into bias and dispersion by the reduced centered random variable (RCRV) score in order to investigate the reliability properties of the ensemble forecast system. The improvement of the assimilation is demonstrated using these validation metrics. Finally, the deterministic validation and the probabilistic validation are analyzed jointly. The consistency and complementarity between both validations are highlighted.

Probabilistic eruption forecasting at short and long time scales

NASA Astrophysics Data System (ADS)

Marzocchi, Warner; Bebbington, Mark S.

2012-10-01

Any effective volcanic risk mitigation strategy requires a scientific assessment of the future evolution of a volcanic system and its eruptive behavior. Some consider the onus should be on volcanologists to provide simple but emphatic deterministic forecasts. This traditional way of thinking, however, does not deal with the implications of inherent uncertainties, both aleatoric and epistemic, that are inevitably present in observations, monitoring data, and interpretation of any natural system. In contrast to deterministic predictions, probabilistic eruption forecasting attempts to quantify these inherent uncertainties utilizing all available information to the extent that it can be relied upon and is informative. As with many other natural hazards, probabilistic eruption forecasting is becoming established as the primary scientific basis for planning rational risk mitigation actions: at short-term (hours to weeks or months), it allows decision-makers to prioritize actions in a crisis; and at long-term (years to decades), it is the basic component for land use and emergency planning. Probabilistic eruption forecasting consists of estimating the probability of an eruption event and where it sits in a complex multidimensional time-space-magnitude framework. In this review, we discuss the key developments and features of models that have been used to address the problem.

Probabilistic graphs as a conceptual and computational tool in hydrology and water management

NASA Astrophysics Data System (ADS)

Schoups, Gerrit

2014-05-01

Originally developed in the fields of machine learning and artificial intelligence, probabilistic graphs constitute a general framework for modeling complex systems in the presence of uncertainty. The framework consists of three components: 1. Representation of the model as a graph (or network), with nodes depicting random variables in the model (e.g. parameters, states, etc), which are joined together by factors. Factors are local probabilistic or deterministic relations between subsets of variables, which, when multiplied together, yield the joint distribution over all variables. 2. Consistent use of probability theory for quantifying uncertainty, relying on basic rules of probability for assimilating data into the model and expressing unknown variables as a function of observations (via the posterior distribution). 3. Efficient, distributed approximation of the posterior distribution using general-purpose algorithms that exploit model structure encoded in the graph. These attributes make probabilistic graphs potentially useful as a conceptual and computational tool in hydrology and water management (and beyond). Conceptually, they can provide a common framework for existing and new probabilistic modeling approaches (e.g. by drawing inspiration from other fields of application), while computationally they can make probabilistic inference feasible in larger hydrological models. The presentation explores, via examples, some of these benefits.

Littelmann path model for geometric crystals, Whittaker functions on Lie groups and Brownian motion

NASA Astrophysics Data System (ADS)

Chhaibi, Reda

2013-02-01

Generally speaking, this thesis focuses on the interplay between the representations of Lie groups and probability theory. It subdivides into essentially three parts. In a first rather algebraic part, we construct a path model for geometric crystals in the sense of Berenstein and Kazhdan, for complex semi-simple Lie groups. We will mainly describe the algebraic structure, its natural morphisms and parameterizations. The theory of total positivity will play a particularly important role. Then, we anticipate on the probabilistic part by exhibiting a canonical measure on geometric crystals. It uses as ingredients the superpotential for the flag manifold and a measure invariant under the crystal actions. The image measure under the weight map plays the role of Duistermaat-Heckman measure. Its Laplace transform defines Whittaker functions, providing an interesting formula for all Lie groups. Then it appears clearly that Whittaker functions are to geometric crystals, what characters are to combinatorial crystals. The Littlewood-Richardson rule is also exposed. Finally we present the probabilistic approach that allows to find the canonical measure. It is based on the fundamental idea that the Wiener measure will induce the adequate measure on the algebraic structures through the path model. In the last chapter, we show how our geometric model degenerates to the continuous classical Littelmann path model and thus recover known results. For example, the canonical measure on a geometric crystal of highest weight degenerates into a uniform measure on a polytope, and recovers the parameterizations of continuous crystals.

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing.

PubMed

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models.

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

PubMed Central

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models. PMID:29062288

Using stochastic language models (SLM) to map lexical, syntactic, and phonological information processing in the brain.

PubMed

Lopopolo, Alessandro; Frank, Stefan L; van den Bosch, Antal; Willems, Roel M

2017-01-01

Language comprehension involves the simultaneous processing of information at the phonological, syntactic, and lexical level. We track these three distinct streams of information in the brain by using stochastic measures derived from computational language models to detect neural correlates of phoneme, part-of-speech, and word processing in an fMRI experiment. Probabilistic language models have proven to be useful tools for studying how language is processed as a sequence of symbols unfolding in time. Conditional probabilities between sequences of words are at the basis of probabilistic measures such as surprisal and perplexity which have been successfully used as predictors of several behavioural and neural correlates of sentence processing. Here we computed perplexity from sequences of words and their parts of speech, and their phonemic transcriptions. Brain activity time-locked to each word is regressed on the three model-derived measures. We observe that the brain keeps track of the statistical structure of lexical, syntactic and phonological information in distinct areas.

Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

PubMed

Mørk, Søren; Holmes, Ian

2012-03-01

Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

Probabilistic Assessment of Cancer Risk for Astronauts on Lunar Missions

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee Y.; Cucinotta, Francis A.

2009-01-01

During future lunar missions, exposure to solar particle events (SPEs) is a major safety concern for crew members during extra-vehicular activities (EVAs) on the lunar surface or Earth-to-moon transit. NASA s new lunar program anticipates that up to 15% of crew time may be on EVA, with minimal radiation shielding. For the operational challenge to respond to events of unknown size and duration, a probabilistic risk assessment approach is essential for mission planning and design. Using the historical database of proton measurements during the past 5 solar cycles, a typical hazard function for SPE occurrence was defined using a non-homogeneous Poisson model as a function of time within a non-specific future solar cycle of 4000 days duration. Distributions ranging from the 5th to 95th percentile of particle fluences for a specified mission period were simulated. Organ doses corresponding to particle fluences at the median and at the 95th percentile for a specified mission period were assessed using NASA s baryon transport model, BRYNTRN. The cancer fatality risk for astronauts as functions of age, gender, and solar cycle activity were then analyzed. The probability of exceeding the NASA 30- day limit of blood forming organ (BFO) dose inside a typical spacecraft was calculated. Future work will involve using this probabilistic risk assessment approach to SPE forecasting, combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

DCMDN: Deep Convolutional Mixture Density Network

NASA Astrophysics Data System (ADS)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.

Probabilistic structural analysis methods of hot engine structures

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Hopkins, D. A.

1989-01-01

Development of probabilistic structural analysis methods for hot engine structures is a major activity at Lewis Research Center. Recent activities have focused on extending the methods to include the combined uncertainties in several factors on structural response. This paper briefly describes recent progress on composite load spectra models, probabilistic finite element structural analysis, and probabilistic strength degradation modeling. Progress is described in terms of fundamental concepts, computer code development, and representative numerical results.

Online probabilistic learning with an ensemble of forecasts

NASA Astrophysics Data System (ADS)

Thorey, Jean; Mallet, Vivien; Chaussin, Christophe

2016-04-01

Our objective is to produce a calibrated weighted ensemble to forecast a univariate time series. In addition to a meteorological ensemble of forecasts, we rely on observations or analyses of the target variable. The celebrated Continuous Ranked Probability Score (CRPS) is used to evaluate the probabilistic forecasts. However applying the CRPS on weighted empirical distribution functions (deriving from the weighted ensemble) may introduce a bias because of which minimizing the CRPS does not produce the optimal weights. Thus we propose an unbiased version of the CRPS which relies on clusters of members and is strictly proper. We adapt online learning methods for the minimization of the CRPS. These methods generate the weights associated to the members in the forecasted empirical distribution function. The weights are updated before each forecast step using only past observations and forecasts. Our learning algorithms provide the theoretical guarantee that, in the long run, the CRPS of the weighted forecasts is at least as good as the CRPS of any weighted ensemble with weights constant in time. In particular, the performance of our forecast is better than that of any subset ensemble with uniform weights. A noteworthy advantage of our algorithm is that it does not require any assumption on the distributions of the observations and forecasts, both for the application and for the theoretical guarantee to hold. As application example on meteorological forecasts for photovoltaic production integration, we show that our algorithm generates a calibrated probabilistic forecast, with significant performance improvements on probabilistic diagnostic tools (the CRPS, the reliability diagram and the rank histogram).

Formal analysis and evaluation of the back-off procedure in IEEE802.11P VANET

NASA Astrophysics Data System (ADS)

Jin, Li; Zhang, Guoan; Zhu, Xiaojun

2017-07-01

The back-off procedure is one of the media access control technologies in 802.11P communication protocol. It plays an important role in avoiding message collisions and allocating channel resources. Formal methods are effective approaches for studying the performances of communication systems. In this paper, we establish a discrete time model for the back-off procedure. We use Markov Decision Processes (MDPs) to model the non-deterministic and probabilistic behaviors of the procedure, and use the probabilistic computation tree logic (PCTL) language to express different properties, which ensure that the discrete time model performs their basic functionality. Based on the model and PCTL specifications, we study the effect of contention window length on the number of senders in the neighborhood of given receivers, and that on the station’s expected cost required by the back-off procedure to successfully send packets. The variation of the window length may increase or decrease the maximum probability of correct transmissions within a time contention unit. We propose to use PRISM model checker to describe our proposed back-off procedure for IEEE802.11P protocol in vehicle network, and define different probability properties formulas to automatically verify the model and derive numerical results. The obtained results are helpful for justifying the values of the time contention unit.

A global empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

2015-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

An empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

2016-04-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Application of Probabilistic Analysis to Aircraft Impact Dynamics

NASA Technical Reports Server (NTRS)

Lyle, Karen H.; Padula, Sharon L.; Stockwell, Alan E.

2003-01-01

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stressstrain behaviors, laminated composites, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the uncertainty in the simulated responses. Several criteria are used to determine that a response surface method is the most appropriate probabilistic approach. The work is extended to compare optimization results with and without probabilistic constraints.

Real-time probabilistic covariance tracking with efficient model update.

PubMed

Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li

2012-05-01

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

NASA Technical Reports Server (NTRS)

Warner, James E.; Hochhalter, Jacob D.; Leser, William P.; Leser, Patrick E.; Newman, John A

2016-01-01

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

scoringRules - A software package for probabilistic model evaluation

NASA Astrophysics Data System (ADS)

Lerch, Sebastian; Jordan, Alexander; Krüger, Fabian

2016-04-01

Models in the geosciences are generally surrounded by uncertainty, and being able to quantify this uncertainty is key to good decision making. Accordingly, probabilistic forecasts in the form of predictive distributions have become popular over the last decades. With the proliferation of probabilistic models arises the need for decision theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way. Various scoring rules have been developed over the past decades to address this demand. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. As such, they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This poster presents the software package scoringRules for the statistical programming language R, which contains functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. Two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, Bayesian forecasts produced via Markov Chain Monte Carlo take this form. Thereby, the scoringRules package provides a framework for generalized model evaluation that both includes Bayesian as well as classical parametric models. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices.

Nine steps to risk-informed wellhead protection and management: Methods and application to the Burgberg Catchment

NASA Astrophysics Data System (ADS)

Nowak, W.; Enzenhoefer, R.; Bunk, T.

2013-12-01

Wellhead protection zones are commonly delineated via advective travel time analysis without considering any aspects of model uncertainty. In the past decade, research efforts produced quantifiable risk-based safety margins for protection zones. They are based on well vulnerability criteria (e.g., travel times, exposure times, peak concentrations) cast into a probabilistic setting, i.e., they consider model and parameter uncertainty. Practitioners still refrain from applying these new techniques for mainly three reasons. (1) They fear the possibly cost-intensive additional areal demand of probabilistic safety margins, (2) probabilistic approaches are allegedly complex, not readily available, and consume huge computing resources, and (3) uncertainty bounds are fuzzy, whereas final decisions are binary. The primary goal of this study is to show that these reservations are unjustified. We present a straightforward and computationally affordable framework based on a novel combination of well-known tools (e.g., MODFLOW, PEST, Monte Carlo). This framework provides risk-informed decision support for robust and transparent wellhead delineation under uncertainty. Thus, probabilistic risk-informed wellhead protection is possible with methods readily available for practitioners. As vivid proof of concept, we illustrate our key points on a pumped karstic well catchment, located in Germany. In the case study, we show that reliability levels can be increased by re-allocating the existing delineated area at no increase in delineated area. This is achieved by simply swapping delineated low-risk areas against previously non-delineated high-risk areas. Also, we show that further improvements may often be available at only low additional delineation area. Depending on the context, increases or reductions of delineated area directly translate to costs and benefits, if the land is priced, or if land owners need to be compensated for land use restrictions.

Probabilistic Modeling of Settlement Risk at Land Disposal Facilities - 12304

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foye, Kevin C.; Soong, Te-Yang

2012-07-01

The long-term reliability of land disposal facility final cover systems - and therefore the overall waste containment - depends on the distortions imposed on these systems by differential settlement/subsidence. The evaluation of differential settlement is challenging because of the heterogeneity of the waste mass (caused by inconsistent compaction, void space distribution, debris-soil mix ratio, waste material stiffness, time-dependent primary compression of the fine-grained soil matrix, long-term creep settlement of the soil matrix and the debris, etc.) at most land disposal facilities. Deterministic approaches to long-term final cover settlement prediction are not able to capture the spatial variability in the wastemore » mass and sub-grade properties which control differential settlement. An alternative, probabilistic solution is to use random fields to model the waste and sub-grade properties. The modeling effort informs the design, construction, operation, and maintenance of land disposal facilities. A probabilistic method to establish design criteria for waste placement and compaction is introduced using the model. Random fields are ideally suited to problems of differential settlement modeling of highly heterogeneous foundations, such as waste. Random fields model the seemingly random spatial distribution of a design parameter, such as compressibility. When used for design, the use of these models prompts the need for probabilistic design criteria. It also allows for a statistical approach to waste placement acceptance criteria. An example design evaluation was performed, illustrating the use of the probabilistic differential settlement simulation methodology to assemble a design guidance chart. The purpose of this design evaluation is to enable the designer to select optimal initial combinations of design slopes and quality control acceptance criteria that yield an acceptable proportion of post-settlement slopes meeting some design minimum. For this specific example, relative density, which can be determined through field measurements, was selected as the field quality control parameter for waste placement. This technique can be extended to include a rigorous performance-based methodology using other parameters (void space criteria, debris-soil mix ratio, pre-loading, etc.). As shown in this example, each parameter range, or sets of parameter ranges can be selected such that they can result in an acceptable, long-term differential settlement according to the probabilistic model. The methodology can also be used to re-evaluate the long-term differential settlement behavior at closed land disposal facilities to identify, if any, problematic facilities so that remedial action (e.g., reinforcement of upper and intermediate waste layers) can be implemented. Considering the inherent spatial variability in waste and earth materials and the need for engineers to apply sound quantitative practices to engineering analysis, it is important to apply the available probabilistic techniques to problems of differential settlement. One such method to implement probability-based differential settlement analyses for the design of landfill final covers has been presented. The design evaluation technique presented is one tool to bridge the gap from deterministic practice to probabilistic practice. (authors)« less

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI).

PubMed

Pappenberger, F; Jendritzky, G; Staiger, H; Dutra, E; Di Giuseppe, F; Richardson, D S; Cloke, H L

2015-03-01

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecasting has had two further limitations: it has been narrowly focused on a particular region or nation and has relied on the use of single 'deterministic' forecasts. Here, the UTCI is computed on a global scale, which is essential for international health-hazard warnings and disaster preparedness, and it is provided as a probabilistic forecast. It is shown that probabilistic UTCI forecasts are superior in skill to deterministic forecasts and that despite global variations, the UTCI forecast is skilful for lead times up to 10 days. The paper also demonstrates the utility of probabilistic UTCI forecasts on the example of the 2010 heat wave in Russia.

The composite load spectra project

NASA Technical Reports Server (NTRS)

Newell, J. F.; Ho, H.; Kurth, R. E.

1990-01-01

Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

NASA Technical Reports Server (NTRS)

Cruse, T. A.

1987-01-01

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

NASA Technical Reports Server (NTRS)

Cruse, T. A.; Burnside, O. H.; Wu, Y.-T.; Polch, E. Z.; Dias, J. B.

1988-01-01

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration.

PubMed

Oh-Descher, Hanna; Beck, Jeffrey M; Ferrari, Silvia; Sommer, Marc A; Egner, Tobias

2017-11-15

Real-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. To mitigate these pressures, people "satisfice", foregoing a full evaluation of all available evidence to focus on a subset of cues that allow for fast and "good-enough" decisions. Although this form of decision-making likely mediates many of our everyday choices, very little is known about the way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants' choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed near-optimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in the amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic Phonotactics as a Cue for Recognizing Spoken Cantonese Words in Speech

ERIC Educational Resources Information Center

Yip, Michael C. W.

2017-01-01

Previous experimental psycholinguistic studies suggested that the probabilistic phonotactics information might likely to hint the locations of word boundaries in continuous speech and hence posed an interesting solution to the empirical question on how we recognize/segment individual spoken word in speech. We investigated this issue by using…

Development of a Probabilistic Decision-Support Model to Forecast Coastal Resilience

NASA Astrophysics Data System (ADS)

Wilson, K.; Safak, I.; Brenner, O.; Lentz, E. E.; Hapke, C. J.

2016-02-01

Site-specific forecasts of coastal change are a valuable management tool in preparing for and assessing storm-driven impacts in coastal areas. More specifically, understanding the likelihood of storm impacts, recovery following events, and the alongshore variability of both is central in evaluating vulnerability and resiliency of barrier islands. We introduce a probabilistic modeling framework that integrates hydrodynamic, anthropogenic, and morphologic components of the barrier system to evaluate coastal change at Fire Island, New York. The model is structured on a Bayesian network (BN), which utilizes observations to learn statistical relationships between system variables. In addition to predictive ability, probabilistic models convey the level of confidence associated with a prediction, an important consideration for coastal managers. Our model predicts the likelihood of morphologic change on the upper beach based on several decades of beach monitoring data. A coupled hydrodynamic BN combines probabilistic and deterministic modeling approaches; by querying nearly two decades of nested-grid wave simulations that account for both distant swells and local seas, we produce scenarios of event and seasonal wave climates. The wave scenarios of total water level - a sum of run up, surge and tide - and anthropogenic modification are the primary drivers of morphologic change in our model structure. Preliminary results show the hydrodynamic BN is able to reproduce time series of total water levels, a critical validation process before generating scenarios, and forecasts of geomorphic change over three month intervals are up to 70% accurate. Predictions of storm-induced change and recovery are linked to evaluate zones of persistent vulnerability or resilience and will help managers target restoration efforts, identify areas most vulnerable to habitat degradation, and highlight resilient zones that may best support relocation of critical infrastructure.

Worst case encoder-decoder policies for a communication system in the presence of an unknown probabilistic jammer

NASA Astrophysics Data System (ADS)

Cascio, David M.

1988-05-01

States of nature or observed data are often stochastically modelled as Gaussian random variables. At times it is desirable to transmit this information from a source to a destination with minimal distortion. Complicating this objective is the possible presence of an adversary attempting to disrupt this communication. In this report, solutions are provided to a class of minimax and maximin decision problems, which involve the transmission of a Gaussian random variable over a communications channel corrupted by both additive Gaussian noise and probabilistic jamming noise. The jamming noise is termed probabilistic in the sense that with nonzero probability 1-P, the jamming noise is prevented from corrupting the channel. We shall seek to obtain optimal linear encoder-decoder policies which minimize given quadratic distortion measures.

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-01-01

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm. PMID:28587084

A Max-Flow Based Algorithm for Connected Target Coverage with Probabilistic Sensors.

PubMed

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao; Wang, Wensheng

2017-05-25

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ -connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Process for computing geometric perturbations for probabilistic analysis

DOEpatents

Fitch, Simeon H. K. [Charlottesville, VA; Riha, David S [San Antonio, TX; Thacker, Ben H [San Antonio, TX

2012-04-10

A method for computing geometric perturbations for probabilistic analysis. The probabilistic analysis is based on finite element modeling, in which uncertainties in the modeled system are represented by changes in the nominal geometry of the model, referred to as "perturbations". These changes are accomplished using displacement vectors, which are computed for each node of a region of interest and are based on mean-value coordinate calculations.

Probabilistic transport models for plasma transport in the presence of critical thresholds: Beyond the diffusive paradigma)

NASA Astrophysics Data System (ADS)

Sánchez, R.; van Milligen, B. Ph.; Carreras, B. A.

2005-05-01

It is argued that the modeling of plasma transport in tokamaks may benefit greatly from extending the usual local paradigm to accommodate scale-free transport mechanisms. This can be done by combining Lévy distributions and a nonlinear threshold condition within the continuous time random walk concept. The advantages of this nonlocal, nonlinear extension are illustrated by constructing a simple particle density transport model that, as a result of these ideas, spontaneously exhibits much of nondiffusive phenomenology routinely observed in tokamaks. The fluid limit of the system shows that the kind of equations that are appropriate to capture these dynamics are based on fractional differential operators. In them, effective diffusivities and pinch velocities are found that are dynamically set by the system in response to the specific characteristics of the fueling source and external perturbations. This fact suggests some dramatic consequences for the extrapolation of these transport properties to larger size systems.

Assessment of the long-lead probabilistic prediction for the Asian summer monsoon precipitation (1983-2011) based on the APCC multimodel system and a statistical model

NASA Astrophysics Data System (ADS)

Sohn, Soo-Jin; Min, Young-Mi; Lee, June-Yi; Tam, Chi-Yung; Kang, In-Sik; Wang, Bin; Ahn, Joong-Bae; Yamagata, Toshio

2012-02-01

The performance of the probabilistic multimodel prediction (PMMP) system of the APEC Climate Center (APCC) in predicting the Asian summer monsoon (ASM) precipitation at a four-month lead (with February initial condition) was compared with that of a statistical model using hindcast data for 1983-2005 and real-time forecasts for 2006-2011. Particular attention was paid to probabilistic precipitation forecasts for the boreal summer after the mature phase of El Niño and Southern Oscillation (ENSO). Taking into account the fact that coupled models' skill for boreal spring and summer precipitation mainly comes from their ability to capture ENSO teleconnection, we developed the statistical model using linear regression with the preceding winter ENSO condition as the predictor. Our results reveal several advantages and disadvantages in both forecast systems. First, the PMMP appears to have higher skills for both above- and below-normal categories in the six-year real-time forecast period, whereas the cross-validated statistical model has higher skills during the 23-year hindcast period. This implies that the cross-validated statistical skill may be overestimated. Second, the PMMP is the better tool for capturing atypical ENSO (or non-canonical ENSO related) teleconnection, which has affected the ASM precipitation during the early 1990s and in the recent decade. Third, the statistical model is more sensitive to the ENSO phase and has an advantage in predicting the ASM precipitation after the mature phase of La Niña.

A mediation model to explain decision making under conditions of risk among adolescents: the role of fluid intelligence and probabilistic reasoning.

PubMed

Donati, Maria Anna; Panno, Angelo; Chiesi, Francesca; Primi, Caterina

2014-01-01

This study tested the mediating role of probabilistic reasoning ability in the relationship between fluid intelligence and advantageous decision making among adolescents in explicit situations of risk--that is, in contexts in which information on the choice options (gains, losses, and probabilities) were explicitly presented at the beginning of the task. Participants were 282 adolescents attending high school (77% males, mean age = 17.3 years). We first measured fluid intelligence and probabilistic reasoning ability. Then, to measure decision making under explicit conditions of risk, participants performed the Game of Dice Task, in which they have to decide among different alternatives that are explicitly linked to a specific amount of gain or loss and have obvious winning probabilities that are stable over time. Analyses showed a significant positive indirect effect of fluid intelligence on advantageous decision making through probabilistic reasoning ability that acted as a mediator. Specifically, fluid intelligence may enhance ability to reason in probabilistic terms, which in turn increases the likelihood of advantageous choices when adolescents are confronted with an explicit decisional context. Findings show that in experimental paradigm settings, adolescents are able to make advantageous decisions using cognitive abilities when faced with decisions under explicit risky conditions. This study suggests that interventions designed to promote probabilistic reasoning, for example by incrementing the mathematical prerequisites necessary to reason in probabilistic terms, may have a positive effect on adolescents' decision-making abilities.

E-Area LLWF Vadose Zone Model: Probabilistic Model for Estimating Subsided-Area Infiltration Rates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyer, J.; Flach, G.

A probabilistic model employing a Monte Carlo sampling technique was developed in Python to generate statistical distributions of the upslope-intact-area to subsided-area ratio (Area UAi/Area SAi) for closure cap subsidence scenarios that differ in assumed percent subsidence and the total number of intact plus subsided compartments. The plan is to use this model as a component in the probabilistic system model for the E-Area Performance Assessment (PA), contributing uncertainty in infiltration estimates.

Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2015-08-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

Developing Probabilistic Safety Performance Margins for Unknown and Underappreciated Risks

NASA Technical Reports Server (NTRS)

Benjamin, Allan; Dezfuli, Homayoon; Everett, Chris

2015-01-01

Probabilistic safety requirements currently formulated or proposed for space systems, nuclear reactor systems, nuclear weapon systems, and other types of systems that have a low-probability potential for high-consequence accidents depend on showing that the probability of such accidents is below a specified safety threshold or goal. Verification of compliance depends heavily upon synthetic modeling techniques such as PRA. To determine whether or not a system meets its probabilistic requirements, it is necessary to consider whether there are significant risks that are not fully considered in the PRA either because they are not known at the time or because their importance is not fully understood. The ultimate objective is to establish a reasonable margin to account for the difference between known risks and actual risks in attempting to validate compliance with a probabilistic safety threshold or goal. In this paper, we examine data accumulated over the past 60 years from the space program, from nuclear reactor experience, from aircraft systems, and from human reliability experience to formulate guidelines for estimating probabilistic margins to account for risks that are initially unknown or underappreciated. The formulation includes a review of the safety literature to identify the principal causes of such risks.

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

NASA Astrophysics Data System (ADS)

Wahl, N.; Hennig, P.; Wieser, H. P.; Bangert, M.

2017-07-01

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ≤slant {5} min). The resulting standard deviation (expectation value) of dose show average global γ{3% / {3}~mm} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.

Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy.

PubMed

Wahl, N; Hennig, P; Wieser, H P; Bangert, M

2017-06-26

The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU [Formula: see text] min). The resulting standard deviation (expectation value) of dose show average global [Formula: see text] pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.

Near Real-Time Event Detection & Prediction Using Intelligent Software Agents

DTIC Science & Technology

2006-03-01

value was 0.06743. Multiple autoregressive integrated moving average ( ARIMA ) models were then build to see if the raw data, differenced data, or...slight improvement. The best adjusted r^2 value was found to be 0.1814. Successful results were not expected from linear or ARIMA -based modelling ...appear, 2005. [63] Mora-Lopez, L., Mora, J., Morales-Bueno, R., et al. Modelling time series of climatic parameters with probabilistic finite

Mastodon

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coleman, Justin Leigh; Veeraraghavan, Swetha; Bolisetti, Chandrakanth

MASTODON has the capability to model stochastic nonlinear soil-structure interaction (NLSSI) in a dynamic probabilistic risk assessment framework. The NLSSI simulations include structural dynamics, time integration, dynamic porous media flow, nonlinear hysteretic soil constitutive models, geometric nonlinearities (gapping, sliding, and uplift). MASTODON is also the MOOSE based master application for dynamic PRA of external hazards.

Probabilistic In Situ Stress Estimation and Forecasting using Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Fichtner, A.; van Dinther, Y.; Kuensch, H. R.

2017-12-01

Our physical understanding and forecasting ability of earthquakes, and other solid Earth dynamic processes, is significantly hampered by limited indications on the evolving state of stress and strength on faults. Integrating observations and physics-based numerical modeling to quantitatively estimate this evolution of a fault's state is crucial. However, systematic attempts are limited and tenuous, especially in light of the scarcity and uncertainty of natural data and the difficulty of modelling the physics governing earthquakes. We adopt the statistical framework of sequential data assimilation - extensively developed for weather forecasting - to efficiently integrate observations and prior knowledge in a forward model, while acknowledging errors in both. To prove this concept we perform a perfect model test in a simplified subduction zone setup, where we assimilate synthetic noised data on velocities and stresses from a single location. Using an Ensemble Kalman Filter, these data and their errors are assimilated to update 150 ensemble members from a Partial Differential Equation-driven seismic cycle model. Probabilistic estimates of fault stress and dynamic strength evolution capture the truth exceptionally well. This is possible, because the sampled error covariance matrix contains prior information from the physics that relates velocities, stresses and pressure at the surface to those at the fault. During the analysis step, stress and strength distributions are thus reconstructed such that fault coupling can be updated to either inhibit or trigger events. In the subsequent forecast step the physical equations are solved to propagate the updated states forward in time and thus provide probabilistic information on the occurrence of the next event. At subsequent assimilation steps, the system's forecasting ability turns out to be significantly better than that of a periodic recurrence model (requiring an alarm 17% vs. 68% of the time). This thus provides distinct added value with respect to using observations or numerical models separately. Although several challenges for applications to a natural setting remain, these first results indicate the large potential of data assimilation techniques for probabilistic seismic hazard assessment and other challenges in dynamic solid earth systems.

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review

PubMed Central

McClelland, James L.

2013-01-01

This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered. PMID:23970868

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review.

PubMed

McClelland, James L

2013-01-01

This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation (IA) model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of context in interactive neural networks, in which both bottom-up and top-down signals affect the interpretation of sensory inputs. It is pointed out that connectionist units that use the logistic or softmax activation functions can exactly compute Bayesian posterior probabilities when the bias terms and connection weights affecting such units are set to the logarithms of appropriate probabilistic quantities. Bayesian concepts such the prior, likelihood, (joint and marginal) posterior, probability matching and maximizing, and calculating vs. sampling from the posterior are all reviewed and linked to neural network computations. Probabilistic and neural network models are explicitly linked to the concept of a probabilistic generative model that describes the relationship between the underlying target of perception (e.g., the word intended by a speaker or other source of sensory stimuli) and the sensory input that reaches the perceiver for use in inferring the underlying target. It is shown how a new version of the IA model called the multinomial interactive activation (MIA) model can sample correctly from the joint posterior of a proposed generative model for perception of letters in words, indicating that interactive processing is fully consistent with principled probabilistic computation. Ways in which these computations might be realized in real neural systems are also considered.

Spatiotemporal movement planning and rapid adaptation for manual interaction.

PubMed

Huber, Markus; Kupferberg, Aleksandra; Lenz, Claus; Knoll, Alois; Brandt, Thomas; Glasauer, Stefan

2013-01-01

Many everyday tasks require the ability of two or more individuals to coordinate their actions with others to increase efficiency. Such an increase in efficiency can often be observed even after only very few trials. Previous work suggests that such behavioral adaptation can be explained within a probabilistic framework that integrates sensory input and prior experience. Even though higher cognitive abilities such as intention recognition have been described as probabilistic estimation depending on an internal model of the other agent, it is not clear whether much simpler daily interaction is consistent with a probabilistic framework. Here, we investigate whether the mechanisms underlying efficient coordination during manual interactions can be understood as probabilistic optimization. For this purpose we studied in several experiments a simple manual handover task concentrating on the action of the receiver. We found that the duration until the receiver reacts to the handover decreases over trials, but strongly depends on the position of the handover. We then replaced the human deliverer by different types of robots to further investigate the influence of the delivering movement on the reaction of the receiver. Durations were found to depend on movement kinematics and the robot's joint configuration. Modeling the task was based on the assumption that the receiver's decision to act is based on the accumulated evidence for a specific handover position. The evidence for this handover position is collected from observing the hand movement of the deliverer over time and, if appropriate, by integrating this sensory likelihood with prior expectation that is updated over trials. The close match of model simulations and experimental results shows that the efficiency of handover coordination can be explained by an adaptive probabilistic fusion of a-priori expectation and online estimation.

Joint Probabilistic Projection of Female and Male Life Expectancy

PubMed Central

Raftery, Adrian E.; Lalic, Nevena; Gerland, Patrick

2014-01-01

BACKGROUND The United Nations (UN) produces population projections for all countries every two years. These are used by international organizations, governments, the private sector and researchers for policy planning, for monitoring development goals, as inputs to economic and environmental models, and for social and health research. The UN is considering producing fully probabilistic population projections, for which joint probabilistic projections of future female and male life expectancy at birth are needed. OBJECTIVE We propose a methodology for obtaining joint probabilistic projections of female and male life expectancy at birth. METHODS We first project female life expectancy using a one-sex method for probabilistic projection of life expectancy. We then project the gap between female and male life expectancy. We propose an autoregressive model for the gap in a future time period for a particular country, which is a function of female life expectancy and a t-distributed random perturbation. This method takes into account mortality data limitations, is comparable across countries, and accounts for shocks. We estimate all parameters based on life expectancy estimates for 1950–2010. The methods are implemented in the bayesLife and bayesPop R packages. RESULTS We evaluated our model using out-of-sample projections for the period 1995–2010, and found that our method performed better than several possible alternatives. CONCLUSIONS We find that the average gap between female and male life expectancy has been increasing for female life expectancy below 75, and decreasing for female life expectancy above 75. Our projections of the gap are lower than the UN’s 2008 projections for most countries and so lead to higher projections of male life expectancy. PMID:25580082

Use of the LUS in sequence allele designations to facilitate probabilistic genotyping of NGS-based STR typing results.

PubMed

Just, Rebecca S; Irwin, Jodi A

2018-05-01

Some of the expected advantages of next generation sequencing (NGS) for short tandem repeat (STR) typing include enhanced mixture detection and genotype resolution via sequence variation among non-homologous alleles of the same length. However, at the same time that NGS methods for forensic DNA typing have advanced in recent years, many caseworking laboratories have implemented or are transitioning to probabilistic genotyping to assist the interpretation of complex autosomal STR typing results. Current probabilistic software programs are designed for length-based data, and were not intended to accommodate sequence strings as the product input. Yet to leverage the benefits of NGS for enhanced genotyping and mixture deconvolution, the sequence variation among same-length products must be utilized in some form. Here, we propose use of the longest uninterrupted stretch (LUS) in allele designations as a simple method to represent sequence variation within the STR repeat regions and facilitate - in the nearterm - probabilistic interpretation of NGS-based typing results. An examination of published population data indicated that a reference LUS region is straightforward to define for most autosomal STR loci, and that using repeat unit plus LUS length as the allele designator can represent greater than 80% of the alleles detected by sequencing. A proof of concept study performed using a freely available probabilistic software demonstrated that the LUS length can be used in allele designations when a program does not require alleles to be integers, and that utilizing sequence information improves interpretation of both single-source and mixed contributor STR typing results as compared to using repeat unit information alone. The LUS concept for allele designation maintains the repeat-based allele nomenclature that will permit backward compatibility to extant STR databases, and the LUS lengths themselves will be concordant regardless of the NGS assay or analysis tools employed. Further, these biologically based, easy-to-derive designations uphold clear relationships between parent alleles and their stutter products, enabling analysis in fully continuous probabilistic programs that model stutter while avoiding the algorithmic complexities that come with string based searches. Though using repeat unit plus LUS length as the allele designator does not capture variation that occurs outside of the core repeat regions, this straightforward approach would permit the large majority of known STR sequence variation to be used for mixture deconvolution and, in turn, result in more informative mixture statistics in the near term. Ultimately, the method could bridge the gap from current length-based probabilistic systems to facilitate broader adoption of NGS by forensic DNA testing laboratories. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

NASA Technical Reports Server (NTRS)

Rozier, Kristin Y.; Schumann, Johann; Ippolito, Corey

2015-01-01

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

How much are you prepared to PAY for a forecast?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Coughlan, Erin; Ramos, Maria-Helena; Pappenberger, Florian; Wetterhall, Fredrik; Bachofen, Carina; van Andel, Schalk Jan

2015-04-01

Probabilistic hydro-meteorological forecasts are a crucial element of the decision-making chain in the field of flood prevention. The operational use of probabilistic forecasts is increasingly promoted through the development of new novel state-of-the-art forecast methods and numerical skill is continuously increasing. However, the value of such forecasts for flood early-warning systems is a topic of diverging opinions. Indeed, the word value, when applied to flood forecasting, is multifaceted. It refers, not only to the raw cost of acquiring and maintaining a probabilistic forecasting system (in terms of human and financial resources, data volume and computational time), but also and most importantly perhaps, to the use of such products. This game aims at investigating this point. It is a willingness to pay game, embedded in a risk-based decision-making experiment. Based on a ``Red Cross/Red Crescent, Climate Centre'' game, it is a contribution to the international Hydrologic Ensemble Prediction Experiment (HEPEX). A limited number of probabilistic forecasts will be auctioned to the participants; the price of these forecasts being market driven. All participants (irrespective of having bought or not a forecast set) will then be taken through a decision-making process to issue warnings for extreme rainfall. This game will promote discussions around the topic of the value of forecasts for decision-making in the field of flood prevention.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

PubMed

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

PubMed Central

Rivas, Elena; Lang, Raymond; Eddy, Sean R.

2012-01-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

NASA Technical Reports Server (NTRS)

Abdi, Frank

1996-01-01

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.

Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghil, M.; Kravtsov, S.; Robertson, A. W.

2008-10-14

This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influencemore » large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.« less

Improving inflow forecasting into hydropower reservoirs through a complementary modelling framework

NASA Astrophysics Data System (ADS)

Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

2014-10-01

Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an error model added on top of an un-alterable constant parameter conceptual model, the models being demonstrated with reference to the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead-times up to 17 h. Season based evaluations indicated that the improvement in inflow forecasts varies across seasons and inflow forecasts in autumn and spring are less successful with the 95% prediction interval bracketing less than 95% of the observations for lead-times beyond 17 h.

Petri Net and Probabilistic Model Checking Based Approach for the Modelling, Simulation and Verification of Internet Worm Propagation

PubMed Central

Razzaq, Misbah; Ahmad, Jamil

2015-01-01

Internet worms are analogous to biological viruses since they can infect a host and have the ability to propagate through a chosen medium. To prevent the spread of a worm or to grasp how to regulate a prevailing worm, compartmental models are commonly used as a means to examine and understand the patterns and mechanisms of a worm spread. However, one of the greatest challenge is to produce methods to verify and validate the behavioural properties of a compartmental model. This is why in this study we suggest a framework based on Petri Nets and Model Checking through which we can meticulously examine and validate these models. We investigate Susceptible-Exposed-Infectious-Recovered (SEIR) model and propose a new model Susceptible-Exposed-Infectious-Recovered-Delayed-Quarantined (Susceptible/Recovered) (SEIDQR(S/I)) along with hybrid quarantine strategy, which is then constructed and analysed using Stochastic Petri Nets and Continuous Time Markov Chain. The analysis shows that the hybrid quarantine strategy is extremely effective in reducing the risk of propagating the worm. Through Model Checking, we gained insight into the functionality of compartmental models. Model Checking results validate simulation ones well, which fully support the proposed framework. PMID:26713449

Petri Net and Probabilistic Model Checking Based Approach for the Modelling, Simulation and Verification of Internet Worm Propagation.

PubMed

Razzaq, Misbah; Ahmad, Jamil

2015-01-01

Internet worms are analogous to biological viruses since they can infect a host and have the ability to propagate through a chosen medium. To prevent the spread of a worm or to grasp how to regulate a prevailing worm, compartmental models are commonly used as a means to examine and understand the patterns and mechanisms of a worm spread. However, one of the greatest challenge is to produce methods to verify and validate the behavioural properties of a compartmental model. This is why in this study we suggest a framework based on Petri Nets and Model Checking through which we can meticulously examine and validate these models. We investigate Susceptible-Exposed-Infectious-Recovered (SEIR) model and propose a new model Susceptible-Exposed-Infectious-Recovered-Delayed-Quarantined (Susceptible/Recovered) (SEIDQR(S/I)) along with hybrid quarantine strategy, which is then constructed and analysed using Stochastic Petri Nets and Continuous Time Markov Chain. The analysis shows that the hybrid quarantine strategy is extremely effective in reducing the risk of propagating the worm. Through Model Checking, we gained insight into the functionality of compartmental models. Model Checking results validate simulation ones well, which fully support the proposed framework.

Improved probabilistic inference as a general learning mechanism with action video games.

PubMed

Green, C Shawn; Pouget, Alexandre; Bavelier, Daphne

2010-09-14

Action video game play benefits performance in an array of sensory, perceptual, and attentional tasks that go well beyond the specifics of game play [1-9]. That a training regimen may induce improvements in so many different skills is notable because the majority of studies on training-induced learning report improvements on the trained task but limited transfer to other, even closely related, tasks ([10], but see also [11-13]). Here we ask whether improved probabilistic inference may explain such broad transfer. By using a visual perceptual decision making task [14, 15], the present study shows for the first time that action video game experience does indeed improve probabilistic inference. A neural model of this task [16] establishes how changing a single parameter, namely the strength of the connections between the neural layer providing the momentary evidence and the layer integrating the evidence over time, captures improvements in action-gamers behavior. These results were established in a visual, but also in a novel auditory, task, indicating generalization across modalities. Thus, improved probabilistic inference provides a general mechanism for why action video game playing enhances performance in a wide variety of tasks. In addition, this mechanism may serve as a signature of training regimens that are likely to produce transfer of learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Methodology for assessing quantities of water and proppant injection, and water production associated with development of continuous petroleum accumulations

USGS Publications Warehouse

Haines, Seth S.

2015-07-13

The quantities of water and hydraulic fracturing proppant required for producing petroleum (oil, gas, and natural gas liquids) from continuous accumulations, and the quantities of water extracted during petroleum production, can be quantitatively assessed using a probabilistic approach. The water and proppant assessment methodology builds on the U.S. Geological Survey methodology for quantitative assessment of undiscovered technically recoverable petroleum resources in continuous accumulations. The U.S. Geological Survey assessment methodology for continuous petroleum accumulations includes fundamental concepts such as geologically defined assessment units, and probabilistic input values including well-drainage area, sweet- and non-sweet-spot areas, and success ratio within the untested area of each assessment unit. In addition to petroleum-related information, required inputs for the water and proppant assessment methodology include probabilistic estimates of per-well water usage for drilling, cementing, and hydraulic-fracture stimulation; the ratio of proppant to water for hydraulic fracturing; the percentage of hydraulic fracturing water that returns to the surface as flowback; and the ratio of produced water to petroleum over the productive life of each well. Water and proppant assessments combine information from recent or current petroleum assessments with water- and proppant-related input values for the assessment unit being studied, using Monte Carlo simulation, to yield probabilistic estimates of the volume of water for drilling, cementing, and hydraulic fracture stimulation; the quantity of proppant for hydraulic fracture stimulation; and the volumes of water produced as flowback shortly after well completion, and produced over the life of the well.

A probabilistic NF2 relational algebra for integrated information retrieval and database systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fuhr, N.; Roelleke, T.

The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in non-first-normal-form (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Thus, the set of weighted index terms of a document are represented as a probabilistic subrelation. In a similar way, imprecise attribute values are modelled as a set-valued attribute. We redefine the relational operators for this type of relations such thatmore » the result of each operator is again a probabilistic NF2 relation, where the weight of a tuple gives the probability that this tuple belongs to the result. By ordering the tuples according to decreasing probabilities, the model yields a ranking of answers like in most IR models. This effect also can be used for typical database queries involving imprecise attribute values as well as for combinations of database and IR queries.« less

Avoiding the ensemble decorrelation problem using member-by-member post-processing

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2014-05-01

Forecast calibration or post-processing has become a standard tool in atmospheric and climatological science due to the presence of systematic initial condition and model errors. For ensemble forecasts the most competitive methods derive from the assumption of a fixed ensemble distribution. However, when independently applying such 'statistical' methods at different locations, lead times or for multiple variables the correlation structure for individual ensemble members is destroyed. Instead of reastablishing the correlation structure as in Schefzik et al. (2013) we instead propose a calibration method that avoids such problem by correcting each ensemble member individually. Moreover, we analyse the fundamental mechanisms by which the probabilistic ensemble skill can be enhanced. In terms of continuous ranked probability score, our member-by-member approach amounts to skill gain that extends for lead times far beyond the error doubling time and which is as good as the one of the most competitive statistical approach, non-homogeneous Gaussian regression (Gneiting et al. 2005). Besides the conservation of correlation structure, additional benefits arise including the fact that higher-order ensemble moments like kurtosis and skewness are inherited from the uncorrected forecasts. Our detailed analysis is performed in the context of the Kuramoto-Sivashinsky equation and different simple models but the results extent succesfully to the ensemble forecast of the European Centre for Medium-Range Weather Forecasts (Van Schaeybroeck and Vannitsem, 2013, 2014) . References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Schefzik, R., T.L. Thorarinsdottir, and T. Gneiting, 2013: Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling. To appear in Statistical Science 28. [3] Van Schaeybroeck, B., and S. Vannitsem, 2013: Reliable probabilities through statistical post-processing of ensemble forecasts. Proceedings of the European Conference on Complex Systems 2012, Springer proceedings on complexity, XVI, p. 347-352. [4] Van Schaeybroeck, B., and S. Vannitsem, 2014: Ensemble post-processing using member-by-member approaches: theoretical aspects, under review.

Automatized near-real-time short-term Probabilistic Volcanic Hazard Assessment of tephra dispersion before and during eruptions: BET_VHst for Mt. Etna

NASA Astrophysics Data System (ADS)

Selva, Jacopo; Scollo, Simona; Costa, Antonio; Brancato, Alfonso; Prestifilippo, Michele

2015-04-01

Tephra dispersal, even in small amounts, may heavily affect public health and critical infrastructures, such as airports, train and road networks, and electric power supply systems. Probabilistic Volcanic Hazard Assessment (PVHA) represents the most complete scientific contribution for planning rational strategies aimed at managing and mitigating the risk posed by activity during volcanic crises and during eruptions. Short-term PVHA (over time intervals in the order of hours to few days) must account for rapidly changing information coming from the monitoring system, as well as, updated wind forecast, and they must be accomplished in near-real-time. In addition, while during unrest the primary goal is to forecast potential eruptions, during eruptions it is also fundamental to correctly account for the real-time status of the eruption and of tephra dispersal, as well as its potential evolution in the short-term. Here, we present a preliminary application of BET_VHst model (Selva et al. 2014) for Mt. Etna. The model has its roots into present state deterministic procedure, and it deals with the large uncertainty that such procedures typically ignore, like uncertainty on the potential position of the vent and eruptive size, on the possible evolution of volcanological input during ongoing eruptions, as well as, on wind field. Uncertainty is treated by making use of Bayesian inference, alternative modeling procedures for tephra dispersal, and statistical mixing of long- and short-term analyses. References Selva J., Costa A., Sandri L., Macedonio G., Marzocchi W. (2014) Probabilistic short-term volcanic hazard in phases of unrest: a case study for tephra fallout, J. Geophys. Res., 119, doi: 10.1002/2014JB011252

Drawing the line on the sand

NASA Astrophysics Data System (ADS)

Ranasinghe, R.; Jongejan, R.; Wainwright, D.; Callaghan, D. P.

2016-02-01

Up to 70% of the world's sandy coastlines are eroding, resulting in gradual and continuous coastline recession. The rate of coastline recession is likely to increase due to the projected impacts of climate change on mean sea levels, offshore wave climate and storm surges. At the same time, rapid development in the world's coastal zones continues to increase potential damages, while often reducing the resilience of coastal systems. The risks associated with coastline recession are thus likely to increase over the coming decades, unless effective risk management plans are put in place. Land-use restrictions are a key component of coastal zone risk management plans. These involve the use of coastal setback lines which are mainly established by linearly adding the impacts of storms, recession due to sea level rise, and ambient long term trends in shoreline evolution. This approach does not differentiate between uncertainties that develop differently over time, nor takes into account the value and lifetime of property developments. Both shortcomings could entail considerable social cost. For balancing risk and reward, probabilistic estimates of coastline recession are a pre-requisite. Yet the presently adopted deterministic methods for establishing setback lines are unable to provide such estimates. Here, we present a quantitative risk analysis (QRA) model, underpinned by a multi-scale, physics based coastal recession model capable of providing time-dependent risk estimates. The modelling approach presented enables the determination of setback lines in terms of exceedance probabilities, a quantity that directly feeds into risk evaluations and economic optimizations. As a demonstration, the risk-informed approach is applied to Narrabeen beach, Sydney, Australia.

Probabilistic modeling of percutaneous absorption for risk-based exposure assessments and transdermal drug delivery.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, Clifford Kuofei

Chemical transport through human skin can play a significant role in human exposure to toxic chemicals in the workplace, as well as to chemical/biological warfare agents in the battlefield. The viability of transdermal drug delivery also relies on chemical transport processes through the skin. Models of percutaneous absorption are needed for risk-based exposure assessments and drug-delivery analyses, but previous mechanistic models have been largely deterministic. A probabilistic, transient, three-phase model of percutaneous absorption of chemicals has been developed to assess the relative importance of uncertain parameters and processes that may be important to risk-based assessments. Penetration routes through the skinmore » that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times. This probabilistic analysis of percutaneous absorption (PAPA) method has been developed to improve risk-based exposure assessments and transdermal drug-delivery analyses, where parameters and processes can be highly uncertain.« less

On the Use of the Beta Distribution in Probabilistic Resource Assessments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olea, Ricardo A., E-mail: olea@usgs.gov

2011-12-15

The triangular distribution is a popular choice when it comes to modeling bounded continuous random variables. Its wide acceptance derives mostly from its simple analytic properties and the ease with which modelers can specify its three parameters through the extremes and the mode. On the negative side, hardly any real process follows a triangular distribution, which from the outset puts at a disadvantage any model employing triangular distributions. At a time when numerical techniques such as the Monte Carlo method are displacing analytic approaches in stochastic resource assessments, easy specification remains the most attractive characteristic of the triangular distribution. Themore » beta distribution is another continuous distribution defined within a finite interval offering wider flexibility in style of variation, thus allowing consideration of models in which the random variables closely follow the observed or expected styles of variation. Despite its more complex definition, generation of values following a beta distribution is as straightforward as generating values following a triangular distribution, leaving the selection of parameters as the main impediment to practically considering beta distributions. This contribution intends to promote the acceptance of the beta distribution by explaining its properties and offering several suggestions to facilitate the specification of its two shape parameters. In general, given the same distributional parameters, use of the beta distributions in stochastic modeling may yield significantly different results, yet better estimates, than the triangular distribution.« less

High-resolution surface wave tomography of the European crust and uppermost mantle from ambient seismic noise

NASA Astrophysics Data System (ADS)

Lu, Yang; Stehly, Laurent; Paul, Anne; AlpArray Working Group

2018-05-01

Taking advantage of the large number of seismic stations installed in Europe, in particular in the greater Alpine region with the AlpArray experiment, we derive a new high-resolution 3-D shear-wave velocity model of the European crust and uppermost mantle from ambient noise tomography. The correlation of up to four years of continuous vertical-component seismic recordings from 1293 broadband stations (10° W-35° E, 30° N-75° N) provides Rayleigh wave group velocity dispersion data in the period band 5-150 s at more than 0.8 million virtual source-receiver pairs. Two-dimensional Rayleigh wave group velocity maps are estimated using adaptive parameterization to accommodate the strong heterogeneity of path coverage. A probabilistic 3-D shear-wave velocity model, including probability densities for the depth of layer boundaries and S-wave velocity values, is obtained by non-linear Bayesian inversion. A weighted average of the probabilistic model is then used as starting model for the linear inversion step, providing the final Vs model. The resulting S-wave velocity model and Moho depth are validated by comparison with previous geophysical studies. Although surface-wave tomography is weakly sensitive to layer boundaries, vertical cross-sections through our Vs model and the associated probability of presence of interfaces display striking similarities with reference controlled-source (CSS) and receiver-function sections across the Alpine belt. Our model even provides new structural information such as a ˜8 km Moho jump along the CSS ECORS-CROP profile that was not imaged by reflection data due to poor penetration across a heterogeneous upper crust. Our probabilistic and final shear wave velocity models have the potential to become new reference models of the European crust, both for crustal structure probing and geophysical studies including waveform modeling or full waveform inversion.

PCEMCAN - Probabilistic Ceramic Matrix Composites Analyzer: User's Guide, Version 1.0

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Mital, Subodh K.; Murthy, Pappu L. N.

1998-01-01

PCEMCAN (Probabalistic CEramic Matrix Composites ANalyzer) is an integrated computer code developed at NASA Lewis Research Center that simulates uncertainties associated with the constituent properties, manufacturing process, and geometric parameters of fiber reinforced ceramic matrix composites and quantifies their random thermomechanical behavior. The PCEMCAN code can perform the deterministic as well as probabilistic analyses to predict thermomechanical properties. This User's guide details the step-by-step procedure to create input file and update/modify the material properties database required to run PCEMCAN computer code. An overview of the geometric conventions, micromechanical unit cell, nonlinear constitutive relationship and probabilistic simulation methodology is also provided in the manual. Fast probability integration as well as Monte-Carlo simulation methods are available for the uncertainty simulation. Various options available in the code to simulate probabilistic material properties and quantify sensitivity of the primitive random variables have been described. The description of deterministic as well as probabilistic results have been described using demonstration problems. For detailed theoretical description of deterministic and probabilistic analyses, the user is referred to the companion documents "Computational Simulation of Continuous Fiber-Reinforced Ceramic Matrix Composite Behavior," NASA TP-3602, 1996 and "Probabilistic Micromechanics and Macromechanics for Ceramic Matrix Composites", NASA TM 4766, June 1997.

Ensemble reconstruction of severe low flow events in France since 1871

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

2016-04-01

This work presents a study of severe low flow events that occurred from 1871 onwards for a large number of near-natural catchments in France. It aims at assessing and comparing their characteristics to improve our knowledge on historical events and to provide a selection of benchmark events for climate change adaptation purposes. The historical depth of streamflow observations is generally limited to the last 50 years and therefore offers too small a sample of severe low flow events to properly explore the long-term evolution of their characteristics and associated impacts. In order to overcome this limit, this work takes advantage of a 140-year ensemble hydrometeorological dataset over France based on: (1) a probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France (Caillouet et al., 2015), and (2) a continuous hydrological modelling that uses the high-resolution meteorological reconstructions as forcings over the whole period. This dataset provides an ensemble of 25 equally plausible daily streamflow time series for a reference network of stations in France over the whole 1871-2012 period. Severe low flow events are identified based on a combination of a fixed threshold and a daily variable threshold. Each event is characterized by its deficit, duration and timing by applying the Sequent Peak Algorithm. The procedure is applied to the 25 simulated time series as well as to the observed time series in order to compare observed and simulated events over the recent period, and to characterize in a probabilistic way unrecorded historical events. The ensemble aspect of the reconstruction leads to address specific issues, for properly defining events across ensemble simulations, as well as for adequately comparing the simulated characteristics to the observed ones. This study brings forward the outstanding 1921 and 1940s events but also older and less known ones that occurred during the last decade of the 19th century. For the first time, severe low flow events are qualified in a homogeneous way over 140 years on a large set of near-natural French catchments, allowing for detailed analyses of the effect of climate variability and anthropogenic climate change on low flow hydrology. Caillouet, L., Vidal, J.-P., Sauquet, E., and Graff, B. (2015) Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France, Clim. Past Discuss., 11, 4425-4482, doi:10.5194/cpd-11-4425-2015

Demonstration of Advanced EMI Models for Live-Site UXO Discrimination at Waikoloa, Hawaii

DTIC Science & Technology

2015-12-01

magnetic source models PNN Probabilistic Neural Network SERDP Strategic Environmental Research and Development Program SLO San Luis Obispo...SNR Signal to noise ratio SVM Support vector machine TD Time Domain TEMTADS Time Domain Electromagnetic Towed Array Detection System TOI... intrusive procedure, which was used by Parsons at WMA, failed to document accurately all intrusive results, or failed to detect and clear all UXO like

Neo-Deterministic and Probabilistic Seismic Hazard Assessments: a Comparative Analysis

NASA Astrophysics Data System (ADS)

Peresan, Antonella; Magrin, Andrea; Nekrasova, Anastasia; Kossobokov, Vladimir; Panza, Giuliano F.

2016-04-01

Objective testing is the key issue towards any reliable seismic hazard assessment (SHA). Different earthquake hazard maps must demonstrate their capability in anticipating ground shaking from future strong earthquakes before an appropriate use for different purposes - such as engineering design, insurance, and emergency management. Quantitative assessment of maps performances is an essential step also in scientific process of their revision and possible improvement. Cross-checking of probabilistic models with available observations and independent physics based models is recognized as major validation procedure. The existing maps from the classical probabilistic seismic hazard analysis (PSHA), as well as those from the neo-deterministic analysis (NDSHA), which have been already developed for several regions worldwide (including Italy, India and North Africa), are considered to exemplify the possibilities of the cross-comparative analysis in spotting out limits and advantages of different methods. Where the data permit, a comparative analysis versus the documented seismic activity observed in reality is carried out, showing how available observations about past earthquakes can contribute to assess performances of the different methods. Neo-deterministic refers to a scenario-based approach, which allows for consideration of a wide range of possible earthquake sources as the starting point for scenarios constructed via full waveforms modeling. The method does not make use of empirical attenuation models (i.e. Ground Motion Prediction Equations, GMPE) and naturally supplies realistic time series of ground shaking (i.e. complete synthetic seismograms), readily applicable to complete engineering analysis and other mitigation actions. The standard NDSHA maps provide reliable envelope estimates of maximum seismic ground motion from a wide set of possible scenario earthquakes, including the largest deterministically or historically defined credible earthquake. In addition, the flexibility of NDSHA allows for generation of ground shaking maps at specified long-term return times, which may permit a straightforward comparison between NDSHA and PSHA maps in terms of average rates of exceedance for specified time windows. The comparison of NDSHA and PSHA maps, particularly for very long recurrence times, may indicate to what extent probabilistic ground shaking estimates are consistent with those from physical models of seismic waves propagation. A systematic comparison over the territory of Italy is carried out exploiting the uniqueness of the Italian earthquake catalogue, a data set covering more than a millennium (a time interval about ten times longer than that available in most of the regions worldwide) with a satisfactory completeness level for M>5, which warrants the results of analysis. By analysing in some detail seismicity in the Vrancea region, we show that well constrained macroseismic field information for individual earthquakes may provide useful information about the reliability of ground shaking estimates. Finally, in order to generalise observations, the comparative analysis is extended to further regions where both standard NDSHA and PSHA maps are available (e.g. State of Gujarat, India). The final Global Seismic Hazard Assessment Program (GSHAP) results and the most recent version of Seismic Hazard Harmonization in Europe (SHARE) project maps, along with other national scale probabilistic maps, all obtained by PSHA, are considered for this comparative analysis.

ORNL Pre-test Analyses of A Large-scale Experiment in STYLE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Paul T; Yin, Shengjun; Klasky, Hilda B

Oak Ridge National Laboratory (ORNL) is conducting a series of numerical analyses to simulate a large scale mock-up experiment planned within the European Network for Structural Integrity for Lifetime Management non-RPV Components (STYLE). STYLE is a European cooperative effort to assess the structural integrity of (non-reactor pressure vessel) reactor coolant pressure boundary components relevant to ageing and life-time management and to integrate the knowledge created in the project into mainstream nuclear industry assessment codes. ORNL contributes work-in-kind support to STYLE Work Package 2 (Numerical Analysis/Advanced Tools) and Work Package 3 (Engineering Assessment Methods/LBB Analyses). This paper summarizes the current statusmore » of ORNL analyses of the STYLE Mock-Up3 large-scale experiment to simulate and evaluate crack growth in a cladded ferritic pipe. The analyses are being performed in two parts. In the first part, advanced fracture mechanics models are being developed and performed to evaluate several experiment designs taking into account the capabilities of the test facility while satisfying the test objectives. Then these advanced fracture mechanics models will be utilized to simulate the crack growth in the large scale mock-up test. For the second part, the recently developed ORNL SIAM-PFM open-source, cross-platform, probabilistic computational tool will be used to generate an alternative assessment for comparison with the advanced fracture mechanics model results. The SIAM-PFM probabilistic analysis of the Mock-Up3 experiment will utilize fracture modules that are installed into a general probabilistic framework. The probabilistic results of the Mock-Up3 experiment obtained from SIAM-PFM will be compared to those results generated using the deterministic 3D nonlinear finite-element modeling approach. The objective of the probabilistic analysis is to provide uncertainty bounds that will assist in assessing the more detailed 3D finite-element solutions and to also assess the level of confidence that can be placed in the best-estimate finiteelement solutions.« less

Initial Probabilistic Evaluation of Reactor Pressure Vessel Fracture with Grizzly and Raven

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spencer, Benjamin; Hoffman, William; Sen, Sonat

2015-10-01

The Grizzly code is being developed with the goal of creating a general tool that can be applied to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled reactor pressure vessels (RPVs). Grizzly can be used to model the thermal/mechanical response of an RPV under transient conditions that would be observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtainmore » stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. These capabilities have been demonstrated previously. A typical RPV is likely to contain a large population of pre-existing flaws introduced during the manufacturing process. This flaw population is characterized stastistically through probability density functions of the flaw distributions. The use of probabilistic techniques is necessary to assess the likelihood of crack initiation during a transient event. This report documents initial work to perform probabilistic analysis of RPV fracture during a PTS event using a combination of the RAVEN risk analysis code and Grizzly. This work is limited in scope, considering only a single flaw with deterministic geometry, but with uncertainty introduced in the parameters that influence fracture toughness. These results are benchmarked against equivalent models run in the FAVOR code. When fully developed, the RAVEN/Grizzly methodology for modeling probabilistic fracture in RPVs will provide a general capability that can be used to consider a wider variety of vessel and flaw conditions that are difficult to consider with current tools. In addition, this will provide access to advanced probabilistic techniques provided by RAVEN, including adaptive sampling and parallelism, which can dramatically decrease run times.« less

Monitoring and modeling as a continuing learning process: the use of hydrological models in a general probabilistic framework.

NASA Astrophysics Data System (ADS)

Baroni, G.; Gräff, T.; Reinstorf, F.; Oswald, S. E.

2012-04-01

Nowadays uncertainty and sensitivity analysis are considered basic tools for the assessment of hydrological models and the evaluation of the most important sources of uncertainty. In this context, in the last decades several methods have been developed and applied in different hydrological conditions. However, in most of the cases, the studies have been done by investigating mainly the influence of the parameter uncertainty on the simulated outputs and few approaches tried to consider also other sources of uncertainty i.e. input and model structure. Moreover, several constrains arise when spatially distributed parameters are involved. To overcome these limitations a general probabilistic framework based on Monte Carlo simulations and the Sobol method has been proposed. In this study, the general probabilistic framework was applied at field scale using a 1D physical-based hydrological model (SWAP). Furthermore, the framework was extended at catchment scale in combination with a spatially distributed hydrological model (SHETRAN). The models are applied in two different experimental sites in Germany: a relatively flat cropped field close to Potsdam (Brandenburg) and a small mountainous catchment with agricultural land use (Schaefertal, Harz Mountains). For both cases, input and parameters are considered as major sources of uncertainty. Evaluation of the models was based on soil moisture detected at plot scale in different depths and, for the catchment site, also with daily discharge values. The study shows how the framework can take into account all the various sources of uncertainty i.e. input data, parameters (either in scalar or spatially distributed form) and model structures. The framework can be used in a loop in order to optimize further monitoring activities used to improve the performance of the model. In the particular applications, the results show how the sources of uncertainty are specific for each process considered. The influence of the input data as well as the presence of compensating errors become clear by the different processes simulated.

The sampled-data consensus of multi-agent systems with probabilistic time-varying delays and packet losses

NASA Astrophysics Data System (ADS)

Sui, Xin; Yang, Yongqing; Xu, Xianyun; Zhang, Shuai; Zhang, Lingzhong

2018-02-01

This paper investigates the consensus of multi-agent systems with probabilistic time-varying delays and packet losses via sampled-data control. On the one hand, a Bernoulli-distributed white sequence is employed to model random packet losses among agents. On the other hand, a switched system is used to describe packet dropouts in a deterministic way. Based on the special property of the Laplacian matrix, the consensus problem can be converted into a stabilization problem of a switched system with lower dimensions. Some mean square consensus criteria are derived in terms of constructing an appropriate Lyapunov function and using linear matrix inequalities (LMIs). Finally, two numerical examples are given to show the effectiveness of the proposed method.

Probabilistic risk assessment for a loss of coolant accident in McMaster Nuclear Reactor and application of reliability physics model for modeling human reliability

NASA Astrophysics Data System (ADS)

Ha, Taesung

A probabilistic risk assessment (PRA) was conducted for a loss of coolant accident, (LOCA) in the McMaster Nuclear Reactor (MNR). A level 1 PRA was completed including event sequence modeling, system modeling, and quantification. To support the quantification of the accident sequence identified, data analysis using the Bayesian method and human reliability analysis (HRA) using the accident sequence evaluation procedure (ASEP) approach were performed. Since human performance in research reactors is significantly different from that in power reactors, a time-oriented HRA model (reliability physics model) was applied for the human error probability (HEP) estimation of the core relocation. This model is based on two competing random variables: phenomenological time and performance time. The response surface and direct Monte Carlo simulation with Latin Hypercube sampling were applied for estimating the phenomenological time, whereas the performance time was obtained from interviews with operators. An appropriate probability distribution for the phenomenological time was assigned by statistical goodness-of-fit tests. The human error probability (HEP) for the core relocation was estimated from these two competing quantities: phenomenological time and operators' performance time. The sensitivity of each probability distribution in human reliability estimation was investigated. In order to quantify the uncertainty in the predicted HEPs, a Bayesian approach was selected due to its capability of incorporating uncertainties in model itself and the parameters in that model. The HEP from the current time-oriented model was compared with that from the ASEP approach. Both results were used to evaluate the sensitivity of alternative huinan reliability modeling for the manual core relocation in the LOCA risk model. This exercise demonstrated the applicability of a reliability physics model supplemented with a. Bayesian approach for modeling human reliability and its potential usefulness of quantifying model uncertainty as sensitivity analysis in the PRA model.

A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

2010-01-01

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Characterizing Topology of Probabilistic Biological Networks.

PubMed

Todor, Andrei; Dobra, Alin; Kahveci, Tamer

2013-09-06

Biological interactions are often uncertain events, that may or may not take place with some probability. Existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. Here, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. We develop a method that accurately describes the degree distribution of such networks. We also extend our method to accurately compute the joint degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. It also helps us find an adequate mathematical model using maximum likelihood estimation. Our results demonstrate that power law and log-normal models best describe degree distributions for probabilistic networks. The inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected.

Fuzzy-probabilistic model for risk assessment of radioactive material railway transportation.

PubMed

Avramenko, M; Bolyatko, V; Kosterev, V

2005-01-01

Transportation of radioactive materials is obviously accompanied by a certain risk. A model for risk assessment of emergency situations and terrorist attacks may be useful for choosing possible routes and for comparing the various defence strategies. In particular, risk assessment is crucial for safe transportation of excess weapons-grade plutonium arising from the removal of plutonium from military employment. A fuzzy-probabilistic model for risk assessment of railway transportation has been developed taking into account the different natures of risk-affecting parameters (probabilistic and not probabilistic but fuzzy). Fuzzy set theory methods as well as standard methods of probability theory have been used for quantitative risk assessment. Information-preserving transformations are applied to realise the correct aggregation of probabilistic and fuzzy parameters. Estimations have also been made of the inhalation doses resulting from possible accidents during plutonium transportation. The obtained data show the scale of possible consequences that may arise from plutonium transportation accidents.

Fine-Scale Event Location and Error Analysis in NET-VISA

NASA Astrophysics Data System (ADS)

Arora, N. S.; Russell, S.

2016-12-01

NET-VISA is a generative probabilistic model for the occurrence of seismic, hydro, and atmospheric events, and the propagation of energy from these events through various mediums and phases before being detected, or misdetected, by IMS stations. It is built on top of the basic station, and arrival detection processing at the IDC, and is currently being tested in the IDC network processing pipelines. A key distinguishing feature of NET-VISA is that it is easy to incorporate prior scientific knowledge and historical data into the probabilistic model. The model accounts for both detections and mis-detections when forming events, and this allows it to make more accurate event hypothesis. It has been continuously evaluated since 2012, and in each year it makes a roughly 60% reduction in the number of missed events without increasing the false event rate as compared to the existing GA algorithm. More importantly the model finds large numbers of events that have been confirmed by regional seismic bulletins but missed by the IDC analysts using the same data. In this work we focus on enhancements to the model to improve the location accuracy, and error ellipses. We will present a new version of the model that focuses on the fine scale around the event location, and present error ellipses and analysis of recent important events.

What do we gain with Probabilistic Flood Loss Models?

NASA Astrophysics Data System (ADS)

Schroeter, K.; Kreibich, H.; Vogel, K.; Merz, B.; Lüdtke, S.

2015-12-01

The reliability of flood loss models is a prerequisite for their practical usefulness. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions which are cast in a probabilistic framework. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Spatial search on a two-dimensional lattice with long-range interactions

NASA Astrophysics Data System (ADS)

Osada, Tomo; Sanaka, Kaoru; Munro, William J.; Nemoto, Kae

2018-06-01

Quantum-walk-based algorithms that search a marked location among N locations on a d -dimensional lattice succeeds in time O (√{N }) for d >2 , while this is not found to be possible when d =2 . In this paper, we consider a spatial search algorithm using continuous-time quantum walk on a two-dimensional square lattice with the existence of additional long-range edges. We examined such a search on a probabilistic graph model where an edge connecting non-nearest-neighbor lattice points i and j apart by a distance |i -j | is added by probability pi j=|i-j | -α(α ≥0 ) . Through numerical analysis, we found that the search succeeds in time O (√{N }) when α ≤αc=2.4 ±0.1 . For α >2 , the expectation value of the additional long-range edges on each node scales as a constant when N →∞ , which means that search time of O (√{N }) is achieved on a graph with average degree scaling as a constant.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Evidence synthesis for medical decision making and the appropriate use of quality scores.

PubMed

Doi, Suhail A R

2014-09-01

Meta-analyses today continue to be run using conventional random-effects models that ignore tangible information from studies such as the quality of the studies involved, despite the expectation that results of better quality studies reflect more valid results. Previous research has suggested that quality scores derived from such quality appraisals are unlikely to be useful in meta-analysis, because they would produce biased estimates of effects that are unlikely to be offset by a variance reduction within the studied models. However, previous discussions took place in the context of such scores viewed in terms of their ability to maximize their association with both the magnitude and direction of bias. In this review, another look is taken at this concept, this time asserting that probabilistic bias quantification is not possible or even required of quality scores when used in meta-analysis for redistribution of weights. The use of such a model is contrasted with the conventional random effects model of meta-analysis to demonstrate why the latter is inadequate in the face of a properly specified quality score weighting method. © 2014 Marshfield Clinic.

An Analytic Form for the Interresponse Time Analysis of Shull, Gaynor, and Grimes with Applications and Extensions

ERIC Educational Resources Information Center

Kessel, Robert; Lucke, Robert L.

2008-01-01

Shull, Gaynor and Grimes advanced a model for interresponse time distribution using probabilistic cycling between a higher-rate and a lower-rate response process. Both response processes are assumed to be random in time with a constant rate. The cycling between the two processes is assumed to have a constant transition probability that is…

Probabilistic Model and Analysis of Conventional Preinstalled Mine Field Defense.

DTIC Science & Technology

1980-09-01

process to model the one or two positions of mines in the mine field. The duel between the anti-tank weapon and offensive tanks crossing the field is...mine field. The duel between the anti-tank weapon and offensive tanks crossing the field is modeled with a con- tinuous time Markov chain. Some...11 B. DUEL ------------------------------------------- 15 IV. DUEL

Hybrid Packet-Pheromone-Based Probabilistic Routing for Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Kashkouli Nejad, Keyvan; Shawish, Ahmed; Jiang, Xiaohong; Horiguchi, Susumu

Ad-Hoc networks are collections of mobile nodes communicating using wireless media without any fixed infrastructure. Minimal configuration and quick deployment make Ad-Hoc networks suitable for emergency situations like natural disasters or military conflicts. The current Ad-Hoc networks can only support either high mobility or high transmission rate at a time because they employ static approaches in their routing schemes. However, due to the continuous expansion of the Ad-Hoc network size, node-mobility and transmission rate, the development of new adaptive and dynamic routing schemes has become crucial. In this paper we propose a new routing scheme to support high transmission rates and high node-mobility simultaneously in a big Ad-Hoc network, by combining a new proposed packet-pheromone-based approach with the Hint Based Probabilistic Protocol (HBPP) for congestion avoidance with dynamic path selection in packet forwarding process. Because of using the available feedback information, the proposed algorithm does not introduce any additional overhead. The extensive simulation-based analysis conducted in this paper indicates that the proposed algorithm offers small packet-latency and achieves a significantly higher delivery probability in comparison with the available Hint-Based Probabilistic Protocol (HBPP).

Modeling marine oily wastewater treatment by a probabilistic agent-based approach.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Ye, Xudong

2018-02-01

This study developed a novel probabilistic agent-based approach for modeling of marine oily wastewater treatment processes. It begins first by constructing a probability-based agent simulation model, followed by a global sensitivity analysis and a genetic algorithm-based calibration. The proposed modeling approach was tested through a case study of the removal of naphthalene from marine oily wastewater using UV irradiation. The removal of naphthalene was described by an agent-based simulation model using 8 types of agents and 11 reactions. Each reaction was governed by a probability parameter to determine its occurrence. The modeling results showed that the root mean square errors between modeled and observed removal rates were 8.73 and 11.03% for calibration and validation runs, respectively. Reaction competition was analyzed by comparing agent-based reaction probabilities, while agents' heterogeneity was visualized by plotting their real-time spatial distribution, showing a strong potential for reactor design and process optimization. Copyright © 2017 Elsevier Ltd. All rights reserved.

A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge.

PubMed

Liu, Weiru; Yue, Anbu; Timson, David J

2010-04-01

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool. 2009 Elsevier Ireland Ltd. All rights reserved.

Probabilistic stability analysis: the way forward for stability analysis of sustainable power systems.

PubMed

Milanović, Jovica V

2017-08-13

Future power systems will be significantly different compared with their present states. They will be characterized by an unprecedented mix of a wide range of electricity generation and transmission technologies, as well as responsive and highly flexible demand and storage devices with significant temporal and spatial uncertainty. The importance of probabilistic approaches towards power system stability analysis, as a subsection of power system studies routinely carried out by power system operators, has been highlighted in previous research. However, it may not be feasible (or even possible) to accurately model all of the uncertainties that exist within a power system. This paper describes for the first time an integral approach to probabilistic stability analysis of power systems, including small and large angular stability and frequency stability. It provides guidance for handling uncertainties in power system stability studies and some illustrative examples of the most recent results of probabilistic stability analysis of uncertain power systems.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

2018 one‐year seismic hazard forecast for the central and eastern United States from induced and natural earthquakes

USGS Publications Warehouse

Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Rukstales, Kenneth S.; McNamara, Daniel E.; Williams, Robert A.; Shumway, Allison; Powers, Peter; Earle, Paul; Llenos, Andrea L.; Michael, Andrew J.; Rubinstein, Justin L.; Norbeck, Jack; Cochran, Elizabeth S.

2018-01-01

This article describes the U.S. Geological Survey (USGS) 2018 one‐year probabilistic seismic hazard forecast for the central and eastern United States from induced and natural earthquakes. For consistency, the updated 2018 forecast is developed using the same probabilistic seismicity‐based methodology as applied in the two previous forecasts. Rates of earthquakes across the United States M≥3.0">M≥3.0 grew rapidly between 2008 and 2015 but have steadily declined over the past 3 years, especially in areas of Oklahoma and southern Kansas where fluid injection has decreased. The seismicity pattern in 2017 was complex with earthquakes more spatially dispersed than in the previous years. Some areas of west‐central Oklahoma experienced increased activity rates where industrial activity increased. Earthquake rates in Oklahoma (429 earthquakes of M≥3">M≥3 and 4 M≥4">M≥4), Raton basin (Colorado/New Mexico border, six earthquakes M≥3">M≥3), and the New Madrid seismic zone (11 earthquakes M≥3">M≥3) continue to be higher than historical levels. Almost all of these earthquakes occurred within the highest hazard regions of the 2017 forecast. Even though rates declined over the past 3 years, the short‐term hazard for damaging ground shaking across much of Oklahoma remains at high levels due to continuing high rates of smaller earthquakes that are still hundreds of times higher than at any time in the state’s history. Fine details and variability between the 2016–2018 forecasts are obscured by significant uncertainties in the input model. These short‐term hazard levels are similar to active regions in California. During 2017, M≥3">M≥3 earthquakes also occurred in or near Ohio, West Virginia, Missouri, Kentucky, Tennessee, Arkansas, Illinois, Oklahoma, Kansas, Colorado, New Mexico, Utah, and Wyoming.

The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability

PubMed Central

Reich, Steven

2014-01-01

Neuronal variability plays a central role in neural coding and impacts the dynamics of neuronal networks. Unreliability of synaptic transmission is a major source of neural variability: synaptic neurotransmitter vesicles are released probabilistically in response to presynaptic action potentials and are recovered stochastically in time. The dynamics of this process of vesicle release and recovery interacts with variability in the arrival times of presynaptic spikes to shape the variability of the postsynaptic response. We use continuous time Markov chain methods to analyze a model of short term synaptic depression with stochastic vesicle dynamics coupled with three different models of presynaptic spiking: one model in which the timing of presynaptic action potentials are modeled as a Poisson process, one in which action potentials occur more regularly than a Poisson process (sub-Poisson) and one in which action potentials occur more irregularly (super-Poisson). We use this analysis to investigate how variability in a presynaptic spike train is transformed by short term depression and stochastic vesicle dynamics to determine the variability of the postsynaptic response. We find that sub-Poisson presynaptic spiking increases the average rate at which vesicles are released, that the number of vesicles released over a time window is more variable for smaller time windows than larger time windows and that fast presynaptic spiking gives rise to Poisson-like variability of the postsynaptic response even when presynaptic spike times are non-Poisson. Our results complement and extend previously reported theoretical results and provide possible explanations for some trends observed in recorded data. PMID:23354693

The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color

PubMed Central

Austerweil, Joseph L.; Griffiths, Thomas L.; Regier, Terry

2016-01-01

The Sapir-Whorf hypothesis holds that our thoughts are shaped by our native language, and that speakers of different languages therefore think differently. This hypothesis is controversial in part because it appears to deny the possibility of a universal groundwork for human cognition, and in part because some findings taken to support it have not reliably replicated. We argue that considering this hypothesis through the lens of probabilistic inference has the potential to resolve both issues, at least with respect to certain prominent findings in the domain of color cognition. We explore a probabilistic model that is grounded in a presumed universal perceptual color space and in language-specific categories over that space. The model predicts that categories will most clearly affect color memory when perceptual information is uncertain. In line with earlier studies, we show that this model accounts for language-consistent biases in color reconstruction from memory in English speakers, modulated by uncertainty. We also show, to our knowledge for the first time, that such a model accounts for influential existing data on cross-language differences in color discrimination from memory, both within and across categories. We suggest that these ideas may help to clarify the debate over the Sapir-Whorf hypothesis. PMID:27434643

Assessment of uncertainty in discrete fracture network modeling using probabilistic distribution method.

PubMed

Wei, Yaqiang; Dong, Yanhui; Yeh, Tian-Chyi J; Li, Xiao; Wang, Liheng; Zha, Yuanyuan

2017-11-01

There have been widespread concerns about solute transport problems in fractured media, e.g. the disposal of high-level radioactive waste in geological fractured rocks. Numerical simulation of particle tracking is gradually being employed to address these issues. Traditional predictions of radioactive waste transport using discrete fracture network (DFN) models often consider one particular realization of the fracture distribution based on fracture statistic features. This significantly underestimates the uncertainty of the risk of radioactive waste deposit evaluation. To adequately assess the uncertainty during the DFN modeling in a potential site for the disposal of high-level radioactive waste, this paper utilized the probabilistic distribution method (PDM). The method was applied to evaluate the risk of nuclear waste deposit in Beishan, China. Moreover, the impact of the number of realizations on the simulation results was analyzed. In particular, the differences between the modeling results of one realization and multiple realizations were demonstrated. Probabilistic distributions of 20 realizations at different times were also obtained. The results showed that the employed PDM can be used to describe the ranges of the contaminant particle transport. The high-possibility contaminated areas near the release point were more concentrated than the farther areas after 5E6 days, which was 25,400 m 2 .

Landslide Hazard from Coupled Inherent and Dynamic Probabilities

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.; Nudurupati, S. S.

2015-12-01

Landslide hazard research has typically been conducted independently from hydroclimate research. We sought to unify these two lines of research to provide regional scale landslide hazard information for risk assessments and resource management decision-making. Our approach couples an empirical inherent landslide probability, based on a frequency ratio analysis, with a numerical dynamic probability, generated by combining subsurface water recharge and surface runoff from the Variable Infiltration Capacity (VIC) macro-scale land surface hydrologic model with a finer resolution probabilistic slope stability model. Landslide hazard mapping is advanced by combining static and dynamic models of stability into a probabilistic measure of geohazard prediction in both space and time. This work will aid resource management decision-making in current and future landscape and climatic conditions. The approach is applied as a case study in North Cascade National Park Complex in northern Washington State.

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

PubMed

Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael

2014-09-06

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

Bayesian Probability Theory

NASA Astrophysics Data System (ADS)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

High dynamic range infrared radiometry and imaging

NASA Technical Reports Server (NTRS)

Coon, Darryl D.; Karunasiri, R. P. G.; Bandara, K. M. S. V.

1988-01-01

The use is described of cryogenically cooled, extrinsic silicon infrared detectors in an unconventional mode of operation which offers an unusually large dynamic range. The system performs intensity-to-frequency conversion at the focal plane via simple circuits with very low power consumption. The incident IR intensity controls the repetition rate of short duration output pulses over a pulse rate dynamic range of about 10(6). Theory indicates the possibility of monotonic and approx. linear response over the full dynamic range. A comparison between the theoretical and the experimental results shows that the model provides a reasonably good description of experimental data. Some measurements of survivability with a very intense IR source were made on these devices and found to be very encouraging. Evidence continues to indicate that some variations in interpulse time intervals are deterministic rather than probabilistic.

Markov Chain Model with Catastrophe to Determine Mean Time to Default of Credit Risky Assets

NASA Astrophysics Data System (ADS)

Dharmaraja, Selvamuthu; Pasricha, Puneet; Tardelli, Paola

2017-11-01

This article deals with the problem of probabilistic prediction of the time distance to default for a firm. To model the credit risk, the dynamics of an asset is described as a function of a homogeneous discrete time Markov chain subject to a catastrophe, the default. The behaviour of the Markov chain is investigated and the mean time to the default is expressed in a closed form. The methodology to estimate the parameters is given. Numerical results are provided to illustrate the applicability of the proposed model on real data and their analysis is discussed.

Capabilities of stochastic rainfall models as data providers for urban hydrology

NASA Astrophysics Data System (ADS)

Haberlandt, Uwe

2017-04-01

For planning of urban drainage systems using hydrological models, long, continuous precipitation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative. This contribution compares three precipitation models regarding their suitability to provide 5 minute continuous rainfall time series for a) sizing of drainage networks for urban flood protection and b) dimensioning of combined sewage systems for pollution reduction. The rainfall models are a parametric stochastic model (Haberlandt et al., 2008), a non-parametric probabilistic approach (Bárdossy, 1998) and a stochastic downscaling of dynamically simulated rainfall (Berg et al., 2013); all models are operated both as single site and multi-site generators. The models are applied with regionalised parameters assuming that there is no station at the target location. Rainfall and discharge characteristics are utilised for evaluation of the model performance. The simulation results are compared against results obtained from reference rainfall stations not used for parameter estimation. The rainfall simulations are carried out for the federal states of Baden-Württemberg and Lower Saxony in Germany and the discharge simulations for the drainage networks of the cities of Hamburg, Brunswick and Freiburg. Altogether, the results show comparable simulation performance for the three models, good capabilities for single site simulations but low skills for multi-site simulations. Remarkably, there is no significant difference in simulation performance comparing the tasks flood protection with pollution reduction, so the models are finally able to simulate both the extremes and the long term characteristics of rainfall equally well. Bárdossy, A., 1998. Generating precipitation time series using simulated annealing. Wat. Resour. Res., 34(7): 1737-1744. Berg, P., Wagner, S., Kunstmann, H., Schädler, G., 2013. High resolution regional climate model simulations for Germany: part I — validation. Climate Dynamics, 40(1): 401-414. Haberlandt, U., Ebner von Eschenbach, A.-D., Buchwald, I., 2008. A space-time hybrid hourly rainfall model for derived flood frequency analysis. Hydrol. Earth Syst. Sci., 12: 1353-1367.

A Simple Probabilistic Model for Estimating the Risk of Standard Air Dives

DTIC Science & Technology

2004-12-01

Decompression Models Table Al. Decompression Table Based on the StandAir Model and Comparison with the VVaI-1 8 Algorithm. A-l-A-4 Table A2. The VVaI-1 8...cannot be as strong as might be desired - especially for dives with long TDTs. Comparisons of the positions of the dive-outcome symbols with the... comparisons for several depth/bottom-time combinations. The three left-hand panels, for dives with short bottom times, show that the crossover point

Probabilistic models of cognition: conceptual foundations.

PubMed

Chater, Nick; Tenenbaum, Joshua B; Yuille, Alan

2006-07-01

Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, 'sophisticated' probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today.

Development of Probabilistic Flood Inundation Mapping For Flooding Induced by Dam Failure

NASA Astrophysics Data System (ADS)

Tsai, C.; Yeh, J. J. J.

2017-12-01

A primary function of flood inundation mapping is to forecast flood hazards and assess potential losses. However, uncertainties limit the reliability of inundation hazard assessments. Major sources of uncertainty should be taken into consideration by an optimal flood management strategy. This study focuses on the 20km reach downstream of the Shihmen Reservoir in Taiwan. A dam failure induced flood herein provides the upstream boundary conditions of flood routing. The two major sources of uncertainty that are considered in the hydraulic model and the flood inundation mapping herein are uncertainties in the dam break model and uncertainty of the roughness coefficient. The perturbance moment method is applied to a dam break model and the hydro system model to develop probabilistic flood inundation mapping. Various numbers of uncertain variables can be considered in these models and the variability of outputs can be quantified. The probabilistic flood inundation mapping for dam break induced floods can be developed with consideration of the variability of output using a commonly used HEC-RAS model. Different probabilistic flood inundation mappings are discussed and compared. Probabilistic flood inundation mappings are hoped to provide new physical insights in support of the evaluation of concerning reservoir flooded areas.

Design and analysis of DNA strand displacement devices using probabilistic model checking

PubMed Central

Lakin, Matthew R.; Parker, David; Cardelli, Luca; Kwiatkowska, Marta; Phillips, Andrew

2012-01-01

Designing correct, robust DNA devices is difficult because of the many possibilities for unwanted interference between molecules in the system. DNA strand displacement has been proposed as a design paradigm for DNA devices, and the DNA strand displacement (DSD) programming language has been developed as a means of formally programming and analysing these devices to check for unwanted interference. We demonstrate, for the first time, the use of probabilistic verification techniques to analyse the correctness, reliability and performance of DNA devices during the design phase. We use the probabilistic model checker prism, in combination with the DSD language, to design and debug DNA strand displacement components and to investigate their kinetics. We show how our techniques can be used to identify design flaws and to evaluate the merits of contrasting design decisions, even on devices comprising relatively few inputs. We then demonstrate the use of these components to construct a DNA strand displacement device for approximate majority voting. Finally, we discuss some of the challenges and possible directions for applying these methods to more complex designs. PMID:22219398

Stochastic simulation by image quilting of process-based geological models

NASA Astrophysics Data System (ADS)

Hoffimann, Júlio; Scheidt, Céline; Barfod, Adrian; Caers, Jef

2017-09-01

Process-based modeling offers a way to represent realistic geological heterogeneity in subsurface models. The main limitation lies in conditioning such models to data. Multiple-point geostatistics can use these process-based models as training images and address the data conditioning problem. In this work, we further develop image quilting as a method for 3D stochastic simulation capable of mimicking the realism of process-based geological models with minimal modeling effort (i.e. parameter tuning) and at the same time condition them to a variety of data. In particular, we develop a new probabilistic data aggregation method for image quilting that bypasses traditional ad-hoc weighting of auxiliary variables. In addition, we propose a novel criterion for template design in image quilting that generalizes the entropy plot for continuous training images. The criterion is based on the new concept of voxel reuse-a stochastic and quilting-aware function of the training image. We compare our proposed method with other established simulation methods on a set of process-based training images of varying complexity, including a real-case example of stochastic simulation of the buried-valley groundwater system in Denmark.

Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model.

PubMed

Martin, Sébastien; Troccaz, Jocelyne; Daanenc, Vincent

2010-04-01

The authors present a fully automatic algorithm for the segmentation of the prostate in three-dimensional magnetic resonance (MR) images. The approach requires the use of an anatomical atlas which is built by computing transformation fields mapping a set of manually segmented images to a common reference. These transformation fields are then applied to the manually segmented structures of the training set in order to get a probabilistic map on the atlas. The segmentation is then realized through a two stage procedure. In the first stage, the processed image is registered to the probabilistic atlas. Subsequently, a probabilistic segmentation is obtained by mapping the probabilistic map of the atlas to the patient's anatomy. In the second stage, a deformable surface evolves toward the prostate boundaries by merging information coming from the probabilistic segmentation, an image feature model and a statistical shape model. During the evolution of the surface, the probabilistic segmentation allows the introduction of a spatial constraint that prevents the deformable surface from leaking in an unlikely configuration. The proposed method is evaluated on 36 exams that were manually segmented by a single expert. A median Dice similarity coefficient of 0.86 and an average surface error of 2.41 mm are achieved. By merging prior knowledge, the presented method achieves a robust and completely automatic segmentation of the prostate in MR images. Results show that the use of a spatial constraint is useful to increase the robustness of the deformable model comparatively to a deformable surface that is only driven by an image appearance model.

Probabilistic load simulation: Code development status

NASA Astrophysics Data System (ADS)

Newell, J. F.; Ho, H.

1991-05-01

The objective of the Composite Load Spectra (CLS) project is to develop generic load models to simulate the composite load spectra that are included in space propulsion system components. The probabilistic loads thus generated are part of the probabilistic design analysis (PDA) of a space propulsion system that also includes probabilistic structural analyses, reliability, and risk evaluations. Probabilistic load simulation for space propulsion systems demands sophisticated probabilistic methodology and requires large amounts of load information and engineering data. The CLS approach is to implement a knowledge based system coupled with a probabilistic load simulation module. The knowledge base manages and furnishes load information and expertise and sets up the simulation runs. The load simulation module performs the numerical computation to generate the probabilistic loads with load information supplied from the CLS knowledge base.

Probabilistic choice between symmetric disparities in motion stereo matching for a lateral navigation system

NASA Astrophysics Data System (ADS)

Ershov, Egor; Karnaukhov, Victor; Mozerov, Mikhail

2016-02-01

Two consecutive frames of a lateral navigation camera video sequence can be considered as an appropriate approximation to epipolar stereo. To overcome edge-aware inaccuracy caused by occlusion, we propose a model that matches the current frame to the next and to the previous ones. The positive disparity of matching to the previous frame has its symmetric negative disparity to the next frame. The proposed algorithm performs probabilistic choice for each matched pixel between the positive disparity and its symmetric disparity cost. A disparity map obtained by optimization over the cost volume composed of the proposed probabilistic choice is more accurate than the traditional left-to-right and right-to-left disparity maps cross-check. Also, our algorithm needs two times less computational operations per pixel than the cross-check technique. The effectiveness of our approach is demonstrated on synthetic data and real video sequences, with ground-truth value.

Development and Validation of a New Air Carrier Block Time Prediction Model and Methodology

NASA Astrophysics Data System (ADS)

Litvay, Robyn Olson

Commercial airline operations rely on predicted block times as the foundation for critical, successive decisions that include fuel purchasing, crew scheduling, and airport facility usage planning. Small inaccuracies in the predicted block times have the potential to result in huge financial losses, and, with profit margins for airline operations currently almost nonexistent, potentially negate any possible profit. Although optimization techniques have resulted in many models targeting airline operations, the challenge of accurately predicting and quantifying variables months in advance remains elusive. The objective of this work is the development of an airline block time prediction model and methodology that is practical, easily implemented, and easily updated. Research was accomplished, and actual U.S., domestic, flight data from a major airline was utilized, to develop a model to predict airline block times with increased accuracy and smaller variance in the actual times from the predicted times. This reduction in variance represents tens of millions of dollars (U.S.) per year in operational cost savings for an individual airline. A new methodology for block time prediction is constructed using a regression model as the base, as it has both deterministic and probabilistic components, and historic block time distributions. The estimation of the block times for commercial, domestic, airline operations requires a probabilistic, general model that can be easily customized for a specific airline’s network. As individual block times vary by season, by day, and by time of day, the challenge is to make general, long-term estimations representing the average, actual block times while minimizing the variation. Predictions of block times for the third quarter months of July and August of 2011 were calculated using this new model. The resulting, actual block times were obtained from the Research and Innovative Technology Administration, Bureau of Transportation Statistics (Airline On-time Performance Data, 2008-2011) for comparison and analysis. Future block times are shown to be predicted with greater accuracy, without exception and network-wide, for a major, U.S., domestic airline.

Application of a stochastic snowmelt model for probabilistic decisionmaking

NASA Technical Reports Server (NTRS)

Mccuen, R. H.

1983-01-01

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients.

Probabilistic tsunami hazard analysis: Multiple sources and global applications

USGS Publications Warehouse

Grezio, Anita; Babeyko, Andrey; Baptista, Maria Ana; Behrens, Jörn; Costa, Antonio; Davies, Gareth; Geist, Eric L.; Glimsdal, Sylfest; González, Frank I.; Griffin, Jonathan; Harbitz, Carl B.; LeVeque, Randall J.; Lorito, Stefano; Løvholt, Finn; Omira, Rachid; Mueller, Christof; Paris, Raphaël; Parsons, Thomas E.; Polet, Jascha; Power, William; Selva, Jacopo; Sørensen, Mathilde B.; Thio, Hong Kie

2017-01-01

Applying probabilistic methods to infrequent but devastating natural events is intrinsically challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated because of the different causes generating tsunamis (earthquakes, landslides, volcanic activity, meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami Hazard Analyses (PTHAs) are conducted in different areas of the world at global, regional, and local scales with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance knowledge of the potential tsunamigenic threat by estimating the probability of exceeding specific levels of tsunami intensity metrics (e.g., run-up or maximum inundation heights) within a certain period of time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii) statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the rapid development of PTHA methods during the last decade and the globally distributed applications, including the importance of considering multiple sources, their relative intensities, probabilities of occurrence, and uncertainties in an integrated and consistent probabilistic framework.

Probabilistic Tsunami Hazard Analysis: Multiple Sources and Global Applications

NASA Astrophysics Data System (ADS)

Grezio, Anita; Babeyko, Andrey; Baptista, Maria Ana; Behrens, Jörn; Costa, Antonio; Davies, Gareth; Geist, Eric L.; Glimsdal, Sylfest; González, Frank I.; Griffin, Jonathan; Harbitz, Carl B.; LeVeque, Randall J.; Lorito, Stefano; Løvholt, Finn; Omira, Rachid; Mueller, Christof; Paris, Raphaël.; Parsons, Tom; Polet, Jascha; Power, William; Selva, Jacopo; Sørensen, Mathilde B.; Thio, Hong Kie

2017-12-01

Applying probabilistic methods to infrequent but devastating natural events is intrinsically challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated because of the different causes generating tsunamis (earthquakes, landslides, volcanic activity, meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami Hazard Analyses (PTHAs) are conducted in different areas of the world at global, regional, and local scales with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance knowledge of the potential tsunamigenic threat by estimating the probability of exceeding specific levels of tsunami intensity metrics (e.g., run-up or maximum inundation heights) within a certain period of time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii) statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the rapid development of PTHA methods during the last decade and the globally distributed applications, including the importance of considering multiple sources, their relative intensities, probabilities of occurrence, and uncertainties in an integrated and consistent probabilistic framework.

Derivation of Failure Rates and Probability of Failures for the International Space Station Probabilistic Risk Assessment Study

NASA Technical Reports Server (NTRS)

Vitali, Roberto; Lutomski, Michael G.

2004-01-01

National Aeronautics and Space Administration s (NASA) International Space Station (ISS) Program uses Probabilistic Risk Assessment (PRA) as part of its Continuous Risk Management Process. It is used as a decision and management support tool to not only quantify risk for specific conditions, but more importantly comparing different operational and management options to determine the lowest risk option and provide rationale for management decisions. This paper presents the derivation of the probability distributions used to quantify the failure rates and the probability of failures of the basic events employed in the PRA model of the ISS. The paper will show how a Bayesian approach was used with different sources of data including the actual ISS on orbit failures to enhance the confidence in results of the PRA. As time progresses and more meaningful data is gathered from on orbit failures, an increasingly accurate failure rate probability distribution for the basic events of the ISS PRA model can be obtained. The ISS PRA has been developed by mapping the ISS critical systems such as propulsion, thermal control, or power generation into event sequences diagrams and fault trees. The lowest level of indenture of the fault trees was the orbital replacement units (ORU). The ORU level was chosen consistently with the level of statistically meaningful data that could be obtained from the aerospace industry and from the experts in the field. For example, data was gathered for the solenoid valves present in the propulsion system of the ISS. However valves themselves are composed of parts and the individual failure of these parts was not accounted for in the PRA model. In other words the failure of a spring within a valve was considered a failure of the valve itself.

Developments of the European Flood Awareness System (EFAS)

NASA Astrophysics Data System (ADS)

Thiemig, Vera; Olav Skøien, Jon; Salamon, Peter; Pappenberger, Florian; Wetterhall, Fredrik; Holst, Bo; Asp, Sara-Sophia; Garcia Padilla, Mercedes; Garcia, Rafael J.; Schweim, Christoph; Ziese, Markus

2017-04-01

EFAS (http://www.efas.eu) is an operational system for flood forecasting and early warning for the entire Europe, which is fully operational as part of the Copernicus Emergency Management Service since 2012. The prime aim of EFAS is to gain time for preparedness measures before major flood events - particularly in trans-national river basins - strike. This is achieved by providing complementary, added value information to the national and regional services holding the mandate for flood warning as well as to the ERCC (European Response and Coordination Centre). Using a coherent model for all of Europe forced with a range of deterministic and ensemble weather forecasts, the system can give a probabilistic flood forecast for a medium range lead time (up to 10 days) independent of country borders. The system is under continuous development, and we will present the basic set up, some prominent examples of recent and ongoing developments (such as the rapid impact assessment, seasonal outlook and the extended domain) and the future challenges.

Accelerating slip rates on the puente hills blind thrust fault system beneath metropolitan Los Angeles, California, USA

USGS Publications Warehouse

Bergen, Kristian J.; Shaw, John H.; Leon, Lorraine A.; Dolan, James F.; Pratt, Thomas L.; Ponti, Daniel J.; Morrow, Eric; Barrera, Wendy; Rhodes, Edward J.; Murari, Madhav K.; Owen, Lewis A.

2017-01-01

Slip rates represent the average displacement across a fault over time and are essential to estimating earthquake recurrence for proba-bilistic seismic hazard assessments. We demonstrate that the slip rate on the western segment of the Puente Hills blind thrust fault system, which is beneath downtown Los Angeles, California (USA), has accel-erated from ~0.22 mm/yr in the late Pleistocene to ~1.33 mm/yr in the Holocene. Our analysis is based on syntectonic strata derived from the Los Angeles River, which has continuously buried a fold scarp above the blind thrust. Slip on the fault beneath our field site began during the late-middle Pleistocene and progressively increased into the Holocene. This increase in rate implies that the magnitudes and/or the frequency of earthquakes on this fault segment have increased over time. This challenges the characteristic earthquake model and presents an evolving and potentially increasing seismic hazard to metropolitan Los Angeles.

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks.

PubMed

Han, Guangjie; Li, Shanshan; Zhu, Chunsheng; Jiang, Jinfang; Zhang, Wenbo

2017-02-08

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.

Probabilistic tsunami hazard assessment at Seaside, Oregon, for near-and far-field seismic sources

USGS Publications Warehouse

Gonzalez, F.I.; Geist, E.L.; Jaffe, B.; Kanoglu, U.; Mofjeld, H.; Synolakis, C.E.; Titov, V.V.; Areas, D.; Bellomo, D.; Carlton, D.; Horning, T.; Johnson, J.; Newman, J.; Parsons, T.; Peters, R.; Peterson, C.; Priest, G.; Venturato, A.; Weber, J.; Wong, F.; Yalciner, A.

2009-01-01

The first probabilistic tsunami flooding maps have been developed. The methodology, called probabilistic tsunami hazard assessment (PTHA), integrates tsunami inundation modeling with methods of probabilistic seismic hazard assessment (PSHA). Application of the methodology to Seaside, Oregon, has yielded estimates of the spatial distribution of 100- and 500-year maximum tsunami amplitudes, i.e., amplitudes with 1% and 0.2% annual probability of exceedance. The 100-year tsunami is generated most frequently by far-field sources in the Alaska-Aleutian Subduction Zone and is characterized by maximum amplitudes that do not exceed 4 m, with an inland extent of less than 500 m. In contrast, the 500-year tsunami is dominated by local sources in the Cascadia Subduction Zone and is characterized by maximum amplitudes in excess of 10 m and an inland extent of more than 1 km. The primary sources of uncertainty in these results include those associated with interevent time estimates, modeling of background sea level, and accounting for temporal changes in bathymetry and topography. Nonetheless, PTHA represents an important contribution to tsunami hazard assessment techniques; viewed in the broader context of risk analysis, PTHA provides a method for quantifying estimates of the likelihood and severity of the tsunami hazard, which can then be combined with vulnerability and exposure to yield estimates of tsunami risk. Copyright 2009 by the American Geophysical Union.

From Theory to Air Force Practice: Applications and Non-Binary Extensions of Probabilistic Model-Building Genetic Algorithms

DTIC Science & Technology

2006-05-31

dynamics (MD) and kinetic Monte Carlo ( KMC ) procedures. In 2D surface modeling our calculations project speedups of 9 orders of magnitude at 300 degrees...programming is used to perform customized statistical mechanics by bridging the different time scales of MD and KMC quickly and well. Speedups in

Evaluation of probabilistic forecasts with the scoringRules package

NASA Astrophysics Data System (ADS)

Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian

2017-04-01

Over the last decades probabilistic forecasts in the form of predictive distributions have become popular in many scientific disciplines. With the proliferation of probabilistic models arises the need for decision-theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way in order to better understand sources of prediction errors and to improve the models. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. In coherence with decision-theoretical principles they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This contribution presents the software package scoringRules for the statistical programming language R, which provides functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. For univariate variables, two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, ensemble weather forecasts take this form. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices. Recent developments include the addition of scoring rules to evaluate multivariate forecast distributions. The use of the scoringRules package is illustrated in an example on post-processing ensemble forecasts of temperature.

Probabilistic forecasts based on radar rainfall uncertainty

NASA Astrophysics Data System (ADS)

Liguori, S.; Rico-Ramirez, M. A.

2012-04-01

The potential advantages resulting from integrating weather radar rainfall estimates in hydro-meteorological forecasting systems is limited by the inherent uncertainty affecting radar rainfall measurements, which is due to various sources of error [1-3]. The improvement of quality control and correction techniques is recognized to play a role for the future improvement of radar-based flow predictions. However, the knowledge of the uncertainty affecting radar rainfall data can also be effectively used to build a hydro-meteorological forecasting system in a probabilistic framework. This work discusses the results of the implementation of a novel probabilistic forecasting system developed to improve ensemble predictions over a small urban area located in the North of England. An ensemble of radar rainfall fields can be determined as the sum of a deterministic component and a perturbation field, the latter being informed by the knowledge of the spatial-temporal characteristics of the radar error assessed with reference to rain-gauges measurements. This approach is similar to the REAL system [4] developed for use in the Southern-Alps. The radar uncertainty estimate can then be propagated with a nowcasting model, used to extrapolate an ensemble of radar rainfall forecasts, which can ultimately drive hydrological ensemble predictions. A radar ensemble generator has been calibrated using radar rainfall data made available from the UK Met Office after applying post-processing and corrections algorithms [5-6]. One hour rainfall accumulations from 235 rain gauges recorded for the year 2007 have provided the reference to determine the radar error. Statistics describing the spatial characteristics of the error (i.e. mean and covariance) have been computed off-line at gauges location, along with the parameters describing the error temporal correlation. A system has then been set up to impose the space-time error properties to stochastic perturbations, generated in real-time at gauges location, and then interpolated back onto the radar domain, in order to obtain probabilistic radar rainfall fields in real time. The deterministic nowcasting model integrated in the STEPS system [7-8] has been used for the purpose of propagating the uncertainty and assessing the benefit of implementing the radar ensemble generator for probabilistic rainfall forecasts and ultimately sewer flow predictions. For this purpose, events representative of different types of precipitation (i.e. stratiform/convective) and significant at the urban catchment scale (i.e. in terms of sewer overflow within the urban drainage system) have been selected. As high spatial/temporal resolution is required to the forecasts for their use in urban areas [9-11], the probabilistic nowcasts have been set up to be produced at 1 km resolution and 5 min intervals. The forecasting chain is completed by a hydrodynamic model of the urban drainage network. The aim of this work is to discuss the implementation of this probabilistic system, which takes into account the radar error to characterize the forecast uncertainty, with consequent potential benefits in the management of urban systems. It will also allow a comparison with previous findings related to the analysis of different approaches to uncertainty estimation and quantification in terms of rainfall [12] and flows at the urban scale [13]. Acknowledgements The authors would like to acknowledge the BADC, the UK Met Office and Dr. Alan Seed from the Australian Bureau of Meteorology for providing the radar data and the nowcasting model. The authors acknowledge the support from the Engineering and Physical Sciences Research Council (EPSRC) via grant EP/I012222/1.

Vagueness as Probabilistic Linguistic Knowledge

NASA Astrophysics Data System (ADS)

Lassiter, Daniel

Consideration of the metalinguistic effects of utterances involving vague terms has led Barker [1] to treat vagueness using a modified Stalnakerian model of assertion. I present a sorites-like puzzle for factual beliefs in the standard Stalnakerian model [28] and show that it can be resolved by enriching the model to make use of probabilistic belief spaces. An analogous problem arises for metalinguistic information in Barker's model, and I suggest that a similar enrichment is needed here as well. The result is a probabilistic theory of linguistic representation that retains a classical metalanguage but avoids the undesirable divorce between meaning and use inherent in the epistemic theory [34]. I also show that the probabilistic approach provides a plausible account of the sorites paradox and higher-order vagueness and that it fares well empirically and conceptually in comparison to leading competitors.

The analysis of probability task completion; Taxonomy of probabilistic thinking-based across gender in elementary school students

NASA Astrophysics Data System (ADS)

Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

2017-08-01

Formulation of mathematical learning goals now is not only oriented on cognitive product, but also leads to cognitive process, which is probabilistic thinking. Probabilistic thinking is needed by students to make a decision. Elementary school students are required to develop probabilistic thinking as foundation to learn probability at higher level. A framework of probabilistic thinking of students had been developed by using SOLO taxonomy, which consists of prestructural probabilistic thinking, unistructural probabilistic thinking, multistructural probabilistic thinking and relational probabilistic thinking. This study aimed to analyze of probability task completion based on taxonomy of probabilistic thinking. The subjects were two students of fifth grade; boy and girl. Subjects were selected by giving test of mathematical ability and then based on high math ability. Subjects were given probability tasks consisting of sample space, probability of an event and probability comparison. The data analysis consisted of categorization, reduction, interpretation and conclusion. Credibility of data used time triangulation. The results was level of boy's probabilistic thinking in completing probability tasks indicated multistructural probabilistic thinking, while level of girl's probabilistic thinking in completing probability tasks indicated unistructural probabilistic thinking. The results indicated that level of boy's probabilistic thinking was higher than level of girl's probabilistic thinking. The results could contribute to curriculum developer in developing probability learning goals for elementary school students. Indeed, teachers could teach probability with regarding gender difference.

Statistical and Probabilistic Extensions to Ground Operations' Discrete Event Simulation Modeling

NASA Technical Reports Server (NTRS)

Trocine, Linda; Cummings, Nicholas H.; Bazzana, Ashley M.; Rychlik, Nathan; LeCroy, Kenneth L.; Cates, Grant R.

2010-01-01

NASA's human exploration initiatives will invest in technologies, public/private partnerships, and infrastructure, paving the way for the expansion of human civilization into the solar system and beyond. As it is has been for the past half century, the Kennedy Space Center will be the embarkation point for humankind's journey into the cosmos. Functioning as a next generation space launch complex, Kennedy's launch pads, integration facilities, processing areas, launch and recovery ranges will bustle with the activities of the world's space transportation providers. In developing this complex, KSC teams work through the potential operational scenarios: conducting trade studies, planning and budgeting for expensive and limited resources, and simulating alternative operational schemes. Numerous tools, among them discrete event simulation (DES), were matured during the Constellation Program to conduct such analyses with the purpose of optimizing the launch complex for maximum efficiency, safety, and flexibility while minimizing life cycle costs. Discrete event simulation is a computer-based modeling technique for complex and dynamic systems where the state of the system changes at discrete points in time and whose inputs may include random variables. DES is used to assess timelines and throughput, and to support operability studies and contingency analyses. It is applicable to any space launch campaign and informs decision-makers of the effects of varying numbers of expensive resources and the impact of off nominal scenarios on measures of performance. In order to develop representative DES models, methods were adopted, exploited, or created to extend traditional uses of DES. The Delphi method was adopted and utilized for task duration estimation. DES software was exploited for probabilistic event variation. A roll-up process was used, which was developed to reuse models and model elements in other less - detailed models. The DES team continues to innovate and expand DES capabilities to address KSC's planning needs.

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

Probalistic Models for Solar Particle Events

NASA Technical Reports Server (NTRS)

Adams, James H., Jr.; Xapsos, Michael

2009-01-01

Probabilistic Models of Solar Particle Events (SPEs) are used in space mission design studies to describe the radiation environment that can be expected at a specified confidence level. The task of the designer is then to choose a design that will operate in the model radiation environment. Probabilistic models have already been developed for solar proton events that describe the peak flux, event-integrated fluence and missionintegrated fluence. In addition a probabilistic model has been developed that describes the mission-integrated fluence for the Z>2 elemental spectra. This talk will focus on completing this suite of models by developing models for peak flux and event-integrated fluence elemental spectra for the Z>2 element

EFFECTS OF CORRELATED PROBABILISTIC EXPOSURE MODEL INPUTS ON SIMULATED RESULTS

EPA Science Inventory

In recent years, more probabilistic models have been developed to quantify aggregate human exposures to environmental pollutants. The impact of correlation among inputs in these models is an important issue, which has not been resolved. Obtaining correlated data and implementi...

Operational Earthquake Forecasting and Decision-Making in a Low-Probability Environment

NASA Astrophysics Data System (ADS)

Jordan, T. H.; the International Commission on Earthquake ForecastingCivil Protection

2011-12-01

Operational earthquake forecasting (OEF) is the dissemination of authoritative information about the time dependence of seismic hazards to help communities prepare for potentially destructive earthquakes. Most previous work on the public utility of OEF has anticipated that forecasts would deliver high probabilities of large earthquakes; i.e., deterministic predictions with low error rates (false alarms and failures-to-predict) would be possible. This expectation has not been realized. An alternative to deterministic prediction is probabilistic forecasting based on empirical statistical models of aftershock triggering and seismic clustering. During periods of high seismic activity, short-term earthquake forecasts can attain prospective probability gains in excess of 100 relative to long-term forecasts. The utility of such information is by no means clear, however, because even with hundredfold increases, the probabilities of large earthquakes typically remain small, rarely exceeding a few percent over forecasting intervals of days or weeks. Civil protection agencies have been understandably cautious in implementing OEF in this sort of "low-probability environment." The need to move more quickly has been underscored by recent seismic crises, such as the 2009 L'Aquila earthquake sequence, in which an anxious public was confused by informal and inaccurate earthquake predictions. After the L'Aquila earthquake, the Italian Department of Civil Protection appointed an International Commission on Earthquake Forecasting (ICEF), which I chaired, to recommend guidelines for OEF utilization. Our report (Ann. Geophys., 54, 4, 2011; doi: 10.4401/ag-5350) concludes: (a) Public sources of information on short-term probabilities should be authoritative, scientific, open, and timely, and need to convey epistemic uncertainties. (b) Earthquake probabilities should be based on operationally qualified, regularly updated forecasting systems. (c) All operational models should be evaluated for reliability and skill by retrospective testing, and the models should be under continuous prospective testing against long-term forecasts and alternative time-dependent models. (d) Short-term models used in operational forecasting should be consistent with the long-term forecasts used in probabilistic seismic hazard analysis. (e) Alert procedures should be standardized to facilitate decisions at different levels of government, based in part on objective analysis of costs and benefits. (f) In establishing alert protocols, consideration should also be given to the less tangible aspects of value-of-information, such as gains in psychological preparedness and resilience. Authoritative statements of increased risk, even when the absolute probability is low, can provide a psychological benefit to the public by filling information vacuums that lead to informal predictions and misinformation. Formal OEF procedures based on probabilistic forecasting appropriately separate hazard estimation by scientists from the decision-making role of civil protection authorities. The prosecution of seven Italian scientists on manslaughter charges stemming from their actions before the L'Aquila earthquake makes clear why this separation should be explicit in defining OEF protocols.

Acoustic emission based damage localization in composites structures using Bayesian identification

NASA Astrophysics Data System (ADS)

Kundu, A.; Eaton, M. J.; Al-Jumali, S.; Sikdar, S.; Pullin, R.

2017-05-01

Acoustic emission based damage detection in composite structures is based on detection of ultra high frequency packets of acoustic waves emitted from damage sources (such as fibre breakage, fatigue fracture, amongst others) with a network of distributed sensors. This non-destructive monitoring scheme requires solving an inverse problem where the measured signals are linked back to the location of the source. This in turn enables rapid deployment of mitigative measures. The presence of significant amount of uncertainty associated with the operating conditions and measurements makes the problem of damage identification quite challenging. The uncertainties stem from the fact that the measured signals are affected by the irregular geometries, manufacturing imprecision, imperfect boundary conditions, existing damages/structural degradation, amongst others. This work aims to tackle these uncertainties within a framework of automated probabilistic damage detection. The method trains a probabilistic model of the parametrized input and output model of the acoustic emission system with experimental data to give probabilistic descriptors of damage locations. A response surface modelling the acoustic emission as a function of parametrized damage signals collected from sensors would be calibrated with a training dataset using Bayesian inference. This is used to deduce damage locations in the online monitoring phase. During online monitoring, the spatially correlated time data is utilized in conjunction with the calibrated acoustic emissions model to infer the probabilistic description of the acoustic emission source within a hierarchical Bayesian inference framework. The methodology is tested on a composite structure consisting of carbon fibre panel with stiffeners and damage source behaviour has been experimentally simulated using standard H-N sources. The methodology presented in this study would be applicable in the current form to structural damage detection under varying operational loads and would be investigated in future studies.

Episodic, generalized, and semantic memory tests: switching and strength effects.

PubMed

Humphreys, Michael S; Murray, Krista L

2011-09-01

We continue the process of investigating the probabilistic paired associate paradigm in an effort to understand the memory access control processes involved and to determine whether the memory structure produced is in transition between episodic and semantic memory. In this paradigm two targets are probabilistically paired with a cue across a large number of short lists. Participants can recall the target paired with the cue in the most recent list (list specific test), produce the first of the two targets that have been paired with that cue to come to mind (generalised test), and produce a free association response (semantic test). Switching between a generalised test and a list specific test did not produce a switching cost indicating a general similarity in the control processes involved. In addition, there was evidence for a dissociation between two different strength manipulations (amount of study time and number of cue-target pairings) such that number of pairings influenced the list specific, generalised and the semantic test but amount of study time only influenced the list specific and generalised test. © 2011 Canadian Psychological Association

Reconciling Streamflow Uncertainty Estimation and River Bed Morphology Dynamics. Insights from a Probabilistic Assessment of Streamflow Uncertainties Using a Reliability Diagram

NASA Astrophysics Data System (ADS)

Morlot, T.; Mathevet, T.; Perret, C.; Favre Pugin, A. C.

2014-12-01

Streamflow uncertainty estimation has recently received a large attention in the literature. A dynamic rating curve assessment method has been introduced (Morlot et al., 2014). This dynamic method allows to compute a rating curve for each gauging and a continuous streamflow time-series, while calculating streamflow uncertainties. Streamflow uncertainty takes into account many sources of uncertainty (water level, rating curve interpolation and extrapolation, gauging aging, etc.) and produces an estimated distribution of streamflow for each days. In order to caracterise streamflow uncertainty, a probabilistic framework has been applied on a large sample of hydrometric stations of the Division Technique Générale (DTG) of Électricité de France (EDF) hydrometric network (>250 stations) in France. A reliability diagram (Wilks, 1995) has been constructed for some stations, based on the streamflow distribution estimated for a given day and compared to a real streamflow observation estimated via a gauging. To build a reliability diagram, we computed the probability of an observed streamflow (gauging), given the streamflow distribution. Then, the reliability diagram allows to check that the distribution of probabilities of non-exceedance of the gaugings follows a uniform law (i.e., quantiles should be equipropables). Given the shape of the reliability diagram, the probabilistic calibration is caracterised (underdispersion, overdispersion, bias) (Thyer et al., 2009). In this paper, we present case studies where reliability diagrams have different statistical properties for different periods. Compared to our knowledge of river bed morphology dynamic of these hydrometric stations, we show how reliability diagram gives us invaluable information on river bed movements, like a continuous digging or backfilling of the hydraulic control due to erosion or sedimentation processes. Hence, the careful analysis of reliability diagrams allows to reconcile statistics and long-term river bed morphology processes. This knowledge improves our real-time management of hydrometric stations, given a better caracterisation of erosion/sedimentation processes and the stability of hydrometric station hydraulic control.

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

NASA Astrophysics Data System (ADS)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.

Probabilistic Tsunami Hazard Assessment: the Seaside, Oregon Pilot Study

NASA Astrophysics Data System (ADS)

Gonzalez, F. I.; Geist, E. L.; Synolakis, C.; Titov, V. V.

2004-12-01

A pilot study of Seaside, Oregon is underway, to develop methodologies for probabilistic tsunami hazard assessments that can be incorporated into Flood Insurance Rate Maps (FIRMs) developed by FEMA's National Flood Insurance Program (NFIP). Current NFIP guidelines for tsunami hazard assessment rely on the science, technology and methodologies developed in the 1970s; although generally regarded as groundbreaking and state-of-the-art for its time, this approach is now superseded by modern methods that reflect substantial advances in tsunami research achieved in the last two decades. In particular, post-1990 technical advances include: improvements in tsunami source specification; improved tsunami inundation models; better computational grids by virtue of improved bathymetric and topographic databases; a larger database of long-term paleoseismic and paleotsunami records and short-term, historical earthquake and tsunami records that can be exploited to develop improved probabilistic methodologies; better understanding of earthquake recurrence and probability models. The NOAA-led U.S. National Tsunami Hazard Mitigation Program (NTHMP), in partnership with FEMA, USGS, NSF and Emergency Management and Geotechnical agencies of the five Pacific States, incorporates these advances into site-specific tsunami hazard assessments for coastal communities in Alaska, California, Hawaii, Oregon and Washington. NTHMP hazard assessment efforts currently focus on developing deterministic, "credible worst-case" scenarios that provide valuable guidance for hazard mitigation and emergency management. The NFIP focus, on the other hand, is on actuarial needs that require probabilistic hazard assessments such as those that characterize 100- and 500-year flooding events. There are clearly overlaps in NFIP and NTHMP objectives. NTHMP worst-case scenario assessments that include an estimated probability of occurrence could benefit the NFIP; NFIP probabilistic assessments of 100- and 500-yr events could benefit the NTHMP. The joint NFIP/NTHMP pilot study at Seaside, Oregon is organized into three closely related components: Probabilistic, Modeling, and Impact studies. Probabilistic studies (Geist, et al., this session) are led by the USGS and include the specification of near- and far-field seismic tsunami sources and their associated probabilities. Modeling studies (Titov, et al., this session) are led by NOAA and include the development and testing of a Seaside tsunami inundation model and an associated database of computed wave height and flow velocity fields. Impact studies (Synolakis, et al., this session) are led by USC and include the computation and analyses of indices for the categorization of hazard zones. The results of each component study will be integrated to produce a Seaside tsunami hazard map. This presentation will provide a brief overview of the project and an update on progress, while the above-referenced companion presentations will provide details on the methods used and the preliminary results obtained by each project component.

A Measure Approximation for Distributionally Robust PDE-Constrained Optimization Problems

DOE PAGES

Kouri, Drew Philip

2017-12-19

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robustmore » optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.« less

Circularly-symmetric complex normal ratio distribution for scalar transmissibility functions. Part I: Fundamentals

NASA Astrophysics Data System (ADS)

Yan, Wang-Ji; Ren, Wei-Xin

2016-12-01

Recent advances in signal processing and structural dynamics have spurred the adoption of transmissibility functions in academia and industry alike. Due to the inherent randomness of measurement and variability of environmental conditions, uncertainty impacts its applications. This study is focused on statistical inference for raw scalar transmissibility functions modeled as complex ratio random variables. The goal is achieved through companion papers. This paper (Part I) is dedicated to dealing with a formal mathematical proof. New theorems on multivariate circularly-symmetric complex normal ratio distribution are proved on the basis of principle of probabilistic transformation of continuous random vectors. The closed-form distributional formulas for multivariate ratios of correlated circularly-symmetric complex normal random variables are analytically derived. Afterwards, several properties are deduced as corollaries and lemmas to the new theorems. Monte Carlo simulation (MCS) is utilized to verify the accuracy of some representative cases. This work lays the mathematical groundwork to find probabilistic models for raw scalar transmissibility functions, which are to be expounded in detail in Part II of this study.

Probabilistic drug connectivity mapping

PubMed Central

2014-01-01

Background The aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most relevant profiles for a given query drug. We infer the relevance for retrieval by data-driven probabilistic modeling of the drug responses, resulting in probabilistic connectivity mapping, and further consider the available cell lines as different data sources. We use a special type of probabilistic model to separate what is shared and specific between the sources, in contrast to earlier connectivity mapping methods that have intentionally aggregated all available data, neglecting information about the differences between the cell lines. Results We show that the probabilistic multi-source connectivity mapping method is superior to alternatives in finding functionally and chemically similar drugs from the Connectivity Map data set. We also demonstrate that an extension of the method is capable of retrieving combinations of drugs that match different relevant parts of the query drug response profile. Conclusions The probabilistic modeling-based connectivity mapping method provides a promising alternative to earlier methods. Principled integration of data from different cell lines helps to identify relevant responses for specific drug repositioning applications. PMID:24742351

The Effects of the Previous Outcome on Probabilistic Choice in Rats

PubMed Central

Marshall, Andrew T.; Kirkpatrick, Kimberly

2014-01-01

This study examined the effects of previous outcomes on subsequent choices in a probabilistic-choice task. Twenty-four rats were trained to choose between a certain outcome (1 or 3 pellets) versus an uncertain outcome (3 or 9 pellets), delivered with a probability of .1, .33, .67, and .9 in different phases. Uncertain outcome choices increased with the probability of uncertain food. Additionally, uncertain choices increased with the probability of uncertain food following both certain-choice outcomes and unrewarded uncertain choices. However, following uncertain-choice food outcomes, there was a tendency to choose the uncertain outcome in all cases, indicating that the rats continued to “gamble” after successful uncertain choices, regardless of the overall probability or magnitude of food. A subsequent manipulation, in which the probability of uncertain food varied within each session as a function of the previous uncertain outcome, examined how the previous outcome and probability of uncertain food affected choice in a dynamic environment. Uncertain-choice behavior increased with the probability of uncertain food. The rats exhibited increased sensitivity to probability changes and a greater degree of win–stay/lose–shift behavior than in the static phase. Simulations of two sequential choice models were performed to explore the possible mechanisms of reward value computations. The simulation results supported an exponentially decaying value function that updated as a function of trial (rather than time). These results emphasize the importance of analyzing global and local factors in choice behavior and suggest avenues for the future development of sequential-choice models. PMID:23205915

Improving the quality of pressure ulcer care with prevention: a cost-effectiveness analysis.

PubMed

Padula, William V; Mishra, Manish K; Makic, Mary Beth F; Sullivan, Patrick W

2011-04-01

In October 2008, Centers for Medicare and Medicaid Services discontinued reimbursement for hospital-acquired pressure ulcers (HAPUs), thus placing stress on hospitals to prevent incidence of this costly condition. To evaluate whether prevention methods are cost-effective compared with standard care in the management of HAPUs. A semi-Markov model simulated the admission of patients to an acute care hospital from the time of admission through 1 year using the societal perspective. The model simulated health states that could potentially lead to an HAPU through either the practice of "prevention" or "standard care." Univariate sensitivity analyses, threshold analyses, and Bayesian multivariate probabilistic sensitivity analysis using 10,000 Monte Carlo simulations were conducted. Cost per quality-adjusted life-years (QALYs) gained for the prevention of HAPUs. Prevention was cost saving and resulted in greater expected effectiveness compared with the standard care approach per hospitalization. The expected cost of prevention was $7276.35, and the expected effectiveness was 11.241 QALYs. The expected cost for standard care was $10,053.95, and the expected effectiveness was 9.342 QALYs. The multivariate probabilistic sensitivity analysis showed that prevention resulted in cost savings in 99.99% of the simulations. The threshold cost of prevention was $821.53 per day per person, whereas the cost of prevention was estimated to be $54.66 per day per person. This study suggests that it is more cost effective to pay for prevention of HAPUs compared with standard care. Continuous preventive care of HAPUs in acutely ill patients could potentially reduce incidence and prevalence, as well as lead to lower expenditures.

A novel visualisation tool for climate services: a case study of temperature extremes and human mortality in Europe

NASA Astrophysics Data System (ADS)

Lowe, R.; Ballester, J.; Robine, J.; Herrmann, F. R.; Jupp, T. E.; Stephenson, D.; Rodó, X.

2013-12-01

Users of climate information often require probabilistic information on which to base their decisions. However, communicating information contained within a probabilistic forecast presents a challenge. In this paper we demonstrate a novel visualisation technique to display ternary probabilistic forecasts on a map in order to inform decision making. In this method, ternary probabilistic forecasts, which assign probabilities to a set of three outcomes (e.g. low, medium, and high risk), are considered as a point in a triangle of barycentric coordinates. This allows a unique colour to be assigned to each forecast from a continuum of colours defined on the triangle. Colour saturation increases with information gain relative to the reference forecast (i.e. the long term average). This provides additional information to decision makers compared with conventional methods used in seasonal climate forecasting, where one colour is used to represent one forecast category on a forecast map (e.g. red = ';dry'). We use the tool to present climate-related mortality projections across Europe. Temperature and humidity are related to human mortality via location-specific transfer functions, calculated using historical data. Daily mortality data at the NUTS2 level for 16 countries in Europe were obtain from 1998-2005. Transfer functions were calculated for 54 aggregations in Europe, defined using criteria related to population and climatological similarities. Aggregations are restricted to fall within political boundaries to avoid problems related to varying adaptation policies between countries. A statistical model is fit to cold and warm tails to estimate future mortality using forecast temperatures, in a Bayesian probabilistic framework. Using predefined categories of temperature-related mortality risk, we present maps of probabilistic projections for human mortality at seasonal to decadal time scales. We demonstrate the information gained from using this technique compared to more traditional methods to display ternary probabilistic forecasts. This technique allows decision makers to identify areas where the model predicts with certainty area-specific heat waves or cold snaps, in order to effectively target resources to those areas most at risk, for a given season or year. It is hoped that this visualisation tool will facilitate the interpretation of the probabilistic forecasts not only for public health decision makers but also within a multi-sectoral climate service framework.

Probabilistic Approach to Conditional Probability of Release of Hazardous Materials from Railroad Tank Cars during Accidents

DOT National Transportation Integrated Search

2009-10-13

This paper describes a probabilistic approach to estimate the conditional probability of release of hazardous materials from railroad tank cars during train accidents. Monte Carlo methods are used in developing a probabilistic model to simulate head ...

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers.

PubMed

Campbell, Kieran R; Yau, Christopher

2017-03-15

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.

Integrated software health management for aerospace guidance, navigation, and control systems: A probabilistic reasoning approach

NASA Astrophysics Data System (ADS)

Mbaya, Timmy

Embedded Aerospace Systems have to perform safety and mission critical operations in a real-time environment where timing and functional correctness are extremely important. Guidance, Navigation, and Control (GN&C) systems substantially rely on complex software interfacing with hardware in real-time; any faults in software or hardware, or their interaction could result in fatal consequences. Integrated Software Health Management (ISWHM) provides an approach for detection and diagnosis of software failures while the software is in operation. The ISWHM approach is based on probabilistic modeling of software and hardware sensors using a Bayesian network. To meet memory and timing constraints of real-time embedded execution, the Bayesian network is compiled into an Arithmetic Circuit, which is used for on-line monitoring. This type of system monitoring, using an ISWHM, provides automated reasoning capabilities that compute diagnoses in a timely manner when failures occur. This reasoning capability enables time-critical mitigating decisions and relieves the human agent from the time-consuming and arduous task of foraging through a multitude of isolated---and often contradictory---diagnosis data. For the purpose of demonstrating the relevance of ISWHM, modeling and reasoning is performed on a simple simulated aerospace system running on a real-time operating system emulator, the OSEK/Trampoline platform. Models for a small satellite and an F-16 fighter jet GN&C (Guidance, Navigation, and Control) system have been implemented. Analysis of the ISWHM is then performed by injecting faults and analyzing the ISWHM's diagnoses.

Solving probability reasoning based on DNA strand displacement and probability modules.

PubMed

Zhang, Qiang; Wang, Xiaobiao; Wang, Xiaojun; Zhou, Changjun

2017-12-01

In computation biology, DNA strand displacement technology is used to simulate the computation process and has shown strong computing ability. Most researchers use it to solve logic problems, but it is only rarely used in probabilistic reasoning. To process probabilistic reasoning, a conditional probability derivation model and total probability model based on DNA strand displacement were established in this paper. The models were assessed through the game "read your mind." It has been shown to enable the application of probabilistic reasoning in genetic diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Word-level language modeling for P300 spellers based on discriminative graphical models

NASA Astrophysics Data System (ADS)

Delgado Saa, Jaime F.; de Pesters, Adriana; McFarland, Dennis; Çetin, Müjdat

2015-04-01

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. Main results. Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. Significance. The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

Localization of the lumbar discs using machine learning and exact probabilistic inference.

PubMed

Oktay, Ayse Betul; Akgul, Yusuf Sinan

2011-01-01

We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.

On the probabilistic structure of water age

NASA Astrophysics Data System (ADS)

Porporato, Amilcare; Calabrese, Salvatore

2015-05-01

The age distribution of water in hydrologic systems has received renewed interest recently, especially in relation to watershed response to rainfall inputs. The purpose of this contribution is first to draw attention to existing theories of age distributions in population dynamics, fluid mechanics and stochastic groundwater, and in particular to the McKendrick-von Foerster equation and its generalizations and solutions. A second and more important goal is to clarify that, when hydrologic fluxes are modeled by means of time-varying stochastic processes, the age distributions must themselves be treated as random functions. Once their probabilistic structure is obtained, it can be used to characterize the variability of age distributions in real systems and thus help quantify the inherent uncertainty in the field determination of water age. We illustrate these concepts with reference to a stochastic storage model, which has been used as a minimalist model of soil moisture and streamflow dynamics.

A Bayesian Attractor Model for Perceptual Decision Making

PubMed Central

Bitzer, Sebastian; Bruineberg, Jelle; Kiebel, Stefan J.

2015-01-01

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks. PMID:26267143

Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles.

PubMed

Moretti, Tamyra R; Just, Rebecca S; Kehl, Susannah C; Willis, Leah E; Buckleton, John S; Bright, Jo-Anne; Taylor, Duncan A; Onorato, Anthony J

2017-07-01

The interpretation of DNA evidence can entail analysis of challenging STR typing results. Genotypes inferred from low quality or quantity specimens, or mixed DNA samples originating from multiple contributors, can result in weak or inconclusive match probabilities when a binary interpretation method and necessary thresholds (such as a stochastic threshold) are employed. Probabilistic genotyping approaches, such as fully continuous methods that incorporate empirically determined biological parameter models, enable usage of more of the profile information and reduce subjectivity in interpretation. As a result, software-based probabilistic analyses tend to produce more consistent and more informative results regarding potential contributors to DNA evidence. Studies to assess and internally validate the probabilistic genotyping software STRmix™ for casework usage at the Federal Bureau of Investigation Laboratory were conducted using lab-specific parameters and more than 300 single-source and mixed contributor profiles. Simulated forensic specimens, including constructed mixtures that included DNA from two to five donors across a broad range of template amounts and contributor proportions, were used to examine the sensitivity and specificity of the system via more than 60,000 tests comparing hundreds of known contributors and non-contributors to the specimens. Conditioned analyses, concurrent interpretation of amplification replicates, and application of an incorrect contributor number were also performed to further investigate software performance and probe the limitations of the system. In addition, the results from manual and probabilistic interpretation of both prepared and evidentiary mixtures were compared. The findings support that STRmix™ is sufficiently robust for implementation in forensic laboratories, offering numerous advantages over historical methods of DNA profile analysis and greater statistical power for the estimation of evidentiary weight, and can be used reliably in human identification testing. With few exceptions, likelihood ratio results reflected intuitively correct estimates of the weight of the genotype possibilities and known contributor genotypes. This comprehensive evaluation provides a model in accordance with SWGDAM recommendations for internal validation of a probabilistic genotyping system for DNA evidence interpretation. Copyright © 2017. Published by Elsevier B.V.

Probabilistic interpretation of Peelle's pertinent puzzle and its resolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanson, Kenneth M.; Kawano, T.; Talou, P.

2004-01-01

Peelle's Pertinent Puzzle (PPP) states a seemingly plausible set of measurements with their covariance matrix, which produce an implausible answer. To answer the PPP question, we describe a reasonable experimental situation that is consistent with the PPP solution. The confusion surrounding the PPP arises in part because of its imprecise statement, which permits to a variety of interpretations and resulting answers, some of which seem implausible. We emphasize the importance of basing the analysis on an unambiguous probabilistic model that reflects the experimental situation. We present several different models of how the measurements quoted in the PPP problem could bemore » obtained, and interpret their solution in terms of a detailed probabilistic analysis. We suggest a probabilistic approach to handling uncertainties about which model to use.« less

Probabilistic Interpretation of Peelle's Pertinent Puzzle and its Resolution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanson, Kenneth M.; Kawano, Toshihiko; Talou, Patrick

2005-05-24

Peelle's Pertinent Puzzle (PPP) states a seemingly plausible set of measurements with their covariance matrix, which produce an implausible answer. To answer the PPP question, we describe a reasonable experimental situation that is consistent with the PPP solution. The confusion surrounding the PPP arises in part because of its imprecise statement, which permits to a variety of interpretations and resulting answers, some of which seem implausible. We emphasize the importance of basing the analysis on an unambiguous probabilistic model that reflects the experimental situation. We present several different models of how the measurements quoted in the PPP problem could bemore » obtained, and interpret their solution in terms of a detailed probabilistic analysis. We suggest a probabilistic approach to handling uncertainties about which model to use.« less

Probabilistic characterization of wind turbine blades via aeroelasticity and spinning finite element formulation

NASA Astrophysics Data System (ADS)

Velazquez, Antonio; Swartz, R. Andrew

2012-04-01

Wind energy is an increasingly important component of this nation's renewable energy portfolio, however safe and economical wind turbine operation is a critical need to ensure continued adoption. Safe operation of wind turbine structures requires not only information regarding their condition, but their operational environment. Given the difficulty inherent in SHM processes for wind turbines (damage detection, location, and characterization), some uncertainty in conditional assessment is expected. Furthermore, given the stochastic nature of the loading on turbine structures, a probabilistic framework is appropriate to characterize their risk of failure at a given time. Such information will be invaluable to turbine controllers, allowing them to operate the structures within acceptable risk profiles. This study explores the characterization of the turbine loading and response envelopes for critical failure modes of the turbine blade structures. A framework is presented to develop an analytical estimation of the loading environment (including loading effects) based on the dynamic behavior of the blades. This is influenced by behaviors including along and across-wind aero-elastic effects, wind shear gradient, tower shadow effects, and centrifugal stiffening effects. The proposed solution includes methods that are based on modal decomposition of the blades and require frequent updates to the estimated modal properties to account for the time-varying nature of the turbine and its environment. The estimated demand statistics are compared to a code-based resistance curve to determine a probabilistic estimate of the risk of blade failure given the loading environment.

A state-based probabilistic model for tumor respiratory motion prediction

NASA Astrophysics Data System (ADS)

Kalet, Alan; Sandison, George; Wu, Huanmei; Schmitz, Ruth

2010-12-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting

NASA Astrophysics Data System (ADS)

Biondi, D.; De Luca, D. L.

2013-02-01

SummaryThe paper evaluates, for a number of flood events, the performance of a Bayesian Forecasting System (BFS), with the aim of evaluating total uncertainty in real-time flood forecasting. The predictive uncertainty of future streamflow is estimated through the Bayesian integration of two separate processors. The former evaluates the propagation of input uncertainty on simulated river discharge, the latter computes the hydrological uncertainty of actual river discharge associated with all other possible sources of error. A stochastic model and a distributed rainfall-runoff model were assumed, respectively, for rainfall and hydrological response simulations. A case study was carried out for a small basin in the Calabria region (southern Italy). The performance assessment of the BFS was performed with adequate verification tools suited for probabilistic forecasts of continuous variables such as streamflow. Graphical tools and scalar metrics were used to evaluate several attributes of the forecast quality of the entire time-varying predictive distributions: calibration, sharpness, accuracy, and continuous ranked probability score (CRPS). Besides the overall system, which incorporates both sources of uncertainty, other hypotheses resulting from the BFS properties were examined, corresponding to (i) a perfect hydrological model; (ii) a non-informative rainfall forecast for predicting streamflow; and (iii) a perfect input forecast. The results emphasize the importance of using different diagnostic approaches to perform comprehensive analyses of predictive distributions, to arrive at a multifaceted view of the attributes of the prediction. For the case study, the selected criteria revealed the interaction of the different sources of error, in particular the crucial role of the hydrological uncertainty processor when compensating, at the cost of wider forecast intervals, for the unreliable and biased predictive distribution resulting from the Precipitation Uncertainty Processor.

Uncertainty in weather and climate prediction

PubMed Central

Slingo, Julia; Palmer, Tim

2011-01-01

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. PMID:22042896

Characterizing the topology of probabilistic biological networks.

PubMed

Todor, Andrei; Dobra, Alin; Kahveci, Tamer

2013-01-01

Biological interactions are often uncertain events, that may or may not take place with some probability. This uncertainty leads to a massive number of alternative interaction topologies for each such network. The existing studies analyze the degree distribution of biological networks by assuming that all the given interactions take place under all circumstances. This strong and often incorrect assumption can lead to misleading results. In this paper, we address this problem and develop a sound mathematical basis to characterize networks in the presence of uncertain interactions. Using our mathematical representation, we develop a method that can accurately describe the degree distribution of such networks. We also take one more step and extend our method to accurately compute the joint-degree distributions of node pairs connected by edges. The number of possible network topologies grows exponentially with the number of uncertain interactions. However, the mathematical model we develop allows us to compute these degree distributions in polynomial time in the number of interactions. Our method works quickly even for entire protein-protein interaction (PPI) networks. It also helps us find an adequate mathematical model using MLE. We perform a comparative study of node-degree and joint-degree distributions in two types of biological networks: the classical deterministic networks and the more flexible probabilistic networks. Our results confirm that power-law and log-normal models best describe degree distributions for both probabilistic and deterministic networks. Moreover, the inverse correlation of degrees of neighboring nodes shows that, in probabilistic networks, nodes with large number of interactions prefer to interact with those with small number of interactions more frequently than expected. We also show that probabilistic networks are more robust for node-degree distribution computation than the deterministic ones. all the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/projects/probNet/.

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

A Re-Unification of Two Competing Models for Document Retrieval.

ERIC Educational Resources Information Center

Bodoff, David

1999-01-01

Examines query-oriented versus document-oriented information retrieval and feedback learning. Highlights include a reunification of the two approaches for probabilistic document retrieval and for vector space model (VSM) retrieval; learning in VSM and in probabilistic models; multi-dimensional scaling; and ongoing field studies. (LRW)

A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5

EPA Science Inventory

A first generation probabilistic population exposure model for Particulate Matter (PM), specifically for predicting PM10, and PM2.5, exposures of an urban, population has been developed. This model is intended to be used to predict exposure (magnitude, frequency, and duration) ...

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

Comparison of the economic impact of different wind power forecast systems for producers

NASA Astrophysics Data System (ADS)

Alessandrini, S.; Davò, F.; Sperati, S.; Benini, M.; Delle Monache, L.

2014-05-01

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

The MIT Integrated Global System Model: A facility for Assessing and Communicating Climate Change Uncertainty (Invited)

NASA Astrophysics Data System (ADS)

Prinn, R. G.

2013-12-01

The world is facing major challenges that create tensions between human development and environmental sustenance. In facing these challenges, computer models are invaluable tools for addressing the need for probabilistic approaches to forecasting. To illustrate this, I use the MIT Integrated Global System Model framework (IGSM; http://globalchange.mit.edu ). The IGSM consists of a set of coupled sub-models of global economic and technological development and resultant emissions, and physical, dynamical and chemical processes in the atmosphere, land, ocean and ecosystems (natural and managed). Some of the sub-models have both complex and simplified versions available, with the choice of which version to use being guided by the questions being addressed. Some sub-models (e.g.urban air pollution) are reduced forms of complex ones created by probabilistic collocation with polynomial chaos bases. Given the significant uncertainties in the model components, it is highly desirable that forecasts be probabilistic. We achieve this by running 400-member ensembles (Latin hypercube sampling) with different choices for key uncertain variables and processes within the human and natural system model components (pdfs of inputs estimated by model-observation comparisons, literature surveys, or expert elicitation). The IGSM has recently been used for probabilistic forecasts of climate, each using 400-member ensembles: one ensemble assumes no explicit climate mitigation policy and others assume increasingly stringent policies involving stabilization of greenhouse gases at various levels. These forecasts indicate clearly that the greatest effect of these policies is to lower the probability of extreme changes. The value of such probability analyses for policy decision-making lies in their ability to compare relative (not just absolute) risks of various policies, which are less affected by the earth system model uncertainties. Given the uncertainties in forecasts, it is also clear that we need to evaluate policies based on their ability to lower risk, and to re-evaluate decisions over time as new knowledge is gained. Reference: R. G. Prinn, Development and Application of Earth System Models, Proceedings, National Academy of Science, June 15, 2012, http://www.pnas.org/cgi/doi/10.1073/pnas.1107470109.

Model Verification and Validation Concepts for a Probabilistic Fracture Assessment Model to Predict Cracking of Knife Edge Seals in the Space Shuttle Main Engine High Pressure Oxidizer

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.; Riha, David S.

2013-01-01

Physics-based models are routinely used to predict the performance of engineered systems to make decisions such as when to retire system components, how to extend the life of an aging system, or if a new design will be safe or available. Model verification and validation (V&V) is a process to establish credibility in model predictions. Ideally, carefully controlled validation experiments will be designed and performed to validate models or submodels. In reality, time and cost constraints limit experiments and even model development. This paper describes elements of model V&V during the development and application of a probabilistic fracture assessment model to predict cracking in space shuttle main engine high-pressure oxidizer turbopump knife-edge seals. The objective of this effort was to assess the probability of initiating and growing a crack to a specified failure length in specific flight units for different usage and inspection scenarios. The probabilistic fracture assessment model developed in this investigation combined a series of submodels describing the usage, temperature history, flutter tendencies, tooth stresses and numbers of cycles, fatigue cracking, nondestructive inspection, and finally the probability of failure. The analysis accounted for unit-to-unit variations in temperature, flutter limit state, flutter stress magnitude, and fatigue life properties. The investigation focused on the calculation of relative risk rather than absolute risk between the usage scenarios. Verification predictions were first performed for three units with known usage and cracking histories to establish credibility in the model predictions. Then, numerous predictions were performed for an assortment of operating units that had flown recently or that were projected for future flights. Calculations were performed using two NASA-developed software tools: NESSUS(Registered Trademark) for the probabilistic analysis, and NASGRO(Registered Trademark) for the fracture mechanics analysis. The goal of these predictions was to provide additional information to guide decisions on the potential of reusing existing and installed units prior to the new design certification.

Analysis of scale effect in compressive ice failure and implications for design

NASA Astrophysics Data System (ADS)

Taylor, Rocky Scott

The main focus of the study was the analysis of scale effect in local ice pressure resulting from probabilistic (spalling) fracture and the relationship between local and global loads due to the averaging of pressures across the width of a structure. A review of fundamental theory, relevant ice mechanics and a critical analysis of data and theory related to the scale dependent pressure behavior of ice were completed. To study high pressure zones (hpzs), data from small-scale indentation tests carried out at the NRC-IOT were analyzed, including small-scale ice block and ice sheet tests. Finite element analysis was used to model a sample ice block indentation event using a damaging, viscoelastic material model and element removal techniques (for spalling). Medium scale tactile sensor data from the Japan Ocean Industries Association (JOIA) program were analyzed to study details of hpz behavior. The averaging of non-simultaneous hpz loads during an ice-structure interaction was examined using local panel pressure data. Probabilistic averaging methodology for extrapolating full-scale pressures from local panel pressures was studied and an improved correlation model was formulated. Panel correlations for high speed events were observed to be lower than panel correlations for low speed events. Global pressure estimates based on probabilistic averaging were found to give substantially lower average errors in estimation of load compared with methods based on linear extrapolation (no averaging). Panel correlations were analyzed for Molikpaq and compared with JOIA results. From this analysis, it was shown that averaging does result in decreasing pressure for increasing structure width. The relationship between local pressure and ice thickness for a panel of unit width was studied in detail using full-scale data from the STRICE, Molikpaq, Cook Inlet and Japan Ocean Industries Association (JOIA) data sets. A distinct trend of decreasing pressure with increasing ice thickness was observed. The pressure-thickness behavior was found to be well modeled by the power law relationships Pavg = 0.278 h-0.408 MPa and Pstd = 0.172h-0.273 MPa for the mean and standard deviation of pressure, respectively. To study theoretical aspects of spalling fracture and the pressure-thickness scale effect, probabilistic failure models have been developed. A probabilistic model based on Weibull theory (tensile stresses only) was first developed. Estimates of failure pressure obtained with this model were orders of magnitude higher than the pressures observed from benchmark data due to the assumption of only tensile failure. A probabilistic fracture mechanics (PFM) model including both tensile and compressive (shear) cracks was developed. Criteria for unstable fracture in tensile and compressive (shear) zones were given. From these results a clear theoretical scale effect in peak (spalling) pressure was observed. This scale effect followed the relationship Pp,th = 0.15h-0.50 MPa which agreed well with the benchmark data. The PFM model was applied to study the effect of ice edge shape (taper angle) and hpz eccentricity. Results indicated that specimens with flat edges spall at lower pressures while those with more tapered edges spall less readily. The mean peak (failure) pressure was also observed to decrease with increased eccentricity. It was concluded that hpzs centered about the middle of the ice thickness are the zones most likely to create the peak pressures that are of interest in design. Promising results were obtained using the PFM model, which provides strong support for continued research in the development and application of probabilistic fracture mechanics to the study of scale effects in compressive ice failure and to guide the development of methods for the estimation of design ice pressures.

Auditory expectation: the information dynamics of music perception and cognition.

PubMed

Pearce, Marcus T; Wiggins, Geraint A

2012-10-01

Following in a psychological and musicological tradition beginning with Leonard Meyer, and continuing through David Huron, we present a functional, cognitive account of the phenomenon of expectation in music, grounded in computational, probabilistic modeling. We summarize a range of evidence for this approach, from psychology, neuroscience, musicology, linguistics, and creativity studies, and argue that simulating expectation is an important part of understanding a broad range of human faculties, in music and beyond. Copyright © 2012 Cognitive Science Society, Inc.

On the Use of the Beta Distribution in Probabilistic Resource Assessments

USGS Publications Warehouse

Olea, R.A.

2011-01-01

The triangular distribution is a popular choice when it comes to modeling bounded continuous random variables. Its wide acceptance derives mostly from its simple analytic properties and the ease with which modelers can specify its three parameters through the extremes and the mode. On the negative side, hardly any real process follows a triangular distribution, which from the outset puts at a disadvantage any model employing triangular distributions. At a time when numerical techniques such as the Monte Carlo method are displacing analytic approaches in stochastic resource assessments, easy specification remains the most attractive characteristic of the triangular distribution. The beta distribution is another continuous distribution defined within a finite interval offering wider flexibility in style of variation, thus allowing consideration of models in which the random variables closely follow the observed or expected styles of variation. Despite its more complex definition, generation of values following a beta distribution is as straightforward as generating values following a triangular distribution, leaving the selection of parameters as the main impediment to practically considering beta distributions. This contribution intends to promote the acceptance of the beta distribution by explaining its properties and offering several suggestions to facilitate the specification of its two shape parameters. In general, given the same distributional parameters, use of the beta distributions in stochastic modeling may yield significantly different results, yet better estimates, than the triangular distribution. ?? 2011 International Association for Mathematical Geology (outside the USA).

Longitudinal Temporal and Probabilistic Prediction of Survival in a Cohort of Patients With Advanced Cancer

PubMed Central

Perez-Cruz, Pedro E.; dos Santos, Renata; Silva, Thiago Buosi; Crovador, Camila Souza; Nascimento, Maria Salete de Angelis; Hall, Stacy; Fajardo, Julieta; Bruera, Eduardo; Hui, David

2014-01-01

Context Survival prognostication is important during end-of-life. The accuracy of clinician prediction of survival (CPS) over time has not been well characterized. Objectives To examine changes in prognostication accuracy during the last 14 days of life in a cohort of patients with advanced cancer admitted to two acute palliative care units and to compare the accuracy between the temporal and probabilistic approaches. Methods Physicians and nurses prognosticated survival daily for cancer patients in two hospitals until death/discharge using two prognostic approaches: temporal and probabilistic. We assessed accuracy for each method daily during the last 14 days of life comparing accuracy at day −14 (baseline) with accuracy at each time point using a test of proportions. Results 6718 temporal and 6621 probabilistic estimations were provided by physicians and nurses for 311 patients, respectively. Median (interquartile range) survival was 8 (4, 20) days. Temporal CPS had low accuracy (10–40%) and did not change over time. In contrast, probabilistic CPS was significantly more accurate (p<.05 at each time point) but decreased close to death. Conclusion Probabilistic CPS was consistently more accurate than temporal CPS over the last 14 days of life; however, its accuracy decreased as patients approached death. Our findings suggest that better tools to predict impending death are necessary. PMID:24746583

The probabilistic nature of preferential choice.

PubMed

Rieskamp, Jörg

2008-11-01

Previous research has developed a variety of theories explaining when and why people's decisions under risk deviate from the standard economic view of expected utility maximization. These theories are limited in their predictive accuracy in that they do not explain the probabilistic nature of preferential choice, that is, why an individual makes different choices in nearly identical situations, or why the magnitude of these inconsistencies varies in different situations. To illustrate the advantage of probabilistic theories, three probabilistic theories of decision making under risk are compared with their deterministic counterparts. The probabilistic theories are (a) a probabilistic version of a simple choice heuristic, (b) a probabilistic version of cumulative prospect theory, and (c) decision field theory. By testing the theories with the data from three experimental studies, the superiority of the probabilistic models over their deterministic counterparts in predicting people's decisions under risk become evident. When testing the probabilistic theories against each other, decision field theory provides the best account of the observed behavior.

Real-time adaptive aircraft scheduling

NASA Technical Reports Server (NTRS)

Kolitz, Stephan E.; Terrab, Mostafa

1990-01-01

One of the most important functions of any air traffic management system is the assignment of ground-holding times to flights, i.e., the determination of whether and by how much the take-off of a particular aircraft headed for a congested part of the air traffic control (ATC) system should be postponed in order to reduce the likelihood and extent of airborne delays. An analysis is presented for the fundamental case in which flights from many destinations must be scheduled for arrival at a single congested airport; the formulation is also useful in scheduling the landing of airborne flights within the extended terminal area. A set of approaches is described for addressing a deterministic and a probabilistic version of this problem. For the deterministic case, where airport capacities are known and fixed, several models were developed with associated low-order polynomial-time algorithms. For general delay cost functions, these algorithms find an optimal solution. Under a particular natural assumption regarding the delay cost function, an extremely fast (O(n ln n)) algorithm was developed. For the probabilistic case, using an estimated probability distribution of airport capacities, a model was developed with an associated low-order polynomial-time heuristic algorithm with useful properties.

Probabilistic Prediction of Lifetimes of Ceramic Parts

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Gyekenyesi, John P.; Jadaan, Osama M.; Palfi, Tamas; Powers, Lynn; Reh, Stefan; Baker, Eric H.

2006-01-01

ANSYS/CARES/PDS is a software system that combines the ANSYS Probabilistic Design System (PDS) software with a modified version of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) Version 6.0 software. [A prior version of CARES/Life was reported in Program for Evaluation of Reliability of Ceramic Parts (LEW-16018), NASA Tech Briefs, Vol. 20, No. 3 (March 1996), page 28.] CARES/Life models effects of stochastic strength, slow crack growth, and stress distribution on the overall reliability of a ceramic component. The essence of the enhancement in CARES/Life 6.0 is the capability to predict the probability of failure using results from transient finite-element analysis. ANSYS PDS models the effects of uncertainty in material properties, dimensions, and loading on the stress distribution and deformation. ANSYS/CARES/PDS accounts for the effects of probabilistic strength, probabilistic loads, probabilistic material properties, and probabilistic tolerances on the lifetime and reliability of the component. Even failure probability becomes a stochastic quantity that can be tracked as a response variable. ANSYS/CARES/PDS enables tracking of all stochastic quantities in the design space, thereby enabling more precise probabilistic prediction of lifetimes of ceramic components.

Economic analysis of continuous antibiotic prophylaxis for prevention of urinary tract infections in infants with high-grade hydronephrosis.

PubMed

Tu, H Y V; Pemberton, J; Lorenzo, A J; Braga, L H

2015-10-01

For infants with hydronephrosis, continuous antibiotic prophylaxis (CAP) may reduce urinary tract infections (UTIs); however, its value remains controversial. Recent studies have suggested that neonates with severe obstructive hydronephrosis are at an increased risk of UTIs, and support the use of CAP. Other studies have demonstrated the negligible risk for UTIs in the setting of suspected ureteropelvic junction obstruction and have highlighted the limited role of CAP in hydronephrosis. Furthermore, economic studies in this patient population have been sparse. This study aimed to evaluate whether the use of CAP is an efficient expenditure for preventing UTIs in children with high-grade hydronephrosis within the first 2 years of life. A decision model was used to estimate expected costs, clinical outcomes and quality-adjusted life years (QALYs) of CAP versus no CAP (Fig. 1). Cost data were collected from provincial databases and converted to 2013 Canadian dollars (CAD). Estimates of risks and health utility values were extracted from published literature. The analysis was performed over a time horizon of 2 years. One-way and probabilistic sensitivity analyses were carried out to assess uncertainty and robustness. Overall, CAP use was less costly and provided a minimal increase in health utility when compared to no CAP (Table). The mean cost over two years for CAP and no CAP was CAD$1571.19 and CAD$1956.44, respectively. The use of CAP reduced outpatient-managed UTIs by 0.21 infections and UTIs requiring hospitalization by 0.04 infections over 2 years. Cost-utility analysis revealed an increase of 0.0001 QALYs/year when using CAP. The CAP arm exhibited strong dominance over no CAP in all sensitivity analyses and across all willingness-to-pay thresholds. The use of CAP exhibited strong dominance in the economic evaluation, despite a small gain of 0.0001 QALYs/year. Whether this slight gain is clinically significant remains to be determined. However, small QALY gains have been reported in other pediatric economic evaluations. Strengths of this study included the use of data from a recent systematic review and meta-analysis, in addition to a comprehensive probabilistic sensitivity analysis. Limitations of this study included the use of estimates for UTI probabilities in the second year of life and health utility values, given that they were lacking in the literature. Spontaneous resolution of hydronephrosis and surgical management were also not implemented in this model. To prevent UTIs within the first 2 years of life in infants with high-grade hydronephrosis, this probabilistic model has shown that CAP use is a prudent expenditure of healthcare resources when compared to no CAP. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Probabilistic structural analysis of aerospace components using NESSUS

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Nagpal, Vinod K.; Chamis, Christos C.

1988-01-01

Probabilistic structural analysis of a Space Shuttle main engine turbopump blade is conducted using the computer code NESSUS (numerical evaluation of stochastic structures under stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade geometry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted assuming different failure models.

Cognitive Development Effects of Teaching Probabilistic Decision Making to Middle School Students

ERIC Educational Resources Information Center

Mjelde, James W.; Litzenberg, Kerry K.; Lindner, James R.

2011-01-01

This study investigated the comprehension and effectiveness of teaching formal, probabilistic decision-making skills to middle school students. Two specific objectives were to determine (1) if middle school students can comprehend a probabilistic decision-making approach, and (2) if exposure to the modeling approaches improves middle school…

Evaluating the uncertainty of predicting future climate time series at the hourly time scale

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2011-12-01

A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays.

PubMed

Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E

2018-06-01

This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fall 2014 SEI Research Review Probabilistic Analysis of Time Sensitive Systems

DTIC Science & Technology

2014-10-28

Osmosis SMC Tool Osmosis is a tool for Statistical Model Checking (SMC) with Semantic Importance Sampling. • Input model is written in subset of C...ASSERT() statements in model indicate conditions that must hold. • Input probability distributions defined by the user. • Osmosis returns the...on: – Target relative error, or – Set number of simulations Osmosis Main Algorithm 1 http://dreal.cs.cmu.edu/ (?⃑?): Indicator

Probabilistic arithmetic automata and their applications.

PubMed

Marschall, Tobias; Herms, Inke; Kaltenbach, Hans-Michael; Rahmann, Sven

2012-01-01

We present a comprehensive review on probabilistic arithmetic automata (PAAs), a general model to describe chains of operations whose operands depend on chance, along with two algorithms to numerically compute the distribution of the results of such probabilistic calculations. PAAs provide a unifying framework to approach many problems arising in computational biology and elsewhere. We present five different applications, namely 1) pattern matching statistics on random texts, including the computation of the distribution of occurrence counts, waiting times, and clump sizes under hidden Markov background models; 2) exact analysis of window-based pattern matching algorithms; 3) sensitivity of filtration seeds used to detect candidate sequence alignments; 4) length and mass statistics of peptide fragments resulting from enzymatic cleavage reactions; and 5) read length statistics of 454 and IonTorrent sequencing reads. The diversity of these applications indicates the flexibility and unifying character of the presented framework. While the construction of a PAA depends on the particular application, we single out a frequently applicable construction method: We introduce deterministic arithmetic automata (DAAs) to model deterministic calculations on sequences, and demonstrate how to construct a PAA from a given DAA and a finite-memory random text model. This procedure is used for all five discussed applications and greatly simplifies the construction of PAAs. Implementations are available as part of the MoSDi package. Its application programming interface facilitates the rapid development of new applications based on the PAA framework.

The Terrestrial Investigation Model: A probabilistic risk assessment model for birds exposed to pesticides

EPA Science Inventory

One of the major recommendations of the National Academy of Science to the USEPA, NMFS and USFWS was to utilize probabilistic methods when assessing the risks of pesticides to federally listed endangered and threatened species. The Terrestrial Investigation Model (TIM, version 3....

Exploring Term Dependences in Probabilistic Information Retrieval Model.

ERIC Educational Resources Information Center

Cho, Bong-Hyun; Lee, Changki; Lee, Gary Geunbae

2003-01-01

Describes a theoretic process to apply Bahadur-Lazarsfeld expansion (BLE) to general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on two standard document collections, one in Korean and one in English, it is demonstrated that incorporation of term dependences using BLE significantly contributes to performance…

A Probabilistic Model of Phonological Relationships from Contrast to Allophony

ERIC Educational Resources Information Center

Hall, Kathleen Currie

2009-01-01

This dissertation proposes a model of phonological relationships, the Probabilistic Phonological Relationship Model (PPRM), that quantifies how predictably distributed two sounds in a relationship are. It builds on a core premise of traditional phonological analysis, that the ability to define phonological relationships such as contrast and…

Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department.

PubMed

Brown, Samuel M; Jones, Jason; Kuttler, Kathryn Gibb; Keddington, Roger K; Allen, Todd L; Haug, Peter

2016-08-22

Sepsis is an often-fatal syndrome resulting from severe infection. Rapid identification and treatment are critical for septic patients. We therefore developed a probabilistic model to identify septic patients in the emergency department (ED). We aimed to produce a model that identifies 80 % of sepsis patients, with no more than 15 false positive alerts per day, within one hour of ED admission, using routine clinical data. We developed the model using retrospective data for 132,748 ED encounters (549 septic), with manual chart review to confirm cases of severe sepsis or septic shock from January 2006 through December 2008. A naïve Bayes model was used to select model features, starting with clinician-proposed candidate variables, which were then used to calculate the probability of sepsis. We evaluated the accuracy of the resulting model in 93,733 ED encounters from April 2009 through June 2010. The final model included mean blood pressure, temperature, age, heart rate, and white blood cell count. The area under the receiver operating characteristic curve (AUC) for the continuous predictor model was 0.953. The binary alert achieved 76.4 % sensitivity with a false positive rate of 4.7 %. We developed and validated a probabilistic model to identify sepsis early in an ED encounter. Despite changes in process, organizational focus, and the H1N1 influenza pandemic, our model performed adequately in our validation cohort, suggesting that it will be generalizable.

PubMed related articles: a probabilistic topic-based model for content similarity

PubMed Central

Lin, Jimmy; Wilbur, W John

2007-01-01

Background We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ® in MEDLINE ®. Results The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of pmra over bm25 in terms of precision. Conclusion Our experiments suggest that the pmra model provides an effective ranking algorithm for related article search. PMID:17971238

How happy is your web browsing? A model to quantify satisfaction of an Internet user searching for desired information

NASA Astrophysics Data System (ADS)

Banerji, Anirban; Magarkar, Aniket

2012-09-01

We feel happy when web browsing operations provide us with necessary information; otherwise, we feel bitter. How to measure this happiness (or bitterness)? How does the profile of happiness grow and decay during the course of web browsing? We propose a probabilistic framework that models the evolution of user satisfaction, on top of his/her continuous frustration at not finding the required information. It is found that the cumulative satisfaction profile of a web-searching individual can be modeled effectively as the sum of a random number of random terms, where each term is a mutually independent random variable, originating from ‘memoryless’ Poisson flow. Evolution of satisfaction over the entire time interval of a user’s browsing was modeled using auto-correlation analysis. A utilitarian marker, a magnitude of greater than unity of which describes happy web-searching operations, and an empirical limit that connects user’s satisfaction with his frustration level-are proposed too. The presence of pertinent information in the very first page of a website and magnitude of the decay parameter of user satisfaction (frustration, irritation etc.) are found to be two key aspects that dominate the web user’s psychology. The proposed model employed different combinations of decay parameter, searching time and number of helpful websites. The obtained results are found to match the results from three real-life case studies.

Exploratory study on a statistical method to analyse time resolved data obtained during nanomaterial exposure measurements

NASA Astrophysics Data System (ADS)

Clerc, F.; Njiki-Menga, G.-H.; Witschger, O.

2013-04-01

Most of the measurement strategies that are suggested at the international level to assess workplace exposure to nanomaterials rely on devices measuring, in real time, airborne particles concentrations (according different metrics). Since none of the instruments to measure aerosols can distinguish a particle of interest to the background aerosol, the statistical analysis of time resolved data requires special attention. So far, very few approaches have been used for statistical analysis in the literature. This ranges from simple qualitative analysis of graphs to the implementation of more complex statistical models. To date, there is still no consensus on a particular approach and the current period is always looking for an appropriate and robust method. In this context, this exploratory study investigates a statistical method to analyse time resolved data based on a Bayesian probabilistic approach. To investigate and illustrate the use of the this statistical method, particle number concentration data from a workplace study that investigated the potential for exposure via inhalation from cleanout operations by sandpapering of a reactor producing nanocomposite thin films have been used. In this workplace study, the background issue has been addressed through the near-field and far-field approaches and several size integrated and time resolved devices have been used. The analysis of the results presented here focuses only on data obtained with two handheld condensation particle counters. While one was measuring at the source of the released particles, the other one was measuring in parallel far-field. The Bayesian probabilistic approach allows a probabilistic modelling of data series, and the observed task is modelled in the form of probability distributions. The probability distributions issuing from time resolved data obtained at the source can be compared with the probability distributions issuing from the time resolved data obtained far-field, leading in a quantitative estimation of the airborne particles released at the source when the task is performed. Beyond obtained results, this exploratory study indicates that the analysis of the results requires specific experience in statistics.

Joint probabilistic determination of earthquake location and velocity structure: application to local and regional events

NASA Astrophysics Data System (ADS)

Beucler, E.; Haugmard, M.; Mocquet, A.

2016-12-01

The most widely used inversion schemes to locate earthquakes are based on iterative linearized least-squares algorithms and using an a priori knowledge of the propagation medium. When a small amount of observations is available for moderate events for instance, these methods may lead to large trade-offs between outputs and both the velocity model and the initial set of hypocentral parameters. We present a joint structure-source determination approach using Bayesian inferences. Monte-Carlo continuous samplings, using Markov chains, generate models within a broad range of parameters, distributed according to the unknown posterior distributions. The non-linear exploration of both the seismic structure (velocity and thickness) and the source parameters relies on a fast forward problem using 1-D travel time computations. The a posteriori covariances between parameters (hypocentre depth, origin time and seismic structure among others) are computed and explicitly documented. This method manages to decrease the influence of the surrounding seismic network geometry (sparse and/or azimuthally inhomogeneous) and a too constrained velocity structure by inferring realistic distributions on hypocentral parameters. Our algorithm is successfully used to accurately locate events of the Armorican Massif (western France), which is characterized by moderate and apparently diffuse local seismicity.

A probabilistic seismic model for the European Arctic

NASA Astrophysics Data System (ADS)

Hauser, Juerg; Dyer, Kathleen M.; Pasyanos, Michael E.; Bungum, Hilmar; Faleide, Jan I.; Clark, Stephen A.; Schweitzer, Johannes

2011-01-01

The development of three-dimensional seismic models for the crust and upper mantle has traditionally focused on finding one model that provides the best fit to the data while observing some regularization constraints. In contrast to this, the inversion employed here fits the data in a probabilistic sense and thus provides a quantitative measure of model uncertainty. Our probabilistic model is based on two sources of information: (1) prior information, which is independent from the data, and (2) different geophysical data sets, including thickness constraints, velocity profiles, gravity data, surface wave group velocities, and regional body wave traveltimes. We use a Markov chain Monte Carlo (MCMC) algorithm to sample models from the prior distribution, the set of plausible models, and test them against the data to generate the posterior distribution, the ensemble of models that fit the data with assigned uncertainties. While being computationally more expensive, such a probabilistic inversion provides a more complete picture of solution space and allows us to combine various data sets. The complex geology of the European Arctic, encompassing oceanic crust, continental shelf regions, rift basins and old cratonic crust, as well as the nonuniform coverage of the region by data with varying degrees of uncertainty, makes it a challenging setting for any imaging technique and, therefore, an ideal environment for demonstrating the practical advantages of a probabilistic approach. Maps of depth to basement and depth to Moho derived from the posterior distribution are in good agreement with previously published maps and interpretations of the regional tectonic setting. The predicted uncertainties, which are as important as the absolute values, correlate well with the variations in data coverage and quality in the region. A practical advantage of our probabilistic model is that it can provide estimates for the uncertainties of observables due to model uncertainties. We will demonstrate how this can be used for the formulation of earthquake location algorithms that take model uncertainties into account when estimating location uncertainties.

Evaluating the Performance of a Climate-Driven Mortality Model during Heat Waves and Cold Spells in Europe

PubMed Central

Lowe, Rachel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier

2015-01-01

The impact of climate change on human health is a serious concern. In particular, changes in the frequency and intensity of heat waves and cold spells are of high relevance in terms of mortality and morbidity. This demonstrates the urgent need for reliable early-warning systems to help authorities prepare and respond to emergency situations. In this study, we evaluate the performance of a climate-driven mortality model to provide probabilistic predictions of exceeding emergency mortality thresholds for heat wave and cold spell scenarios. Daily mortality data corresponding to 187 NUTS2 regions across 16 countries in Europe were obtained from 1998–2003. Data were aggregated to 54 larger regions in Europe, defined according to similarities in population structure and climate. Location-specific average mortality rates, at given temperature intervals over the time period, were modelled to account for the increased mortality observed during both high and low temperature extremes and differing comfort temperatures between regions. Model parameters were estimated in a Bayesian framework, in order to generate probabilistic simulations of mortality across Europe for time periods of interest. For the heat wave scenario (1–15 August 2003), the model was successfully able to anticipate the occurrence or non-occurrence of mortality rates exceeding the emergency threshold (75th percentile of the mortality distribution) for 89% of the 54 regions, given a probability decision threshold of 70%. For the cold spell scenario (1–15 January 2003), mortality events in 69% of the regions were correctly anticipated with a probability decision threshold of 70%. By using a more conservative decision threshold of 30%, this proportion increased to 87%. Overall, the model performed better for the heat wave scenario. By replacing observed temperature data in the model with forecast temperature, from state-of-the-art European forecasting systems, probabilistic mortality predictions could potentially be made several months ahead of imminent heat waves and cold spells. PMID:25625407

A Moore's cellular automaton model to get probabilistic seismic hazard maps for different magnitude releases: A case study for Greece

NASA Astrophysics Data System (ADS)

Jiménez, A.; Posadas, A. M.

2006-09-01

Cellular automata are simple mathematical idealizations of natural systems and they supply useful models for many investigations in natural science. Examples include sandpile models, forest fire models, and slider block models used in seismology. In the present paper, they have been used for establishing temporal relations between the energy releases of the seismic events that occurred in neighboring parts of the crust. The catalogue is divided into time intervals, and the region is divided into cells which are declared active or inactive by means of a threshold energy release criterion. Thus, a pattern of active and inactive cells which evolves over time is determined. A stochastic cellular automaton is constructed starting with these patterns, in order to simulate their spatio-temporal evolution, by supposing a Moore's neighborhood interaction between the cells. The best model is chosen by maximizing the mutual information between the past and the future states. Finally, a Probabilistic Seismic Hazard Map is given for the different energy releases considered. The method has been applied to the Greece catalogue from 1900 to 1999. The Probabilistic Seismic Hazard Maps for energies corresponding to m = 4 and m = 5 are close to the real seismicity after the data in that area, and they correspond to a background seismicity in the whole area. This background seismicity seems to cover the whole area in periods of around 25-50 years. The optimum cell size is in agreement with other studies; for m > 6 the optimum area increases according to the threshold of clear spatial resolution, and the active cells are not so clustered. The results are coherent with other hazard studies in the zone and with the seismicity recorded after the data set, as well as provide an interaction model which points out the large scale nature of the earthquake occurrence.

Evaluating the performance of a climate-driven mortality model during heat waves and cold spells in Europe.

PubMed

Lowe, Rachel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R; Rodó, Xavier

2015-01-23

The impact of climate change on human health is a serious concern. In particular, changes in the frequency and intensity of heat waves and cold spells are of high relevance in terms of mortality and morbidity. This demonstrates the urgent need for reliable early-warning systems to help authorities prepare and respond to emergency situations. In this study, we evaluate the performance of a climate-driven mortality model to provide probabilistic predictions of exceeding emergency mortality thresholds for heat wave and cold spell scenarios. Daily mortality data corresponding to 187 NUTS2 regions across 16 countries in Europe were obtained from 1998-2003. Data were aggregated to 54 larger regions in Europe, defined according to similarities in population structure and climate. Location-specific average mortality rates, at given temperature intervals over the time period, were modelled to account for the increased mortality observed during both high and low temperature extremes and differing comfort temperatures between regions. Model parameters were estimated in a Bayesian framework, in order to generate probabilistic simulations of mortality across Europe for time periods of interest. For the heat wave scenario (1-15 August 2003), the model was successfully able to anticipate the occurrence or non-occurrence of mortality rates exceeding the emergency threshold (75th percentile of the mortality distribution) for 89% of the 54 regions, given a probability decision threshold of 70%. For the cold spell scenario (1-15 January 2003), mortality events in 69% of the regions were correctly anticipated with a probability decision threshold of 70%. By using a more conservative decision threshold of 30%, this proportion increased to 87%. Overall, the model performed better for the heat wave scenario. By replacing observed temperature data in the model with forecast temperature, from state-of-the-art European forecasting systems, probabilistic mortality predictions could potentially be made several months ahead of imminent heat waves and cold spells.

The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes

ERIC Educational Resources Information Center

Robusto, Egidio; Stefanutti, Luca; Anselmi, Pasquale

2010-01-01

Within the theoretical framework of knowledge space theory, a probabilistic skill multimap model for assessing learning processes is proposed. The learning process of a student is modeled as a function of the student's knowledge and of an educational intervention on the attainment of specific skills required to solve problems in a knowledge…

Probabilistic models of eukaryotic evolution: time for integration

PubMed Central

Lartillot, Nicolas

2015-01-01

In spite of substantial work and recent progress, a global and fully resolved picture of the macroevolutionary history of eukaryotes is still under construction. This concerns not only the phylogenetic relations among major groups, but also the general characteristics of the underlying macroevolutionary processes, including the patterns of gene family evolution associated with endosymbioses, as well as their impact on the sequence evolutionary process. All these questions raise formidable methodological challenges, calling for a more powerful statistical paradigm. In this direction, model-based probabilistic approaches have played an increasingly important role. In particular, improved models of sequence evolution accounting for heterogeneities across sites and across lineages have led to significant, although insufficient, improvement in phylogenetic accuracy. More recently, one main trend has been to move away from simple parametric models and stepwise approaches, towards integrative models explicitly considering the intricate interplay between multiple levels of macroevolutionary processes. Such integrative models are in their infancy, and their application to the phylogeny of eukaryotes still requires substantial improvement of the underlying models, as well as additional computational developments. PMID:26323768

A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Krishnan, Venkat K; Zhang, Jie

Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertaintymore » and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.« less

The Role of Working Memory in the Probabilistic Inference of Future Sensory Events.

PubMed

Cashdollar, Nathan; Ruhnau, Philipp; Weisz, Nathan; Hasson, Uri

2017-05-01

The ability to represent the emerging regularity of sensory information from the external environment has been thought to allow one to probabilistically infer future sensory occurrences and thus optimize behavior. However, the underlying neural implementation of this process is still not comprehensively understood. Through a convergence of behavioral and neurophysiological evidence, we establish that the probabilistic inference of future events is critically linked to people's ability to maintain the recent past in working memory. Magnetoencephalography recordings demonstrated that when visual stimuli occurring over an extended time series had a greater statistical regularity, individuals with higher working-memory capacity (WMC) displayed enhanced slow-wave neural oscillations in the θ frequency band (4-8 Hz.) prior to, but not during stimulus appearance. This prestimulus neural activity was specifically linked to contexts where information could be anticipated and influenced the preferential sensory processing for this visual information after its appearance. A separate behavioral study demonstrated that this process intrinsically emerges during continuous perception and underpins a realistic advantage for efficient behavioral responses. In this way, WMC optimizes the anticipation of higher level semantic concepts expected to occur in the near future. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Web-Based System for Bayesian Benchmark Dose Estimation.

PubMed

Shao, Kan; Shapiro, Andrew J

2018-01-11

Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.

Model of Mixing Layer With Multicomponent Evaporating Drops

NASA Technical Reports Server (NTRS)

Bellan, Josette; Le Clercq, Patrick

2004-01-01

A mathematical model of a three-dimensional mixing layer laden with evaporating fuel drops composed of many chemical species has been derived. The study is motivated by the fact that typical real petroleum fuels contain hundreds of chemical species. Previously, for the sake of computational efficiency, spray studies were performed using either models based on a single representative species or models based on surrogate fuels of at most 15 species. The present multicomponent model makes it possible to perform more realistic simulations by accounting for hundreds of chemical species in a computationally efficient manner. The model is used to perform Direct Numerical Simulations in continuing studies directed toward understanding the behavior of liquid petroleum fuel sprays. The model includes governing equations formulated in an Eulerian and a Lagrangian reference frame for the gas and the drops, respectively. This representation is consistent with the expected volumetrically small loading of the drops in gas (of the order of 10 3), although the mass loading can be substantial because of the high ratio (of the order of 103) between the densities of liquid and gas. The drops are treated as point sources of mass, momentum, and energy; this representation is consistent with the drop size being smaller than the Kolmogorov scale. Unsteady drag, added-mass effects, Basset history forces, and collisions between the drops are neglected, and the gas is assumed calorically perfect. The model incorporates the concept of continuous thermodynamics, according to which the chemical composition of a fuel is described probabilistically, by use of a distribution function. Distribution functions generally depend on many parameters. However, for mixtures of homologous species, the distribution can be approximated with acceptable accuracy as a sole function of the molecular weight. The mixing layer is initially laden with drops in its lower stream, and the drops are colder than the gas. Drop evaporation leads to a change in the gas-phase composition, which, like the composition of the drops, is described in a probabilistic manner

A PROBABILISTIC MODELING FRAMEWORK FOR PREDICTING POPULATION EXPOSURES TO BENZENE

EPA Science Inventory

The US Environmental Protection Agency (EPA) is modifying their probabilistic Stochastic Human Exposure Dose Simulation (SHEDS) model to assess aggregate exposures to air toxics. Air toxics include urban Hazardous Air Pollutants (HAPS) such as benzene from mobile sources, part...

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

Probabilistic seismic hazard in the San Francisco Bay area based on a simplified viscoelastic cycle model of fault interactions

USGS Publications Warehouse

Pollitz, F.F.; Schwartz, D.P.

2008-01-01

We construct a viscoelastic cycle model of plate boundary deformation that includes the effect of time-dependent interseismic strain accumulation, coseismic strain release, and viscoelastic relaxation of the substrate beneath the seismogenic crust. For a given fault system, time-averaged stress changes at any point (not on a fault) are constrained to zero; that is, kinematic consistency is enforced for the fault system. The dates of last rupture, mean recurrence times, and the slip distributions of the (assumed) repeating ruptures are key inputs into the viscoelastic cycle model. This simple formulation allows construction of stress evolution at all points in the plate boundary zone for purposes of probabilistic seismic hazard analysis (PSHA). Stress evolution is combined with a Coulomb failure stress threshold at representative points on the fault segments to estimate the times of their respective future ruptures. In our PSHA we consider uncertainties in a four-dimensional parameter space: the rupture peridocities, slip distributions, time of last earthquake (for prehistoric ruptures) and Coulomb failure stress thresholds. We apply this methodology to the San Francisco Bay region using a recently determined fault chronology of area faults. Assuming single-segment rupture scenarios, we find that fature rupture probabilities of area faults in the coming decades are the highest for the southern Hayward, Rodgers Creek, and northern Calaveras faults. This conclusion is qualitatively similar to that of Working Group on California Earthquake Probabilities, but the probabilities derived here are significantly higher. Given that fault rupture probabilities are highly model-dependent, no single model should be used to assess to time-dependent rupture probabilities. We suggest that several models, including the present one, be used in a comprehensive PSHA methodology, as was done by Working Group on California Earthquake Probabilities.

Super Ensemble-based Aviation Turbulence Guidance (SEATG) for Air Traffic Management (ATM)

NASA Astrophysics Data System (ADS)

Kim, Jung-Hoon; Chan, William; Sridhar, Banavar; Sharman, Robert

2014-05-01

Super Ensemble (ensemble of ten turbulence metrics from time-lagged ensemble members of weather forecast data)-based Aviation Turbulence Guidance (SEATG) is developed using Weather Research and Forecasting (WRF) model and in-situ eddy dissipation rate (EDR) observations equipped on commercial aircraft over the contiguous United States. SEATG is a sequence of five procedures including weather modeling, calculating turbulence metrics, mapping EDR-scale, evaluating metrics, and producing final SEATG forecast. This uses similar methodology to the operational Graphic Turbulence Guidance (GTG) with three major improvements. First, SEATG use a higher resolution (3-km) WRF model to capture cloud-resolving scale phenomena. Second, SEATG computes turbulence metrics for multiple forecasts that are combined at the same valid time resulting in an time-lagged ensemble of multiple turbulence metrics. Third, SEATG provides both deterministic and probabilistic turbulence forecasts to take into account weather uncertainties and user demands. It is found that the SEATG forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on 7-8 Sep 2012. And, overall performance skill of deterministic SEATG against the observed EDR data during this period is superior to any single turbulence metrics. Finally, probabilistic SEATG is used as an example application of turbulence forecast for air-traffic management. In this study, a simple Wind-Optimal Route (WOR) passing through the potential areas of probabilistic SEATG and Lateral Turbulence Avoidance Route (LTAR) taking into account the SEATG are calculated at z = 35000 ft (z = 12 km) from Los Angeles to John F. Kennedy international airports. As a result, WOR takes total of 239 minutes with 16 minutes of SEATG areas for 40% of moderate turbulence potential, while LTAR takes total of 252 minutes travel time that 5% of fuel would be additionally consumed to entirely avoid the moderate SEATG regions.

Probabilistic Usage of the Multi-Factor Interaction Model

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A Multi-Factor Interaction Model (MFIM) is used to predict the insulating foam mass expulsion during the ascending of a space vehicle. The exponents in the MFIM are evaluated by an available approach which consists of least squares and an optimization algorithm. These results were subsequently used to probabilistically evaluate the effects of the uncertainties in each participating factor in the mass expulsion. The probabilistic results show that the surface temperature dominates at high probabilities and the pressure which causes the mass expulsion at low probabil

Probabilistic Neighborhood-Based Data Collection Algorithms for 3D Underwater Acoustic Sensor Networks

PubMed Central

Han, Guangjie; Li, Shanshan; Zhu, Chunsheng; Jiang, Jinfang; Zhang, Wenbo

2017-01-01

Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency. PMID:28208735

Evaluating sub-seasonal skill in probabilistic forecasts of Atmospheric Rivers and associated extreme events

NASA Astrophysics Data System (ADS)

Subramanian, A. C.; Lavers, D.; Matsueda, M.; Shukla, S.; Cayan, D. R.; Ralph, M.

2017-12-01

Atmospheric rivers (ARs) - elongated plumes of intense moisture transport - are a primary source of hydrological extremes, water resources and impactful weather along the West Coast of North America and Europe. There is strong demand in the water management, societal infrastructure and humanitarian sectors for reliable sub-seasonal forecasts, particularly of extreme events, such as floods and droughts so that actions to mitigate disastrous impacts can be taken with sufficient lead-time. Many recent studies have shown that ARs in the Pacific and the Atlantic are modulated by large-scale modes of climate variability. Leveraging the improved understanding of how these large-scale climate modes modulate the ARs in these two basins, we use the state-of-the-art multi-model forecast systems such as the North American Multi-Model Ensemble (NMME) and the Subseasonal-to-Seasonal (S2S) database to help inform and assess the probabilistic prediction of ARs and related extreme weather events over the North American and European West Coasts. We will present results from evaluating probabilistic forecasts of extreme precipitation and AR activity at the sub-seasonal scale. In particular, results from the comparison of two winters (2015-16 and 2016-17) will be shown, winters which defied canonical El Niño teleconnection patterns over North America and Europe. We further extend this study to analyze probabilistic forecast skill of AR events in these two basins and the variability in forecast skill during certain regimes of large-scale climate modes.

The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms.

PubMed

Biehler, J; Wall, W A

2018-02-01

If computational models are ever to be used in high-stakes decision making in clinical practice, the use of personalized models and predictive simulation techniques is a must. This entails rigorous quantification of uncertainties as well as harnessing available patient-specific data to the greatest extent possible. Although researchers are beginning to realize that taking uncertainty in model input parameters into account is a necessity, the predominantly used probabilistic description for these uncertain parameters is based on elementary random variable models. In this work, we set out for a comparison of different probabilistic models for uncertain input parameters using the example of an uncertain wall thickness in finite element models of abdominal aortic aneurysms. We provide the first comparison between a random variable and a random field model for the aortic wall and investigate the impact on the probability distribution of the computed peak wall stress. Moreover, we show that the uncertainty about the prevailing peak wall stress can be reduced if noninvasively available, patient-specific data are harnessed for the construction of the probabilistic wall thickness model. Copyright © 2017 John Wiley & Sons, Ltd.

From Cyclone Tracks to the Costs of European Winter Storms: A Probabilistic Loss Assessment Model

NASA Astrophysics Data System (ADS)

Orwig, K.; Renggli, D.; Corti, T.; Reese, S.; Wueest, M.; Viktor, E.; Zimmerli, P.

2014-12-01

European winter storms cause billions of dollars of insured losses every year. Therefore, it is essential to understand potential impacts of future events, and the role reinsurance can play to mitigate the losses. The authors will present an overview on natural catastrophe risk assessment modeling in the reinsurance industry, and the development of a new innovative approach for modeling the risk associated with European winter storms.The new innovative approach includes the development of physically meaningful probabilistic (i.e. simulated) events for European winter storm loss assessment. The meteorological hazard component of the new model is based on cyclone and windstorm tracks identified in the 20thCentury Reanalysis data. The knowledge of the evolution of winter storms both in time and space allows the physically meaningful perturbation of historical event properties (e.g. track, intensity, etc.). The perturbation includes a random element but also takes the local climatology and the evolution of the historical event into account.The low-resolution wind footprints taken from the 20thCentury Reanalysis are processed by a statistical-dynamical downscaling to generate high-resolution footprints for both the simulated and historical events. Downscaling transfer functions are generated using ENSEMBLES regional climate model data. The result is a set of reliable probabilistic events representing thousands of years. The event set is then combined with country and site-specific vulnerability functions and detailed market- or client-specific information to compute annual expected losses.

A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium

PubMed Central

Parker, Aimée; Pin, Carmen; Carding, Simon R.; Watson, Alastair J. M.; Byrne, Helen M.

2017-01-01

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions—uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales. PMID:28753601

A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium.

PubMed

Maclaren, Oliver J; Parker, Aimée; Pin, Carmen; Carding, Simon R; Watson, Alastair J M; Fletcher, Alexander G; Byrne, Helen M; Maini, Philip K

2017-07-01

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions-uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

PubMed

Herzallah, Randa

2015-03-01

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison of bias analysis strategies applied to a large data set.

PubMed

Lash, Timothy L; Abrams, Barbara; Bodnar, Lisa M

2014-07-01

Epidemiologic data sets continue to grow larger. Probabilistic-bias analyses, which simulate hundreds of thousands of replications of the original data set, may challenge desktop computational resources. We implemented a probabilistic-bias analysis to evaluate the direction, magnitude, and uncertainty of the bias arising from misclassification of prepregnancy body mass index when studying its association with early preterm birth in a cohort of 773,625 singleton births. We compared 3 bias analysis strategies: (1) using the full cohort, (2) using a case-cohort design, and (3) weighting records by their frequency in the full cohort. Underweight and overweight mothers were more likely to deliver early preterm. A validation substudy demonstrated misclassification of prepregnancy body mass index derived from birth certificates. Probabilistic-bias analyses suggested that the association between underweight and early preterm birth was overestimated by the conventional approach, whereas the associations between overweight categories and early preterm birth were underestimated. The 3 bias analyses yielded equivalent results and challenged our typical desktop computing environment. Analyses applied to the full cohort, case cohort, and weighted full cohort required 7.75 days and 4 terabytes, 15.8 hours and 287 gigabytes, and 8.5 hours and 202 gigabytes, respectively. Large epidemiologic data sets often include variables that are imperfectly measured, often because data were collected for other purposes. Probabilistic-bias analysis allows quantification of errors but may be difficult in a desktop computing environment. Solutions that allow these analyses in this environment can be achieved without new hardware and within reasonable computational time frames.

Modeling and analysis of cell membrane systems with probabilistic model checking

PubMed Central

2011-01-01

Background Recently there has been a growing interest in the application of Probabilistic Model Checking (PMC) for the formal specification of biological systems. PMC is able to exhaustively explore all states of a stochastic model and can provide valuable insight into its behavior which are more difficult to see using only traditional methods for system analysis such as deterministic and stochastic simulation. In this work we propose a stochastic modeling for the description and analysis of sodium-potassium exchange pump. The sodium-potassium pump is a membrane transport system presents in all animal cell and capable of moving sodium and potassium ions against their concentration gradient. Results We present a quantitative formal specification of the pump mechanism in the PRISM language, taking into consideration a discrete chemistry approach and the Law of Mass Action aspects. We also present an analysis of the system using quantitative properties in order to verify the pump reversibility and understand the pump behavior using trend labels for the transition rates of the pump reactions. Conclusions Probabilistic model checking can be used along with other well established approaches such as simulation and differential equations to better understand pump behavior. Using PMC we can determine if specific events happen such as the potassium outside the cell ends in all model traces. We can also have a more detailed perspective on its behavior such as determining its reversibility and why its normal operation becomes slow over time. This knowledge can be used to direct experimental research and make it more efficient, leading to faster and more accurate scientific discoveries. PMID:22369714

Assessment of SWE data assimilation for ensemble streamflow predictions

NASA Astrophysics Data System (ADS)

Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

2014-11-01

An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

ERIC Educational Resources Information Center

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

QUANTIFYING AGGREGATE CHLORPYRIFOS EXPOSURE AND DOSE TO CHILDREN USING A PHYSICALLY-BASED TWO-STAGE MONTE CARLO PROBABILISTIC MODEL

EPA Science Inventory

To help address the Food Quality Protection Act of 1996, a physically-based, two-stage Monte Carlo probabilistic model has been developed to quantify and analyze aggregate exposure and dose to pesticides via multiple routes and pathways. To illustrate model capabilities and ide...

Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

NASA Technical Reports Server (NTRS)

Hojnicki, Jeffrey S.; Rusick, Jeffrey J.

2005-01-01

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data

ERIC Educational Resources Information Center

Yang, Shuo

2017-01-01

With the expeditious advancement of information technologies, health-related data presented unprecedented potentials for medical and health discoveries but at the same time significant challenges for machine learning techniques both in terms of size and complexity. Those challenges include: the structured data with various storage formats and…

Probabilistic Guidance of Swarms using Sequential Convex Programming

DTIC Science & Technology

2014-01-01

quadcopter fleet [24]. In this paper, sequential convex programming (SCP) [25] is implemented using model predictive control (MPC) to provide real-time...in order to make Problem 1 convex. The details for convexifying this problem can be found in [26]. The main steps are discretizing the problem using

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

NASA Astrophysics Data System (ADS)

Rabatel, Matthias; Rampal, Pierre; Carrassi, Alberto; Bertino, Laurent; Jones, Christopher K. R. T.

2018-03-01

We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

Near-real time 3D probabilistic earthquakes locations at Mt. Etna volcano

NASA Astrophysics Data System (ADS)

Barberi, G.; D'Agostino, M.; Mostaccio, A.; Patane', D.; Tuve', T.

2012-04-01

Automatic procedure for locating earthquake in quasi-real time must provide a good estimation of earthquakes location within a few seconds after the event is first detected and is strongly needed for seismic warning system. The reliability of an automatic location algorithm is influenced by several factors such as errors in picking seismic phases, network geometry, and velocity model uncertainties. On Mt. Etna, the seismic network is managed by INGV and the quasi-real time earthquakes locations are performed by using an automatic-picking algorithm based on short-term-average to long-term-average ratios (STA/LTA) calculated from an approximate squared envelope function of the seismogram, which furnish a list of P-wave arrival times, and the location algorithm Hypoellipse, with a 1D velocity model. The main purpose of this work is to investigate the performances of a different automatic procedure to improve the quasi-real time earthquakes locations. In fact, as the automatic data processing may be affected by outliers (wrong picks), the use of a traditional earthquake location techniques based on a least-square misfit function (L2-norm) often yield unstable and unreliable solutions. Moreover, on Mt. Etna, the 1D model is often unable to represent the complex structure of the volcano (in particular the strong lateral heterogeneities), whereas the increasing accuracy in the 3D velocity models at Mt. Etna during recent years allows their use today in routine earthquake locations. Therefore, we selected, as reference locations, all the events occurred on Mt. Etna in the last year (2011) which was automatically detected and located by means of the Hypoellipse code. By using this dataset (more than 300 events), we applied a nonlinear probabilistic earthquake location algorithm using the Equal Differential Time (EDT) likelihood function, (Font et al., 2004; Lomax, 2005) which is much more robust in the presence of outliers in the data. Successively, by using a probabilistic non linear method (NonLinLoc, Lomax, 2001) and the 3D velocity model, derived from the one developed by Patanè et al. (2006) integrated with that obtained by Chiarabba et al. (2004), we obtained the best possible constraint on the location of the focii expressed as a probability density function (PDF) for the hypocenter location in 3D space. As expected, the obtained results, compared with the reference ones, show that the NonLinLoc software (applied to a 3D velocity model) is more reliable than the Hypoellipse code (applied to layered 1D velocity models), leading to more reliable automatic locations also when outliers are present.

Probabilistic Reward- and Punishment-based Learning in Opioid Addiction: Experimental and Computational Data

PubMed Central

Myers, Catherine E.; Sheynin, Jony; Baldson, Tarryn; Luzardo, Andre; Beck, Kevin D.; Hogarth, Lee; Haber, Paul; Moustafa, Ahmed A.

2016-01-01

Addiction is the continuation of a habit in spite of negative consequences. A vast literature gives evidence that this poor decision-making behavior in individuals addicted to drugs also generalizes to laboratory decision making tasks, suggesting that the impairment in decision-making is not limited to decisions about taking drugs. In the current experiment, opioid-addicted individuals and matched controls with no history of illicit drug use were administered a probabilistic classification task that embeds both reward-based and punishment-based learning trials, and a computational model of decision making was applied to understand the mechanisms describing individuals’ performance on the task. Although behavioral results showed thatopioid-addicted individuals performed as well as controls on both reward- and punishment-based learning, the modeling results suggested subtle differences in how decisions were made between the two groups. Specifically, the opioid-addicted group showed decreased tendency to repeat prior responses, meaning that they were more likely to “chase reward” when expectancies were violated, whereas controls were more likely to stick with a previously-successful response rule, despite occasional expectancy violations. This tendency to chase short-term reward, potentially at the expense of developing rules that maximize reward over the long term, may be a contributing factor to opioid addiction. Further work is indicated to better understand whether this tendency arises as a result of brain changes in the wake of continued opioid use/abuse, or might be a pre-existing factor that may contribute to risk for addiction. PMID:26381438

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

PubMed

Nagarajan, Mahesh B; Raman, Steven S; Lo, Pechin; Lin, Wei-Chan; Khoshnoodi, Pooria; Sayre, James W; Ramakrishna, Bharath; Ahuja, Preeti; Huang, Jiaoti; Margolis, Daniel J A; Lu, David S K; Reiter, Robert E; Goldin, Jonathan G; Brown, Matthew S; Enzmann, Dieter R

2018-02-19

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Shao, Zhongshi; Pi, Dechang; Shao, Weishi

2017-11-01

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

Longitudinal temporal and probabilistic prediction of survival in a cohort of patients with advanced cancer.

PubMed

Perez-Cruz, Pedro E; Dos Santos, Renata; Silva, Thiago Buosi; Crovador, Camila Souza; Nascimento, Maria Salete de Angelis; Hall, Stacy; Fajardo, Julieta; Bruera, Eduardo; Hui, David

2014-11-01

Survival prognostication is important during the end of life. The accuracy of clinician prediction of survival (CPS) over time has not been well characterized. The aims of the study were to examine changes in prognostication accuracy during the last 14 days of life in a cohort of patients with advanced cancer admitted to two acute palliative care units and to compare the accuracy between the temporal and probabilistic approaches. Physicians and nurses prognosticated survival daily for cancer patients in two hospitals until death/discharge using two prognostic approaches: temporal and probabilistic. We assessed accuracy for each method daily during the last 14 days of life comparing accuracy at Day -14 (baseline) with accuracy at each time point using a test of proportions. A total of 6718 temporal and 6621 probabilistic estimations were provided by physicians and nurses for 311 patients, respectively. Median (interquartile range) survival was 8 days (4-20 days). Temporal CPS had low accuracy (10%-40%) and did not change over time. In contrast, probabilistic CPS was significantly more accurate (P < .05 at each time point) but decreased close to death. Probabilistic CPS was consistently more accurate than temporal CPS over the last 14 days of life; however, its accuracy decreased as patients approached death. Our findings suggest that better tools to predict impending death are necessary. Copyright © 2014 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

ENSURF: multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions

NASA Astrophysics Data System (ADS)

Pérez, B.; Brouwer, R.; Beckers, J.; Paradis, D.; Balseiro, C.; Lyons, K.; Cure, M.; Sotillo, M. G.; Hackett, B.; Verlaan, M.; Fanjul, E. A.

2012-03-01

ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.

Simulation of extreme reservoir level distribution with the SCHADEX method (EXTRAFLO project)

NASA Astrophysics Data System (ADS)

Paquet, Emmanuel; Penot, David; Garavaglia, Federico

2013-04-01

The standard practice for the design of dam spillways structures and gates is to consider the maximum reservoir level reached for a given hydrologic scenario. This scenario has several components: peak discharge, flood volumes on different durations, discharge gradients etc. Within a probabilistic analysis framework, several scenarios can be associated with different return times, although a reference return level (e.g. 1000 years) is often prescribed by the local regulation rules or usual practice. Using continuous simulation method for extreme flood estimation is a convenient solution to provide a great variety of hydrological scenarios to feed a hydraulic model of dam operation: flood hydrographs are explicitly simulated by a rainfall-runoff model fed by a stochastic rainfall generator. The maximum reservoir level reached will be conditioned by the scale and the dynamics of the generated hydrograph, by the filling of the reservoir prior to the flood, and by the dam gates and spillway operation during the event. The simulation of a great number of floods will allow building a probabilistic distribution of maximum reservoir levels. A design value can be chosen at a definite return level. An alternative approach is proposed here, based on the SCHADEX method for extreme flood estimation, proposed by Paquet et al. (2006, 2013). SCHADEX is a so-called "semi-continuous" stochastic simulation method in that flood events are simulated on an event basis and are superimposed on a continuous simulation of the catchment saturation hazard using rainfall-runoff modelling. The SCHADEX process works at the study time-step (e.g. daily), and the peak flow distribution is deduced from the simulated daily flow distribution by a peak-to-volume ratio. A reference hydrograph relevant for extreme floods is proposed. In the standard version of the method, both the peak-to-volume and the reference hydrograph are constant. An enhancement of this method is presented, with variable peak-to-volume ratios and hydrographs applied to each simulated event. This allows accounting for different flood dynamics, depending on the season, the generating precipitation event, the soil saturation state, etc. In both cases, a hydraulic simulation of dam operation is performed, in order to compute the distribution of maximum reservoir levels. Results are detailed for an extreme return level, showing that a 1000 years return level reservoir level can be reached during flood events whose components (peaks, volumes) are not necessarily associated with such return level. The presentation will be illustrated by the example of a fictive dam on the Tech River at Reynes (South of France, 477 km²). This study has been carried out within the EXTRAFLO project, Task 8 (https://extraflo.cemagref.fr/). References: Paquet, E., Gailhard, J. and Garçon, R. (2006), Evolution of the GRADEX method: improvement by atmospheric circulation classification and hydrological modeling, La Houille Blanche, 5, 80-90. doi:10.1051/lhb:2006091. Paquet, E., Garavaglia, F., Garçon, R. and Gailhard, J. (2012), The SCHADEX method: a semi-continuous rainfall-runoff simulation for extreme food estimation, Journal of Hydrology, under revision

Effects of sample survey design on the accuracy of classification tree models in species distribution models

Treesearch

Thomas C. Edwards; D. Richard Cutler; Niklaus E. Zimmermann; Linda Geiser; Gretchen G. Moisen

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by...

Probabilistic design of fibre concrete structures

NASA Astrophysics Data System (ADS)

Pukl, R.; Novák, D.; Sajdlová, T.; Lehký, D.; Červenka, J.; Červenka, V.

2017-09-01

Advanced computer simulation is recently well-established methodology for evaluation of resistance of concrete engineering structures. The nonlinear finite element analysis enables to realistically predict structural damage, peak load, failure, post-peak response, development of cracks in concrete, yielding of reinforcement, concrete crushing or shear failure. The nonlinear material models can cover various types of concrete and reinforced concrete: ordinary concrete, plain or reinforced, without or with prestressing, fibre concrete, (ultra) high performance concrete, lightweight concrete, etc. Advanced material models taking into account fibre concrete properties such as shape of tensile softening branch, high toughness and ductility are described in the paper. Since the variability of the fibre concrete material properties is rather high, the probabilistic analysis seems to be the most appropriate format for structural design and evaluation of structural performance, reliability and safety. The presented combination of the nonlinear analysis with advanced probabilistic methods allows evaluation of structural safety characterized by failure probability or by reliability index respectively. Authors offer a methodology and computer tools for realistic safety assessment of concrete structures; the utilized approach is based on randomization of the nonlinear finite element analysis of the structural model. Uncertainty of the material properties or their randomness obtained from material tests are accounted in the random distribution. Furthermore, degradation of the reinforced concrete materials such as carbonation of concrete, corrosion of reinforcement, etc. can be accounted in order to analyze life-cycle structural performance and to enable prediction of the structural reliability and safety in time development. The results can serve as a rational basis for design of fibre concrete engineering structures based on advanced nonlinear computer analysis. The presented methodology is illustrated on results from two probabilistic studies with different types of concrete structures related to practical applications and made from various materials (with the parameters obtained from real material tests).

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions

PubMed Central

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage. PMID:27468262

Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

PubMed

Testolin, Alberto; Zorzi, Marco

2016-01-01

Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

A probabilistic approach for shallow rainfall-triggered landslide modeling at basin scale. A case study in the Luquillo Forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Dialynas, Y. G.; Arnone, E.; Noto, L. V.; Bras, R. L.

2013-12-01

Slope stability depends on geotechnical and hydrological factors that exhibit wide natural spatial variability, yet sufficient measurements of the related parameters are rarely available over entire study areas. The uncertainty associated with the inability to fully characterize hydrologic behavior has an impact on any attempt to model landslide hazards. This work suggests a way to systematically account for this uncertainty in coupled distributed hydrological-stability models for shallow landslide hazard assessment. A probabilistic approach for the prediction of rainfall-triggered landslide occurrence at basin scale was implemented in an existing distributed eco-hydrological and landslide model, tRIBS-VEGGIE -landslide (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator - VEGetation Generator for Interactive Evolution). More precisely, we upgraded tRIBS-VEGGIE- landslide to assess the likelihood of shallow landslides by accounting for uncertainty related to geotechnical and hydrological factors that directly affect slope stability. Natural variability of geotechnical soil characteristics was considered by randomizing soil cohesion and friction angle. Hydrological uncertainty related to the estimation of matric suction was taken into account by considering soil retention parameters as correlated random variables. The probability of failure is estimated through an assumed theoretical Factor of Safety (FS) distribution, conditioned on soil moisture content. At each cell, the temporally variant FS statistics are approximated by the First Order Second Moment (FOSM) method, as a function of parameters statistical properties. The model was applied on the Rio Mameyes Basin, located in the Luquillo Experimental Forest in Puerto Rico, where previous landslide analyses have been carried out. At each time step, model outputs include the probability of landslide occurrence across the basin, and the most probable depth of failure at each soil column. The use of the proposed probabilistic approach for shallow landslide prediction is able to reveal and quantify landslide risk at slopes assessed as stable by simpler deterministic methods.

Modelling default and likelihood reasoning as probabilistic reasoning

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a qualitative default probabilistic (QDP) logic and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequent results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

Assessing the polycyclic aromatic hydrocarbon (PAH) pollution of urban stormwater runoff: a dynamic modeling approach.

PubMed

Zheng, Yi; Lin, Zhongrong; Li, Hao; Ge, Yan; Zhang, Wei; Ye, Youbin; Wang, Xuejun

2014-05-15

Urban stormwater runoff delivers a significant amount of polycyclic aromatic hydrocarbons (PAHs), mostly of atmospheric origin, to receiving water bodies. The PAH pollution of urban stormwater runoff poses serious risk to aquatic life and human health, but has been overlooked by environmental modeling and management. This study proposed a dynamic modeling approach for assessing the PAH pollution and its associated environmental risk. A variable time-step model was developed to simulate the continuous cycles of pollutant buildup and washoff. To reflect the complex interaction among different environmental media (i.e. atmosphere, dust and stormwater), the dependence of the pollution level on antecedent weather conditions was investigated and embodied in the model. Long-term simulations of the model can be efficiently performed, and probabilistic features of the pollution level and its risk can be easily determined. The applicability of this approach and its value to environmental management was demonstrated by a case study in Beijing, China. The results showed that Beijing's PAH pollution of road runoff is relatively severe, and its associated risk exhibits notable seasonal variation. The current sweeping practice is effective in mitigating the pollution, but the effectiveness is both weather-dependent and compound-dependent. The proposed modeling approach can help identify critical timing and major pollutants for monitoring, assessing and controlling efforts to be focused on. The approach is extendable to other urban areas, as well as to other contaminants with similar fate and transport as PAHs. Copyright © 2014 Elsevier B.V. All rights reserved.

Global integrated drought monitoring and prediction system

PubMed Central

Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza

2014-01-01

Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe. PMID:25977759

Global integrated drought monitoring and prediction system.

PubMed

Hao, Zengchao; AghaKouchak, Amir; Nakhjiri, Navid; Farahmand, Alireza

2014-01-01

Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe.

Comparision of the different probability distributions for earthquake hazard assessment in the North Anatolian Fault Zone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yilmaz, Şeyda, E-mail: seydayilmaz@ktu.edu.tr; Bayrak, Erdem, E-mail: erdmbyrk@gmail.com; Bayrak, Yusuf, E-mail: bayrak@ktu.edu.tr

In this study we examined and compared the three different probabilistic distribution methods for determining the best suitable model in probabilistic assessment of earthquake hazards. We analyzed a reliable homogeneous earthquake catalogue between a time period 1900-2015 for magnitude M ≥ 6.0 and estimated the probabilistic seismic hazard in the North Anatolian Fault zone (39°-41° N 30°-40° E) using three distribution methods namely Weibull distribution, Frechet distribution and three-parameter Weibull distribution. The distribution parameters suitability was evaluated Kolmogorov-Smirnov (K-S) goodness-of-fit test. We also compared the estimated cumulative probability and the conditional probabilities of occurrence of earthquakes for different elapsed timemore » using these three distribution methods. We used Easyfit and Matlab software to calculate these distribution parameters and plotted the conditional probability curves. We concluded that the Weibull distribution method was the most suitable than other distribution methods in this region.« less

Probabilistic estimation of residential air exchange rates for population-based human exposure modeling

EPA Science Inventory

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER meas...

From information processing to decisions: Formalizing and comparing psychologically plausible choice models.

PubMed

Heck, Daniel W; Hilbig, Benjamin E; Moshagen, Morten

2017-08-01

Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

Rasagiline in the Treatment of the Persistent Negative Symptoms of Schizophrenia.

PubMed

Buchanan, Robert W; Weiner, Elaine; Kelly, Deanna L; Gold, James M; Keller, William R; Waltz, James A; McMahon, Robert P; Gorelick, David A

2015-07-01

The current study examined the efficacy and safety of rasagiline, a selective MAO-B inhibitor, for the treatment of persistent negative symptoms. Sixty people with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, schizophrenia or schizoaffective disorder, who met a priori criteria for persistent negative symptoms, were randomized to receive rasagiline, 1mg/d (n = 31) or placebo (n = 29) in a 12-week, double-blind, placebo-controlled clinical trial. The Scale for the Assessment of Negative Symptoms (SANS) total score was used to assess change in negative symptoms. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), N-Back test, a probabilistic learning task, and a delayed discounting task were used to assess cognition. In a mixed model analysis of covariance (MM-ANCOVA), with time as a continuous variable, there was a significant treatment × time effect for SANS total score (F = 5.61(df = 1,40.3), P = .023). The treatment × time interaction effect was also significant for the SANS avolition subscale score (F(1,40.2) = 10.41, P = .002). In a post hoc MM-ANCOVA analyses, with time as a categorical variable, group differences were significant at week 12 for SANS total score (t(37.3) = 2.15; P = .04; d = -0.41) and SANS avolition subscale score (t(49.0) = 3.06; P = .004; d = -0.46). There was a significant difference in number of participants with a ≥20% reduction in SANS avolition score (χ(2)(1) = 10.94; P = .0009), but not in SANS total score (χ(2)(1) = 1.11; P = .29). There were no significant group differences on the RBANS, N-Back, probabilistic learning, or delayed discounting tasks. Study results support future studies of the utility of rasagiline for the treatment of negative symptoms, including avolition (clinicaltrials.gov trial number: NCT00492336). © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Rasagiline in the Treatment of the Persistent Negative Symptoms of Schizophrenia

PubMed Central

Buchanan, Robert W.; Weiner, Elaine; Kelly, Deanna L.; Gold, James M.; Keller, William R.; Waltz, James A.; McMahon, Robert P.; Gorelick, David A.

2015-01-01

Objective: The current study examined the efficacy and safety of rasagiline, a selective MAO-B inhibitor, for the treatment of persistent negative symptoms. Methods: Sixty people with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, schizophrenia or schizoaffective disorder, who met a priori criteria for persistent negative symptoms, were randomized to receive rasagiline, 1mg/d (n = 31) or placebo (n = 29) in a 12-week, double-blind, placebo-controlled clinical trial. The Scale for the Assessment of Negative Symptoms (SANS) total score was used to assess change in negative symptoms. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), N-Back test, a probabilistic learning task, and a delayed discounting task were used to assess cognition. Results: In a mixed model analysis of covariance (MM-ANCOVA), with time as a continuous variable, there was a significant treatment × time effect for SANS total score (F = 5.61(df = 1,40.3), P = .023). The treatment × time interaction effect was also significant for the SANS avolition subscale score (F(1,40.2) = 10.41, P = .002). In a post hoc MM-ANCOVA analyses, with time as a categorical variable, group differences were significant at week 12 for SANS total score (t(37.3) = 2.15; P = .04; d = −0.41) and SANS avolition subscale score (t(49.0) = 3.06; P = .004; d = −0.46). There was a significant difference in number of participants with a ≥20% reduction in SANS avolition score (χ2(1) = 10.94; P = .0009), but not in SANS total score (χ2(1) = 1.11; P = .29). There were no significant group differences on the RBANS, N-Back, probabilistic learning, or delayed discounting tasks. Conclusions: Study results support future studies of the utility of rasagiline for the treatment of negative symptoms, including avolition (clinicaltrials.gov trial number: NCT00492336). PMID:25368372

Comparing probabilistic and descriptive analyses of time–dose–toxicity relationship for determining no-observed-adverse-effect level in drug development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glatard, Anaïs; Berges, Aliénor; Sahota, Tarjinder

The no-observed-adverse-effect level (NOAEL) of a drug defined from animal studies is important for inferring a maximal safe dose in human. However, several issues are associated with its concept, determination and application. It is confined to the actual doses used in the study; becomes lower with increasing sample size or dose levels; and reflects the risk level seen in the experiment rather than what may be relevant for human. We explored a pharmacometric approach in an attempt to address these issues. We first used simulation to examine the behaviour of the NOAEL values as determined by current common practice; andmore » then fitted the probability of toxicity as a function of treatment duration and dose to data collected from all applicable toxicology studies of a test compound. Our investigation was in the context of an irreversible toxicity that is detected at the end of the study. Simulations illustrated NOAEL's dependency on experimental factors such as dose and sample size, as well as the underlying uncertainty. Modelling the probability as a continuous function of treatment duration and dose simultaneously to data from multiple studies allowed the estimation of the dose, along with its confidence interval, for a maximal risk level that might be deemed as acceptable for human. The model-based data integration also reconciled between-study inconsistency and explicitly provided maximised estimation confidence. Such alternative NOAEL determination method should be explored for its more efficient data use, more quantifiable insight to toxic doses, and the potential for more relevant animal-to-human translation. - Highlights: • Simulations revealed issues with NOAEL concept, determination and application. • Probabilistic modelling was used to address these issues. • The model integrated time-dose-toxicity data from multiple studies. • The approach uses data efficiently and may allow more meaningful human translation.« less

Probabilistic Risk Assessment for Astronaut Post Flight Bone Fracture

NASA Technical Reports Server (NTRS)

Lewandowski, Beth; Myers, Jerry; Licata, Angelo

2015-01-01

Introduction: Space flight potentially reduces the loading that bone can resist before fracture. This reduction in bone integrity may result from a combination of factors, the most common reported as reduction in astronaut BMD. Although evaluating the condition of bones continues to be a critical aspect of understanding space flight fracture risk, defining the loading regime, whether on earth, in microgravity, or in reduced gravity on a planetary surface, remains a significant component of estimating the fracture risks to astronauts. This presentation summarizes the concepts, development, and application of NASA's Bone Fracture Risk Module (BFxRM) to understanding pre-, post, and in mission astronaut bone fracture risk. The overview includes an assessment of contributing factors utilized in the BFxRM and illustrates how new information, such as biomechanics of space suit design or better understanding of post flight activities may influence astronaut fracture risk. Opportunities for the bone mineral research community to contribute to future model development are also discussed. Methods: To investigate the conditions in which spaceflight induced changes to bone plays a critical role in post-flight fracture probability, we implement a modified version of the NASA Bone Fracture Risk Model (BFxRM). Modifications included incorporation of variations in physiological characteristics, post-flight recovery rate, and variations in lateral fall conditions within the probabilistic simulation parameter space. The modeled fracture probability estimates for different loading scenarios at preflight and at 0 and 365 days post-flight time periods are compared. Results: For simple lateral side falls, mean post-flight fracture probability is elevated over mean preflight fracture probability due to spaceflight induced BMD loss and is not fully recovered at 365 days post-flight. In the case of more energetic falls, such as from elevated heights or with the addition of lateral movement, the contribution of space flight quality changes is much less clear, indicating more granular assessments, such as Finite Element modeling, may be needed to further assess the risks in these scenarios.

Sequential Data Assimilation for Seismicity: a Proof of Concept

NASA Astrophysics Data System (ADS)

van Dinther, Y.; Fichtner, A.; Kuensch, H. R.

2015-12-01

Our physical understanding and probabilistic forecasting ability of earthquakes is significantly hampered by limited indications of the state of stress and strength on faults and their governing parameters. Using the sequential data assimilation framework developed in meteorology and oceanography (e.g., Evensen, JGR, 1994) and a seismic cycle forward model based on Navier-Stokes Partial Differential Equations (van Dinther et al., JGR, 2013), we show that such information with its uncertainties is within reach, at least for laboratory setups. We aim to provide the first, thorough proof of concept for seismicity related PDE applications via a perfect model test of seismic cycles in a simplified wedge-like subduction setup. By evaluating the performance with respect to known numerical input and output, we aim to answer wether there is any probabilistic forecast value for this laboratory-like setup, which and how many parameters can be constrained, and how much data in both space and time would be needed to do so. Thus far our implementation of an Ensemble Kalman Filter demonstrated that probabilistic estimates of both the state of stress and strength on a megathrust fault can be obtained and utilized even when assimilating surface velocity data at a single point in time and space. An ensemble-based error covariance matrix containing velocities, stresses and pressure links surface velocity observations to fault stresses and strengths well enough to update fault coupling accordingly. Depending on what synthetic data show, coseismic events can then be triggered or inhibited.

Probabilistic Assessment of Radiation Risk for Astronauts in Space Missions

NASA Technical Reports Server (NTRS)

Kim, Myung-Hee; DeAngelis, Giovanni; Cucinotta, Francis A.

2009-01-01

Accurate predictions of the health risks to astronauts from space radiation exposure are necessary for enabling future lunar and Mars missions. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons, (less than 100 MeV); and galactic cosmic rays (GCR), which include protons and heavy ions of higher energies. While the expected frequency of SPEs is strongly influenced by the solar activity cycle, SPE occurrences themselves are random in nature. A solar modulation model has been developed for the temporal characterization of the GCR environment, which is represented by the deceleration potential, phi. The risk of radiation exposure from SPEs during extra-vehicular activities (EVAs) or in lightly shielded vehicles is a major concern for radiation protection, including determining the shielding and operational requirements for astronauts and hardware. To support the probabilistic risk assessment for EVAs, which would be up to 15% of crew time on lunar missions, we estimated the probability of SPE occurrence as a function of time within a solar cycle using a nonhomogeneous Poisson model to fit the historical database of measurements of protons with energy > 30 MeV, (phi)30. The resultant organ doses and dose equivalents, as well as effective whole body doses for acute and cancer risk estimations are analyzed for a conceptual habitat module and a lunar rover during defined space mission periods. This probabilistic approach to radiation risk assessment from SPE and GCR is in support of mission design and operational planning to manage radiation risks for space exploration.

Probabilistic Survivability Versus Time Modeling

NASA Technical Reports Server (NTRS)

Joyner, James J., Sr.

2015-01-01

This technical paper documents Kennedy Space Centers Independent Assessment team work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer (CSO) and GSDO management during key programmatic reviews. The assessments provided the GSDO Program with an analysis of how egress time affects the likelihood of astronaut and worker survival during an emergency. For each assessment, the team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedys Vehicle Assembly Building (VAB).Based on the composite survivability versus time graphs from the first two assessments, there was a soft knee in the Figure of Merit graphs at eight minutes (ten minutes after egress ordered). Thus, the graphs illustrated to the decision makers that the final emergency egress design selected should have the capability of transporting the flight crew from the top of LC 39B to a safe location in eight minutes or less. Results for the third assessment were dominated by hazards that were classified as instantaneous in nature (e.g. stacking mishaps) and therefore had no effect on survivability vs time to egress the VAB. VAB emergency scenarios that degraded over time (e.g. fire) produced survivability vs time graphs that were line with aerospace industry norms.

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Model fitting data from syllogistic reasoning experiments.

PubMed

Hattori, Masasi

2016-12-01

The data presented in this article are related to the research article entitled "Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics" (M. Hattori, 2016) [1]. This article presents predicted data by three signature probabilistic models of syllogistic reasoning and model fitting results for each of a total of 12 experiments ( N =404) in the literature. Models are implemented in R, and their source code is also provided.

The Probability Heuristics Model of Syllogistic Reasoning.

ERIC Educational Resources Information Center

Chater, Nick; Oaksford, Mike

1999-01-01

Proposes a probability heuristic model for syllogistic reasoning and confirms the rationality of this heuristic by an analysis of the probabilistic validity of syllogistic reasoning that treats logical inference as a limiting case of probabilistic inference. Meta-analysis and two experiments involving 40 adult participants and using generalized…

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Ishizaki, N. N.

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses.

PubMed

Fuller, Robert William; Wong, Tony E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.

A New Statistical Model for Eruption Forecasting at Open Conduit Volcanoes: an Application to Mt Etna and Kilauea Volcanoes

NASA Astrophysics Data System (ADS)

Passarelli, Luigi; Sanso, Bruno; Laura, Sandri; Marzocchi, Warner

2010-05-01

One of the main goals in volcanology is to forecast volcanic eruptions. A trenchant forecast should be made before the onset of a volcanic eruption, using the data available at that time, with the aim of mitigating the volcanic risk associated to the volcanic event. In other words, models implemented with forecast purposes have to take into account the possibility to provide "forward" forecasts and should avoid the idea of a merely "retrospective" fitting of the data available. In this perspective, the main idea of the present model is to forecast the next volcanic eruption after the end of the last one, using only the data available at that time. We focus our attention on volcanoes with open conduit regime and high eruption frequency. We assume a generalization of the classical time predictable model to describe the eruptive behavior of open conduit volcanoes and we use a Bayesian hierarchical model to make probabilistic forecast. We apply the model to Kilauea volcano eruptive data and Mt. Etna volcano flank eruption data. The aims of this model are: 1) to test whether or not the Kilauea and Mt Etna volcanoes follow a time predictable behavior; 2) to discuss the volcanological implications of the time predictable model parameters inferred; 3) to compare the forecast capabilities of this model with other models present in literature. The results obtained using the MCMC sampling algorithm show that both volcanoes follow a time predictable behavior. The numerical values of the time predictable model parameters inferred suggest that the amount of the erupted volume could change the dynamics of the magma chamber refilling process during the repose period. The probability gain of this model compared with other models already present in literature is appreciably greater than zero. This means that our model performs better forecast than previous models and it could be used in a probabilistic volcanic hazard assessment scheme. In this perspective, the probability of eruptions given by our model for Mt Etna volcano flank eruption are published on a internet website and are updated after any change in the eruptive activity.

A model to assess the Mars Telecommunications Network relay robustness

NASA Technical Reports Server (NTRS)

Girerd, Andre R.; Meshkat, Leila; Edwards, Charles D., Jr.; Lee, Charles H.

2005-01-01

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Applying the food safety objective and related standards to thermal inactivation of Salmonella in poultry meat.

PubMed

Membré, Jeanne-Marie; Bassett, John; Gorris, Leon G M

2007-09-01

The objective of this study was to investigate the practicality of designing a heat treatment process in a food manufacturing operation for a product governed by a Food Safety Objective (FSO). Salmonella in cooked poultry meat was taken as the working example. Although there is no FSO for this product in current legislation, this may change in the (near) future. Four different process design calculations were explored by means of deterministic and probabilistic approaches to mathematical data handling and modeling. It was found that the probabilistic approach was a more objective, transparent, and quantifiable approach to establish the stringency of food safety management systems. It also allowed the introduction of specific prevalence rates. The key input analyzed in this study was the minimum time required for the heat treatment at a fixed temperature to produce a product that complied with the criterion for product safety, i.e., the FSO. By means of the four alternative process design calculations, the minimum time requirement at 70 degrees C was established and ranged from 0.26 to 0.43 min. This is comparable to the U.S. regulation recommendations and significantly less than that of 2 min at 70 degrees C used, for instance, in the United Kingdom regulation concerning vegetative microorganisms in ready-to-eat foods. However, the objective of this study was not to challenge existing regulations but to provide an illustration of how an FSO established by a competent authority can guide decisions on safe product and process designs in practical operation; it hopefully contributes to the collaborative work between regulators, academia, and industries that need to continue learning and gaining experience from each other in order to translate risk-based concepts such as the FSO into everyday operational practice.

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting.

PubMed

Berkvens, Rafael; Peremans, Herbert; Weyn, Maarten

2016-10-02

Localization systems are increasingly valuable, but their location estimates are only useful when the uncertainty of the estimate is known. This uncertainty is currently calculated as the location error given a ground truth, which is then used as a static measure in sometimes very different environments. In contrast, we propose the use of the conditional entropy of a posterior probability distribution as a complementary measure of uncertainty. This measure has the advantage of being dynamic, i.e., it can be calculated during localization based on individual sensor measurements, does not require a ground truth, and can be applied to discrete localization algorithms. Furthermore, for every consistent location estimation algorithm, both the location error and the conditional entropy measures must be related, i.e., a low entropy should always correspond with a small location error, while a high entropy can correspond with either a small or large location error. We validate this relationship experimentally by calculating both measures of uncertainty in three publicly available datasets using probabilistic Wi-Fi fingerprinting with eight different implementations of the sensor model. We show that the discrepancy between these measures, i.e., many location estimates having a high location error while simultaneously having a low conditional entropy, is largest for the least realistic implementations of the probabilistic sensor model. Based on the results presented in this paper, we conclude that conditional entropy, being dynamic, complementary to location error, and applicable to both continuous and discrete localization, provides an important extra means of characterizing a localization method.

Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting

PubMed Central

Berkvens, Rafael; Peremans, Herbert; Weyn, Maarten

2016-01-01

Localization systems are increasingly valuable, but their location estimates are only useful when the uncertainty of the estimate is known. This uncertainty is currently calculated as the location error given a ground truth, which is then used as a static measure in sometimes very different environments. In contrast, we propose the use of the conditional entropy of a posterior probability distribution as a complementary measure of uncertainty. This measure has the advantage of being dynamic, i.e., it can be calculated during localization based on individual sensor measurements, does not require a ground truth, and can be applied to discrete localization algorithms. Furthermore, for every consistent location estimation algorithm, both the location error and the conditional entropy measures must be related, i.e., a low entropy should always correspond with a small location error, while a high entropy can correspond with either a small or large location error. We validate this relationship experimentally by calculating both measures of uncertainty in three publicly available datasets using probabilistic Wi-Fi fingerprinting with eight different implementations of the sensor model. We show that the discrepancy between these measures, i.e., many location estimates having a high location error while simultaneously having a low conditional entropy, is largest for the least realistic implementations of the probabilistic sensor model. Based on the results presented in this paper, we conclude that conditional entropy, being dynamic, complementary to location error, and applicable to both continuous and discrete localization, provides an important extra means of characterizing a localization method. PMID:27706099

Stochastic Controls on Nitrate Transport and Cycling

NASA Astrophysics Data System (ADS)

Botter, G.; Settin, T.; Alessi Celegon, E.; Marani, M.; Rinaldo, A.

2005-12-01

In this paper, the impact of nutrient inputs on basin-scale nitrates losses is investigated in a probabilistic framework by means of a continuous, geomorphologically based, Montecarlo approach, which explicitly tackles the random character of the processes controlling nitrates generation, transformation and transport in river basins. This is obtained by coupling the stochastic generation of climatic and rainfall series with simplified hydrologic and biogeochemical models operating at the hillslope scale. Special attention is devoted to the spatial and temporal variability of nitrogen sources of agricultural origin and to the effect of temporally distributed rainfall fields on the ensuing nitrates leaching. The influence of random climatic variables on bio-geochemical processes affecting the nitrogen cycle in the soil-water system (e.g. plant uptake, nitrification and denitrification, mineralization), is also considered. The approach developed has been applied to a catchment located in North-Eastern Italy and is used to provide probabilistic estimates of the NO_3 load transferred downstream, which is received and accumulated in the Venice lagoon. We found that the nitrogen load introduced by fertilizations significantly affects the pdf of the nitrates content in the soil moisture, leading to prolonged risks of increased nitrates leaching from soil. The model allowed the estimation of the impact of different practices on the probabilistic structure of the basin-scale hydrologic and chemical response. As a result, the return period of the water volumes and of the nitrates loads released into the Venice lagoon has been linked directly to the ongoing climatic, pluviometric and agricultural regimes, with relevant implications for environmental planning activities aimed at achieving sustainable management practices.

Composite load spectra for select space propulsion structural components

NASA Technical Reports Server (NTRS)

Newell, J. F.; Ho, H. W.; Kurth, R. E.

1991-01-01

The work performed to develop composite load spectra (CLS) for the Space Shuttle Main Engine (SSME) using probabilistic methods. The three methods were implemented to be the engine system influence model. RASCAL was chosen to be the principal method as most component load models were implemented with the method. Validation of RASCAL was performed. High accuracy comparable to the Monte Carlo method can be obtained if a large enough bin size is used. Generic probabilistic models were developed and implemented for load calculations using the probabilistic methods discussed above. Each engine mission, either a real fighter or a test, has three mission phases: the engine start transient phase, the steady state phase, and the engine cut off transient phase. Power level and engine operating inlet conditions change during a mission. The load calculation module provides the steady-state and quasi-steady state calculation procedures with duty-cycle-data option. The quasi-steady state procedure is for engine transient phase calculations. In addition, a few generic probabilistic load models were also developed for specific conditions. These include the fixed transient spike model, the poison arrival transient spike model, and the rare event model. These generic probabilistic load models provide sufficient latitude for simulating loads with specific conditions. For SSME components, turbine blades, transfer ducts, LOX post, and the high pressure oxidizer turbopump (HPOTP) discharge duct were selected for application of the CLS program. They include static pressure loads and dynamic pressure loads for all four components, centrifugal force for the turbine blade, temperatures of thermal loads for all four components, and structural vibration loads for the ducts and LOX posts.

Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories.

PubMed

Chikalov, Igor; Yao, Peggy; Moshkov, Mikhail; Latombe, Jean-Claude

2011-02-15

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. This paper describes inductive learning methods to train protein-independent probabilistic models of H-bond stability from molecular dynamics (MD) simulation trajectories of various proteins. The training data contains 32 input attributes (predictors) that describe an H-bond and its local environment in a conformation c and the output attribute is the probability that the H-bond will be present in an arbitrary conformation of this protein achievable from c within a time duration Δ. We model dependence of the output variable on the predictors by a regression tree. Several models are built using 6 MD simulation trajectories containing over 4000 distinct H-bonds (millions of occurrences). Experimental results demonstrate that such models can predict H-bond stability quite well. They perform roughly 20% better than models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a conformation. In most tests, about 80% of the 10% H-bonds predicted as the least stable are actually among the 10% truly least stable. The important attributes identified during the tree construction are consistent with previous findings. We use inductive learning methods to build protein-independent probabilistic models to study H-bond stability, and demonstrate that the models perform better than H-bond energy alone.

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

USGS Publications Warehouse

Barbraud, C.; Nichols, J.D.; Hines, J.E.; Hafner, H.

2003-01-01

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence-absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence-absence data arising from Pollock's robust capture-recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence-absence data on two species of herons (Purple Heron, Ardea purpurea and Grey Heron, Ardea cinerea). Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Probabilistic delay differential equation modeling of event-related potentials.

PubMed

Ostwald, Dirk; Starke, Ludger

2016-08-01

"Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach. Copyright © 2016 Elsevier Inc. All rights reserved.

Turbulent transport with intermittency: Expectation of a scalar concentration.

PubMed

Rast, Mark Peter; Pinton, Jean-François; Mininni, Pablo D

2016-04-01

Scalar transport by turbulent flows is best described in terms of Lagrangian parcel motions. Here we measure the Eulerian distance travel along Lagrangian trajectories in a simple point vortex flow to determine the probabilistic impulse response function for scalar transport in the absence of molecular diffusion. As expected, the mean squared Eulerian displacement scales ballistically at very short times and diffusively for very long times, with the displacement distribution at any given time approximating that of a random walk. However, significant deviations in the displacement distributions from Rayleigh are found. The probability of long distance transport is reduced over inertial range time scales due to spatial and temporal intermittency. This can be modeled as a series of trapping events with durations uniformly distributed below the Eulerian integral time scale. The probability of long distance transport is, on the other hand, enhanced beyond that of the random walk for both times shorter than the Lagrangian integral time and times longer than the Eulerian integral time. The very short-time enhancement reflects the underlying Lagrangian velocity distribution, while that at very long times results from the spatial and temporal variation of the flow at the largest scales. The probabilistic impulse response function, and with it the expectation value of the scalar concentration at any point in space and time, can be modeled using only the evolution of the lowest spatial wave number modes (the mean and the lowest harmonic) and an eddy based constrained random walk that captures the essential velocity phase relations associated with advection by vortex motions. Preliminary examination of Lagrangian tracers in three-dimensional homogeneous isotropic turbulence suggests that transport in that setting can be similarly modeled.

Dynamic Probabilistic Instability of Composite Structures

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2009-01-01

A computationally effective method is described to evaluate the non-deterministic dynamic instability (probabilistic dynamic buckling) of thin composite shells. The method is a judicious combination of available computer codes for finite element, composite mechanics and probabilistic structural analysis. The solution method is incrementally updated Lagrangian. It is illustrated by applying it to thin composite cylindrical shell subjected to dynamic loads. Both deterministic and probabilistic buckling loads are evaluated to demonstrate the effectiveness of the method. A universal plot is obtained for the specific shell that can be used to approximate buckling loads for different load rates and different probability levels. Results from this plot show that the faster the rate, the higher the buckling load and the shorter the time. The lower the probability, the lower is the buckling load for a specific time. Probabilistic sensitivity results show that the ply thickness, the fiber volume ratio and the fiber longitudinal modulus, dynamic load and loading rate are the dominant uncertainties in that order.

Unified Models of Turbulence and Nonlinear Wave Evolution in the Extended Solar Corona and Solar Wind

NASA Technical Reports Server (NTRS)

Cranmer, Steven R.; Wagner, William (Technical Monitor)

2004-01-01

The PI (Cranmer) and Co-I (A. van Ballegooijen) made substantial progress toward the goal of producing a unified model of the basic physical processes responsible for solar wind acceleration. The approach outlined in the original proposal comprised two complementary pieces: (1) to further investigate individual physical processes under realistic coronal and solar wind conditions, and (2) to extract the dominant physical effects from simulations and apply them to a 1D model of plasma heating and acceleration. The accomplishments in Year 2 are divided into these two categories: 1a. Focused Study of Kinetic Magnetohydrodynamic (MHD) Turbulence. lb. Focused Study of Non - WKB Alfven Wave Rejection. and 2. The Unified Model Code. We have continued the development of the computational model of a time-study open flux tube in the extended corona. The proton-electron Monte Carlo model is being tested, and collisionless wave-particle interactions are being included. In order to better understand how to easily incorporate various kinds of wave-particle processes into the code, the PI performed a detailed study of the so-called "Ito Calculus", i.e., the mathematical theory of how to update the positions of particles in a probabilistic manner when their motions are governed by diffusion in velocity space.

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose-glucose metabolism.

PubMed

Asano, Masanari; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-05-28

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies andEscherichia colilactose-glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing withn-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. © 2016 The Author(s).

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit.

PubMed

Frommholz, Ingo; Roelleke, Thomas

2016-01-01

Probabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs. Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing . The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose–glucose metabolism

PubMed Central

Asano, Masanari; Ohya, Masanori; Yamato, Ichiro

2016-01-01

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies and Escherichia coli lactose–glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing with n-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. PMID:27091163

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

On extinction time of a generalized endemic chain-binomial model.

PubMed

Aydogmus, Ozgur

2016-09-01

We considered a chain-binomial epidemic model not conferring immunity after infection. Mean field dynamics of the model has been analyzed and conditions for the existence of a stable endemic equilibrium are determined. The behavior of the chain-binomial process is probabilistically linked to the mean field equation. As a result of this link, we were able to show that the mean extinction time of the epidemic increases at least exponentially as the population size grows. We also present simulation results for the process to validate our analytical findings. Copyright © 2016 Elsevier Inc. All rights reserved.

Probabilistic Cloning of two Single-Atom States via Thermal Cavity

NASA Astrophysics Data System (ADS)

Rui, Pin-Shu; Liu, Dao-Jun

2016-12-01

We propose a cavity QED scheme for implementing the 1 → 2 probabilistic quantum cloning (PQC) of two single-atom states. In our scheme, after the to-be-cloned atom and the assistant atom passing through the first cavity, a measurement is carried out on the assistant atom. Based on the measurement outcome we can judge whether the PQC should be continued. If the cloning fails, the other operations are omitted. This makes our scheme economical. If the PQC is continued (with the optimal probability) according to the measurement outcome, two more cavities and some unitary operations are used for achieving the PQC in a deterministic way. Our scheme is insensitive to the decays of the cavities and the atoms.

Stan : A Probabilistic Programming Language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectationmore » propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.« less

Bayesian networks in neuroscience: a survey.

PubMed

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Bayesian networks in neuroscience: a survey

PubMed Central

Bielza, Concha; Larrañaga, Pedro

2014-01-01

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied. PMID:25360109

Stan : A Probabilistic Programming Language

DOE PAGES

Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; ...

2017-01-01

Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectationmore » propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.« less

Trait-Dependent Biogeography: (Re)Integrating Biology into Probabilistic Historical Biogeographical Models.

PubMed

Sukumaran, Jeet; Knowles, L Lacey

2018-06-01

The development of process-based probabilistic models for historical biogeography has transformed the field by grounding it in modern statistical hypothesis testing. However, most of these models abstract away biological differences, reducing species to interchangeable lineages. We present here the case for reintegration of biology into probabilistic historical biogeographical models, allowing a broader range of questions about biogeographical processes beyond ancestral range estimation or simple correlation between a trait and a distribution pattern, as well as allowing us to assess how inferences about ancestral ranges themselves might be impacted by differential biological traits. We show how new approaches to inference might cope with the computational challenges resulting from the increased complexity of these trait-based historical biogeographical models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparison of Four Probabilistic Models (CARES, Calendex, ConsEspo, SHEDS) to Estimate Aggregate Residential Exposures to Pesticides

EPA Science Inventory

Two deterministic models (US EPA’s Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children’s Non-Occupational Exposure to Pesticides by all Relevant Pathways (Draft Protocol)) and four probabilistic mo...

EXPERIENCES WITH USING PROBABILISTIC EXPOSURE ANALYSIS METHODS IN THE U.S. EPA

EPA Science Inventory

Over the past decade various Offices and Programs within the U.S. EPA have either initiated or increased the development and application of probabilistic exposure analysis models. These models have been applied to a broad range of research or regulatory problems in EPA, such as e...

Seismic probabilistic tsunami hazard: from regional to local analysis and use of geological and historical observations

NASA Astrophysics Data System (ADS)

Tonini, R.; Lorito, S.; Orefice, S.; Graziani, L.; Brizuela, B.; Smedile, A.; Volpe, M.; Romano, F.; De Martini, P. M.; Maramai, A.; Selva, J.; Piatanesi, A.; Pantosti, D.

2016-12-01

Site-specific probabilistic tsunami hazard analyses demand very high computational efforts that are often reduced by introducing approximations on tsunami sources and/or tsunami modeling. On one hand, the large variability of source parameters implies the definition of a huge number of potential tsunami scenarios, whose omission could easily lead to important bias in the analysis. On the other hand, detailed inundation maps computed by tsunami numerical simulations require very long running time. When tsunami effects are calculated at regional scale, a common practice is to propagate tsunami waves in deep waters (up to 50-100 m depth) neglecting non-linear effects and using coarse bathymetric meshes. Then, maximum wave heights on the coast are empirically extrapolated, saving a significant amount of computational time. However, moving to local scale, such assumptions drop out and tsunami modeling would require much greater computational resources. In this work, we perform a local Seismic Probabilistic Tsunami Hazard Analysis (SPTHA) for the 50 km long coastal segment between Augusta and Siracusa, a touristic and commercial area placed along the South-Eastern Sicily coast, Italy. The procedure consists in using the outcomes of a regional SPTHA as input for a two-step filtering method to select and substantially reduce the number of scenarios contributing to the specific target area. These selected scenarios are modeled using high resolution topo-bathymetry for producing detailed inundation maps. Results are presented as probabilistic hazard curves and maps, with the goal of analyze, compare and highlight the different results provided by regional and local hazard assessments. Moreover, the analysis is enriched by the use of local observed tsunami data, both geological and historical. Indeed, tsunami data-sets available for the selected target areas are particularly rich with respect to the scarce and heterogeneous data-sets usually available elsewhere. Therefore, they can represent valuable benchmarks for testing and strengthening the results of such kind of studies. The work is funded by the Italian Flagship Project RITMARE, the two EC FP7 projects ASTARTE (Grant agreement 603839) and STREST (Grant agreement 603389), and the INGV-DPC Agreement.

Aircraft Conflict Analysis and Real-Time Conflict Probing Using Probabilistic Trajectory Modeling

NASA Technical Reports Server (NTRS)

Yang, Lee C.; Kuchar, James K.

2000-01-01

Methods for maintaining separation between aircraft in the current airspace system have been built from a foundation of structured routes and evolved procedures. However, as the airspace becomes more congested and the chance of failures or operational error become more problematic, automated conflict alerting systems have been proposed to help provide decision support and to serve as traffic monitoring aids. The problem of conflict detection and resolution has been tackled from a number of different ways, but in this thesis, it is recast as a problem of prediction in the presence of uncertainties. Much of the focus is concentrated on the errors and uncertainties from the working trajectory model used to estimate future aircraft positions. The more accurate the prediction, the more likely an ideal (no false alarms, no missed detections) alerting system can be designed. Additional insights into the problem were brought forth by a review of current operational and developmental approaches found in the literature. An iterative, trial and error approach to threshold design was identified. When examined from a probabilistic perspective, the threshold parameters were found to be a surrogate to probabilistic performance measures. To overcome the limitations in the current iterative design method, a new direct approach is presented where the performance measures are directly computed and used to perform the alerting decisions. The methodology is shown to handle complex encounter situations (3-D, multi-aircraft, multi-intent, with uncertainties) with relative ease. Utilizing a Monte Carlo approach, a method was devised to perform the probabilistic computations in near realtime. Not only does this greatly increase the method's potential as an analytical tool, but it also opens up the possibility for use as a real-time conflict alerting probe. A prototype alerting logic was developed and has been utilized in several NASA Ames Research Center experimental studies.

The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting

NASA Astrophysics Data System (ADS)

Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Brocca, Luca; Todini, Ezio

2017-08-01

This work extends the multi-temporal approach of the Model Conditional Processor (MCP-MT) to the multi-model case and to the four Truncated Normal Distributions (TNDs) approach, demonstrating the improvement on the single-temporal one. The study is framed in the context of probabilistic Bayesian decision-making that is appropriate to take rational decisions on uncertain future outcomes. As opposed to the direct use of deterministic forecasts, the probabilistic forecast identifies a predictive probability density function that represents a fundamental knowledge on future occurrences. The added value of MCP-MT is the identification of the probability that a critical situation will happen within the forecast lead-time and when, more likely, it will occur. MCP-MT is thoroughly tested for both single-model and multi-model configurations at a gauged site on the Tiber River, central Italy. The stages forecasted by two operative deterministic models, STAFOM-RCM and MISDc, are considered for the study. The dataset used for the analysis consists of hourly data from 34 flood events selected on a time series of six years. MCP-MT improves over the original models' forecasts: the peak overestimation and the rising limb delayed forecast, characterizing MISDc and STAFOM-RCM respectively, are significantly mitigated, with a reduced mean error on peak stage from 45 to 5 cm and an increased coefficient of persistence from 0.53 up to 0.75. The results show that MCP-MT outperforms the single-temporal approach and is potentially useful for supporting decision-making because the exceedance probability of hydrometric thresholds within a forecast horizon and the most probable flooding time can be estimated.

Structural reliability assessment capability in NESSUS

NASA Technical Reports Server (NTRS)

Millwater, H.; Wu, Y.-T.

1992-01-01

The principal capabilities of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), an advanced computer code developed for probabilistic structural response analysis, are reviewed, and its structural reliability assessed. The code combines flexible structural modeling tools with advanced probabilistic algorithms in order to compute probabilistic structural response and resistance, component reliability and risk, and system reliability and risk. An illustrative numerical example is presented.

Structural reliability assessment capability in NESSUS

NASA Astrophysics Data System (ADS)

Millwater, H.; Wu, Y.-T.

1992-07-01

The principal capabilities of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), an advanced computer code developed for probabilistic structural response analysis, are reviewed, and its structural reliability assessed. The code combines flexible structural modeling tools with advanced probabilistic algorithms in order to compute probabilistic structural response and resistance, component reliability and risk, and system reliability and risk. An illustrative numerical example is presented.

Heuristic and optimal policy computations in the human brain during sequential decision-making.

PubMed

Korn, Christoph W; Bach, Dominik R

2018-01-23

Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

NASA Astrophysics Data System (ADS)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Long-term strength and damage accumulation in laminates

NASA Astrophysics Data System (ADS)

Dzenis, Yuris A.; Joshi, Shiv P.

1993-04-01

A modified version of the probabilistic model developed by authors for damage evolution analysis of laminates subjected to random loading is utilized to predict long-term strength of laminates. The model assumes that each ply in a laminate consists of a large number of mesovolumes. Probabilistic variation functions for mesovolumes stiffnesses as well as strengths are used in the analysis. Stochastic strains are calculated using the lamination theory and random function theory. Deterioration of ply stiffnesses is calculated on the basis of the probabilities of mesovolumes failures using the theory of excursions of random process beyond the limits. Long-term strength and damage accumulation in a Kevlar/epoxy laminate under tension and complex in-plane loading are investigated. Effects of the mean level and stochastic deviation of loading on damage evolution and time-to-failure of laminate are discussed. Long-term cumulative damage at the time of the final failure at low loading levels is more than at high loading levels. The effect of the deviation in loading is more pronounced at lower mean loading levels.

Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination

PubMed Central

Lepora, Nathan F.; Fox, Charles W.; Evans, Mathew H.; Diamond, Mathew E.; Gurney, Kevin; Prescott, Tony J.

2012-01-01

Texture perception is studied here in a physical model of the rat whisker system consisting of a robot equipped with a biomimetic vibrissal sensor. Investigations of whisker motion in rodents have led to several explanations for texture discrimination, such as resonance or stick-slips. Meanwhile, electrophysiological studies of decision-making in monkeys have suggested a neural mechanism of evidence accumulation to threshold for competing percepts, described by a probabilistic model of Bayesian sequential analysis. For our robot whisker data, we find that variable reaction-time decision-making with sequential analysis performs better than the fixed response-time maximum-likelihood estimation. These probabilistic classifiers also use whatever available features of the whisker signals aid the discrimination, giving improved performance over a single-feature strategy, such as matching the peak power spectra of whisker vibrations. These results cast new light on how the various proposals for texture discrimination in rodents depend on the whisker contact mechanics and suggest the possibility of a common account of decision-making across mammalian species. PMID:22279155

A Probabilistic Assessment of the Next Geomagnetic Reversal

NASA Astrophysics Data System (ADS)

Buffett, Bruce; Davis, William

2018-02-01

Deterministic forecasts for the next geomagnetic reversal are not feasible due to large uncertainties in the present-day state of the Earth's core. A more practical approach relies on probabilistic assessments using paleomagnetic observations to characterize the amplitude of fluctuations in the geomagnetic dipole. We use paleomagnetic observations for the past 2 Myr to construct a stochastic model for the axial dipole field and apply well-established methods to evaluate the probability of the next geomagnetic reversal as a function of time. For a present-day axial dipole moment of 7.6 × 1022 A m2, the probability of the dipole entering a reversed state is less than 2% after 20 kyr. This probability rises to 11% after 50 kyr. An imminent geomagnetic reversal is not supported by paleomagnetic observations. The current rate of decline in the dipole moment is unusual but within the natural variability predicted by the stochastic model.

A Probabilistic Strategy for Understanding Action Selection

PubMed Central

Kim, Byounghoon; Basso, Michele A.

2010-01-01

Brain regions involved in transforming sensory signals into movement commands are the likely sites where decisions are formed. Once formed, a decision must be read-out from the activity of populations of neurons to produce a choice of action. How this occurs remains unresolved. We recorded from four superior colliculus (SC) neurons simultaneously while monkeys performed a target selection task. We implemented three models to gain insight into the computational principles underlying population coding of action selection. We compared the population vector average (PVA), winner-takes-all (WTA) and a Bayesian model, maximum a posteriori estimate (MAP) to determine which predicted choices most often. The probabilistic model predicted more trials correctly than both the WTA and the PVA. The MAP model predicted 81.88% whereas WTA predicted 71.11% and PVA/OLE predicted the least number of trials at 55.71 and 69.47%. Recovering MAP estimates using simulated, non-uniform priors that correlated with monkeys’ choice performance, improved the accuracy of the model by 2.88%. A dynamic analysis revealed that the MAP estimate evolved over time and the posterior probability of the saccade choice reached a maximum at the time of the saccade. MAP estimates also scaled with choice performance accuracy. Although there was overlap in the prediction abilities of all the models, we conclude that movement choice from populations of neurons may be best understood by considering frameworks based on probability. PMID:20147560

Assessment of global flood exposures - developing an appropriate approach

NASA Astrophysics Data System (ADS)

Millinship, Ian; Booth, Naomi

2015-04-01

Increasingly complex probabilistic catastrophe models have become the standard for quantitative flood risk assessments by re/insurance companies. On the one hand, probabilistic modelling of this nature is extremely useful; a large range of risk metrics can be output. However, they can be time consuming and computationally expensive to develop and run. Levels of uncertainty are persistently high despite, or perhaps because of, attempts to increase resolution and complexity. A cycle of dependency between modelling companies and re/insurers has developed whereby available models are purchased, models run, and both portfolio and model data 'improved' every year. This can lead to potential exposures in perils and territories that are not currently modelled being largely overlooked by companies, who may then face substantial and unexpected losses when large events occur in these areas. We present here an approach to assessing global flood exposures which reduces the scale and complexity of approach used and begins with the identification of hotspots where there is a significant exposure to flood risk. The method comprises four stages: i) compile consistent exposure information, ii) to apply reinsurance terms and conditions to calculate values exposed, iii) to assess the potential hazard using a global set of flood hazard maps, and iv) to identify potential risk 'hotspots' which include considerations of spatially and/or temporally clustered historical events, and local flood defences. This global exposure assessment is designed as a scoping exercise, and reveals areas or cities where the potential for accumulated loss is of significant interest to a reinsurance company, and for which there is no existing catastrophe model. These regions are then candidates for the development of deterministic scenarios, or probabilistic models. The key advantages of this approach will be discussed. These include simplicity and ability of business leaders to understand results, as well as ease and speed of analysis and the advantages this can offer in terms of monitoring changing exposures over time. Significantly, in many areas of the world, this increase in exposure is likely to have more of an impact on increasing catastrophe losses than potential anthropogenically driven changes in weather extremes.