A Gaussian Model-Based Probabilistic Approach for Pulse Transit Time Estimation.

PubMed

Jang, Dae-Geun; Park, Seung-Hun; Hahn, Minsoo

2016-01-01

In this paper, we propose a new probabilistic approach to pulse transit time (PTT) estimation using a Gaussian distribution model. It is motivated basically by the hypothesis that PTTs normalized by RR intervals follow the Gaussian distribution. To verify the hypothesis, we demonstrate the effects of arterial compliance on the normalized PTTs using the Moens-Korteweg equation. Furthermore, we observe a Gaussian distribution of the normalized PTTs on real data. In order to estimate the PTT using the hypothesis, we first assumed that R-waves in the electrocardiogram (ECG) can be correctly identified. The R-waves limit searching ranges to detect pulse peaks in the photoplethysmogram (PPG) and to synchronize the results with cardiac beats--i.e., the peaks of the PPG are extracted within the corresponding RR interval of the ECG as pulse peak candidates. Their probabilities of being the actual pulse peak are then calculated using a Gaussian probability function. The parameters of the Gaussian function are automatically updated when a new pulse peak is identified. This update makes the probability function adaptive to variations of cardiac cycles. Finally, the pulse peak is identified as the candidate with the highest probability. The proposed approach is tested on a database where ECG and PPG waveforms are collected simultaneously during the submaximal bicycle ergometer exercise test. The results are promising, suggesting that the method provides a simple but more accurate PTT estimation in real applications.

Bayesian Processor of Output for Probabilistic Quantitative Precipitation Forecasting

NASA Astrophysics Data System (ADS)

Krzysztofowicz, R.; Maranzano, C. J.

2006-05-01

The Bayesian Processor of Output (BPO) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It processes output from a numerical weather prediction (NWP) model and optimally fuses it with climatic data in order to quantify uncertainty about a predictand. The BPO is being tested by producing Probabilistic Quantitative Precipitation Forecasts (PQPFs) for a set of climatically diverse stations in the contiguous U.S. For each station, the PQPFs are produced for the same 6-h, 12-h, and 24-h periods up to 84- h ahead for which operational forecasts are produced by the AVN-MOS (Model Output Statistics technique applied to output fields from the Global Spectral Model run under the code name AVN). The inputs into the BPO are estimated as follows. The prior distribution is estimated from a (relatively long) climatic sample of the predictand; this sample is retrieved from the archives of the National Climatic Data Center. The family of the likelihood functions is estimated from a (relatively short) joint sample of the predictor vector and the predictand; this sample is retrieved from the same archive that the Meteorological Development Laboratory of the National Weather Service utilized to develop the AVN-MOS system. This talk gives a tutorial introduction to the principles and procedures behind the BPO, and highlights some results from the testing: a numerical example of the estimation of the BPO, and a comparative verification of the BPO forecasts and the MOS forecasts. It concludes with a list of demonstrated attributes of the BPO (vis- à-vis the MOS): more parsimonious definitions of predictors, more efficient extraction of predictive information, better representation of the distribution function of predictand, and equal or better performance (in terms of calibration and informativeness).

Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments

NASA Astrophysics Data System (ADS)

Ishizaki, N. N.; Dairaku, K.; Ueno, G.

2016-12-01

We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.

A Probabilistic Mass Estimation Algorithm for a Novel 7- Channel Capacitive Sample Verification Sensor

NASA Technical Reports Server (NTRS)

Wolf, Michael

2012-01-01

A document describes an algorithm created to estimate the mass placed on a sample verification sensor (SVS) designed for lunar or planetary robotic sample return missions. A novel SVS measures the capacitance between a rigid bottom plate and an elastic top membrane in seven locations. As additional sample material (soil and/or small rocks) is placed on the top membrane, the deformation of the membrane increases the capacitance. The mass estimation algorithm addresses both the calibration of each SVS channel, and also addresses how to combine the capacitances read from each of the seven channels into a single mass estimate. The probabilistic approach combines the channels according to the variance observed during the training phase, and provides not only the mass estimate, but also a value for the certainty of the estimate. SVS capacitance data is collected for known masses under a wide variety of possible loading scenarios, though in all cases, the distribution of sample within the canister is expected to be approximately uniform. A capacitance-vs-mass curve is fitted to this data, and is subsequently used to determine the mass estimate for the single channel s capacitance reading during the measurement phase. This results in seven different mass estimates, one for each SVS channel. Moreover, the variance of the calibration data is used to place a Gaussian probability distribution function (pdf) around this mass estimate. To blend these seven estimates, the seven pdfs are combined into a single Gaussian distribution function, providing the final mean and variance of the estimate. This blending technique essentially takes the final estimate as an average of the estimates of the seven channels, weighted by the inverse of the channel s variance.

Medial compartment knee osteoarthritis: age-stratified cost-effectiveness of total knee arthroplasty, unicompartmental knee arthroplasty, and high tibial osteotomy.

PubMed

Smith, William B; Steinberg, Joni; Scholtes, Stefan; Mcnamara, Iain R

2017-03-01

To compare the age-based cost-effectiveness of total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and high tibial osteotomy (HTO) for the treatment of medial compartment knee osteoarthritis (MCOA). A Markov model was used to simulate theoretical cohorts of patients 40, 50, 60, and 70 years of age undergoing primary TKA, UKA, or HTO. Costs and outcomes associated with initial and subsequent interventions were estimated by following these virtual cohorts over a 10-year period. Revision and mortality rates, costs, and functional outcome data were estimated from a systematic review of the literature. Probabilistic analysis was conducted to accommodate these parameters' inherent uncertainty, and both discrete and probabilistic sensitivity analyses were utilized to assess the robustness of the model's outputs to changes in key variables. HTO was most likely to be cost-effective in cohorts under 60, and UKA most likely in those 60 and over. Probabilistic results did not indicate one intervention to be significantly more cost-effective than another. The model was exquisitely sensitive to changes in utility (functional outcome), somewhat sensitive to changes in cost, and least sensitive to changes in 10-year revision risk. HTO may be the most cost-effective option when treating MCOA in younger patients, while UKA may be preferred in older patients. Functional utility is the primary driver of the cost-effectiveness of these interventions. For the clinician, this study supports HTO as a competitive treatment option in young patient populations. It also validates each one of the three interventions considered as potentially optimal, depending heavily on patient preferences and functional utility derived over time.

Probabilistic Reverse dOsimetry Estimating Exposure Distribution (PROcEED)

EPA Pesticide Factsheets

PROcEED is a web-based application used to conduct probabilistic reverse dosimetry calculations.The tool is used for estimating a distribution of exposure concentrations likely to have produced biomarker concentrations measured in a population.

Software for Probabilistic Risk Reduction

NASA Technical Reports Server (NTRS)

Hensley, Scott; Michel, Thierry; Madsen, Soren; Chapin, Elaine; Rodriguez, Ernesto

2004-01-01

A computer program implements a methodology, denoted probabilistic risk reduction, that is intended to aid in planning the development of complex software and/or hardware systems. This methodology integrates two complementary prior methodologies: (1) that of probabilistic risk assessment and (2) a risk-based planning methodology, implemented in a prior computer program known as Defect Detection and Prevention (DDP), in which multiple requirements and the beneficial effects of risk-mitigation actions are taken into account. The present methodology and the software are able to accommodate both process knowledge (notably of the efficacy of development practices) and product knowledge (notably of the logical structure of a system, the development of which one seeks to plan). Estimates of the costs and benefits of a planned development can be derived. Functional and non-functional aspects of software can be taken into account, and trades made among them. It becomes possible to optimize the planning process in the sense that it becomes possible to select the best suite of process steps and design choices to maximize the expectation of success while remaining within budget.

A probabilistic atlas of the cerebellar white matter.

PubMed

van Baarsen, K M; Kleinnijenhuis, M; Jbabdi, S; Sotiropoulos, S N; Grotenhuis, J A; van Cappellen van Walsum, A M

2016-01-01

Imaging of the cerebellar cortex, deep cerebellar nuclei and their connectivity are gaining attraction, due to the important role the cerebellum plays in cognition and motor control. Atlases of the cerebellar cortex and nuclei are used to locate regions of interest in clinical and neuroscience studies. However, the white matter that connects these relay stations is of at least similar functional importance. Damage to these cerebellar white matter tracts may lead to serious language, cognitive and emotional disturbances, although the pathophysiological mechanism behind it is still debated. Differences in white matter integrity between patients and controls might shed light on structure-function correlations. A probabilistic parcellation atlas of the cerebellar white matter would help these studies by facilitating automatic segmentation of the cerebellar peduncles, the localization of lesions and the comparison of white matter integrity between patients and controls. In this work a digital three-dimensional probabilistic atlas of the cerebellar white matter is presented, based on high quality 3T, 1.25mm resolution diffusion MRI data from 90 subjects participating in the Human Connectome Project. The white matter tracts were estimated using probabilistic tractography. Results over 90 subjects were symmetrical and trajectories of superior, middle and inferior cerebellar peduncles resembled the anatomy as known from anatomical studies. This atlas will contribute to a better understanding of cerebellar white matter architecture. It may eventually aid in defining structure-function correlations in patients with cerebellar disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Grid occupancy estimation for environment perception based on belief functions and PCR6

NASA Astrophysics Data System (ADS)

Moras, Julien; Dezert, Jean; Pannetier, Benjamin

2015-05-01

In this contribution, we propose to improve the grid map occupancy estimation method developed so far based on belief function modeling and the classical Dempster's rule of combination. Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the security (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy of each cell representing a small piece of the surrounding area of the robot must be estimated at first from sensors measurements (typically LIDAR, or camera), and then it must also be classified into different classes in order to get a complete and precise perception of the dynamic environment where the robot moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors. Mainly because the latter offers an interesting management of uncertainties when the quality of available information is low, and when the sources of information appear as conflicting. To improve the performances of the grid map estimation, we propose in this paper to replace Dempster's rule of combination by the PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache) Theory. As an illustrating scenario, we consider a platform moving in dynamic area and we compare our new realistic simulation results (based on a LIDAR sensor) with those obtained by the probabilistic and the classical belief-based approaches.

Photometric redshift estimation via deep learning. Generalized and pre-classification-less, image based, fully probabilistic redshifts

NASA Astrophysics Data System (ADS)

D'Isanto, A.; Polsterer, K. L.

2018-01-01

Context. The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods have utilized photometric features. Aims: We aim to develop a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete. Methods: A modified version of a deep convolutional network was combined 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) were applied as performance criteria. We have adopted a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS (DR9). Results: We show that the proposed method is able to predict redshift PDFs independently from the type of source, for example galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature. Conclusions: The presented method is extremely general and allows us to solve of any kind of probabilistic regression problems based on imaging data, for example estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems

NASA Astrophysics Data System (ADS)

Amelard, Robert; Clausi, David A.; Wong, Alexander

2016-11-01

Photoplethysmographic imaging (PPGI) is a widefield noncontact biophotonic technology able to remotely monitor cardiovascular function over anatomical areas. Although spatial context can provide insight into physiologically relevant sampling locations, existing PPGI systems rely on coarse spatial averaging with no anatomical priors for assessing arterial pulsatility. Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction. Using a data-driven approach, the model was constructed using a 23 participant sample with a large demographic variability (11/12 female/male, age 11 to 60 years, BMI 16.4 to 35.1 kg·m-2). Using time-synchronized ground-truth blood pulse waveforms, spatial correlation priors were computed and projected into a coaligned importance-weighted Cartesian space. A modified Parzen-Rosenblatt kernel density estimation method was used to compute the continuous resolution-agnostic probabilistic pulsatility model. The model identified locations that consistently exhibited pulsatility across the sample. Blood pulse waveform signals extracted with the model exhibited significantly stronger temporal correlation (W=35,p<0.01) and spectral SNR (W=31,p<0.01) compared to uniform spatial averaging. Heart rate estimation was in strong agreement with true heart rate [r2=0.9619, error (μ,σ)=(0.52,1.69) bpm].

Impact of refining the assessment of dietary exposure to cadmium in the European adult population.

PubMed

Ferrari, Pietro; Arcella, Davide; Heraud, Fanny; Cappé, Stefano; Fabiansson, Stefan

2013-01-01

Exposure assessment constitutes an important step in any risk assessment of potentially harmful substances present in food. The European Food Safety Authority (EFSA) first assessed dietary exposure to cadmium in Europe using a deterministic framework, resulting in mean values of exposure in the range of health-based guidance values. Since then, the characterisation of foods has been refined to better match occurrence and consumption data, and a new strategy to handle left-censoring in occurrence data was devised. A probabilistic assessment was performed and compared with deterministic estimates, using occurrence values at the European level and consumption data from 14 national dietary surveys. Mean estimates in the probabilistic assessment ranged from 1.38 (95% CI = 1.35-1.44) to 2.08 (1.99-2.23) µg kg⁻¹ bodyweight (bw) week⁻¹ across the different surveys, which were less than 10% lower than deterministic (middle bound) mean values that ranged from 1.50 to 2.20 µg kg⁻¹ bw week⁻¹. Probabilistic 95th percentile estimates of dietary exposure ranged from 2.65 (2.57-2.72) to 4.99 (4.62-5.38) µg kg⁻¹ bw week⁻¹, which were, with the exception of one survey, between 3% and 17% higher than middle-bound deterministic estimates. Overall, the proportion of subjects exceeding the tolerable weekly intake of 2.5 µg kg⁻¹ bw ranged from 14.8% (13.6-16.0%) to 31.2% (29.7-32.5%) according to the probabilistic assessment. The results of this work indicate that mean values of dietary exposure to cadmium in the European population were of similar magnitude using determinist or probabilistic assessments. For higher exposure levels, probabilistic estimates were almost consistently larger than deterministic counterparts, thus reflecting the impact of using the full distribution of occurrence values to determine exposure levels. It is considered prudent to use probabilistic methodology should exposure estimates be close to or exceeding health-based guidance values.

Connectivity-based parcellation of human cingulate cortex and its relation to functional specialization.

PubMed

Beckmann, Matthias; Johansen-Berg, Heidi; Rushworth, Matthew F S

2009-01-28

Whole-brain neuroimaging studies have demonstrated regional variations in function within human cingulate cortex. At the same time, regional variations in cingulate anatomical connections have been found in animal models. It has, however, been difficult to estimate the relationship between connectivity and function throughout the whole cingulate cortex within the human brain. In this study, magnetic resonance diffusion tractography was used to investigate cingulate probabilistic connectivity in the human brain with two approaches. First, an algorithm was used to search for regional variations in the probabilistic connectivity profiles of all cingulate cortex voxels with the whole of the rest of the brain. Nine subregions with distinctive connectivity profiles were identified. It was possible to characterize several distinct areas in the dorsal cingulate sulcal region. Several distinct regions were also found in subgenual and perigenual cortex. Second, the probabilities of connection between cingulate cortex and 11 predefined target regions of interest were calculated. Cingulate voxels with a high probability of connection with the different targets formed separate clusters within cingulate cortex. Distinct connectivity fingerprints characterized the likelihood of connections between the extracingulate target regions and the nine cingulate subregions. Last, a meta-analysis of 171 functional studies reporting cingulate activation was performed. Seven different cognitive conditions were selected and peak activation coordinates were plotted to create maps of functional localization within the cingulate cortex. Regional functional specialization was found to be related to regional differences in probabilistic anatomical connectivity.

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

Functional mechanisms of probabilistic inference in feature- and space-based attentional systems.

PubMed

Dombert, Pascasie L; Kuhns, Anna; Mengotti, Paola; Fink, Gereon R; Vossel, Simone

2016-11-15

Humans flexibly attend to features or locations and these processes are influenced by the probability of sensory events. We combined computational modeling of response times with fMRI to compare the functional correlates of (re-)orienting, and the modulation by probabilistic inference in spatial and feature-based attention systems. Twenty-four volunteers performed two task versions with spatial or color cues. Percentage of cue validity changed unpredictably. A hierarchical Bayesian model was used to derive trial-wise estimates of probability-dependent attention, entering the fMRI analysis as parametric regressors. Attentional orienting activated a dorsal frontoparietal network in both tasks, without significant parametric modulation. Spatially invalid trials activated a bilateral frontoparietal network and the precuneus, while invalid feature trials activated the left intraparietal sulcus (IPS). Probability-dependent attention modulated activity in the precuneus, left posterior IPS, middle occipital gyrus, and right temporoparietal junction for spatial attention, and in the left anterior IPS for feature-based and spatial attention. These findings provide novel insights into the generality and specificity of the functional basis of attentional control. They suggest that probabilistic inference can distinctively affect each attentional subsystem, but that there is an overlap in the left IPS, which responds to both spatial and feature-based expectancy violations. Copyright © 2016 Elsevier Inc. All rights reserved.

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...

Real-time Mainshock Forecast by Statistical Discrimination of Foreshock Clusters

NASA Astrophysics Data System (ADS)

Nomura, S.; Ogata, Y.

2016-12-01

Foreshock discremination is one of the most effective ways for short-time forecast of large main shocks. Though many large earthquakes accompany their foreshocks, discreminating them from enormous small earthquakes is difficult and only probabilistic evaluation from their spatio-temporal features and magnitude evolution may be available. Logistic regression is the statistical learning method best suited to such binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Statistical learning methods can keep learning discreminating features from updating catalog and give probabilistic recognition of forecast in real time. We estimated a non-linear function of foreshock proportion by smooth spline bases and evaluate the possibility of foreshocks by the logit function. In this study, we classified foreshocks from earthquake catalog by the Japan Meteorological Agency by single-link clustering methods and learned spatial and temporal features of foreshocks by the probability density ratio estimation. We use the epicentral locations, time spans and difference in magnitudes for learning and forecasting. Magnitudes of main shocks are also predicted our method by incorporating b-values into our method. We discuss the spatial pattern of foreshocks from the classifier composed by our model. We also implement a back test to validate predictive performance of the model by this catalog.

Probabilistic Methodology for Estimation of Number and Economic Loss (Cost) of Future Landslides in the San Francisco Bay Region, California

USGS Publications Warehouse

Crovelli, Robert A.; Coe, Jeffrey A.

2008-01-01

The Probabilistic Landslide Assessment Cost Estimation System (PLACES) presented in this report estimates the number and economic loss (cost) of landslides during a specified future time in individual areas, and then calculates the sum of those estimates. The analytic probabilistic methodology is based upon conditional probability theory and laws of expectation and variance. The probabilistic methodology is expressed in the form of a Microsoft Excel computer spreadsheet program. Using historical records, the PLACES spreadsheet is used to estimate the number of future damaging landslides and total damage, as economic loss, from future landslides caused by rainstorms in 10 counties of the San Francisco Bay region in California. Estimates are made for any future 5-year period of time. The estimated total number of future damaging landslides for the entire 10-county region during any future 5-year period of time is about 330. Santa Cruz County has the highest estimated number of damaging landslides (about 90), whereas Napa, San Francisco, and Solano Counties have the lowest estimated number of damaging landslides (5?6 each). Estimated direct costs from future damaging landslides for the entire 10-county region for any future 5-year period are about US $76 million (year 2000 dollars). San Mateo County has the highest estimated costs ($16.62 million), and Solano County has the lowest estimated costs (about $0.90 million). Estimated direct costs are also subdivided into public and private costs.

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 PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

A Bayesian approach to assessing the uncertainty in estimating bioconcentration factors in earthworms--the example of quinoxyfen.

PubMed

Fragoulis, George; Merli, Annalisa; Reeves, Graham; Meregalli, Giovanna; Stenberg, Kristofer; Tanaka, Taku; Capri, Ettore

2011-06-01

Quinoxyfen is a fungicide of the phenoxyquinoline class used to control powdery mildew, Uncinula necator (Schw.) Burr. Owing to its high persistence and strong sorption in soil, it could represent a risk for soil organisms if they are exposed at ecologically relevant concentrations. The objective of this paper is to predict the bioconcentration factors (BCFs) of quinoxyfen in earthworms, selected as a representative soil organism, and to assess the uncertainty in the estimation of this parameter. Three fields in each of four vineyards in southern and northern Italy were sampled over two successive years. The measured BCFs varied over time, possibly owing to seasonal changes and the consequent changes in behaviour and ecology of earthworms. Quinoxyfen did not accumulate in soil, as the mean soil concentrations at the end of the 2 year monitoring period ranged from 9.16 to 16.0 µg kg⁻¹ dw for the Verona province and from 23.9 to 37.5 µg kg⁻¹ dw for the Taranto province, with up to eight applications per season. To assess the uncertainty of the BCF in earthworms, a probabilistic approach was used, firstly by building with weighted bootstrapping techniques a generic probabilistic density function (PDF) accounting for variability and incompleteness of knowledge. The generic PDF was then used to derive prior distribution functions, which, by application of Bayes' theorem, were updated with the new measurements and a posterior distribution was finally created. The study is a good example of probabilistic risk assessment. The means of mean and SD posterior estimates of log BCFworm (2.06, 0.91) are the 'best estimate values'. Further risk assessment of quinoxyfen and other phenoxyquinoline fungicides and realistic representative scenarios for modelling exercises required for future authorization and post-authorization requirements can now use this value as input. Copyright © 2011 Society of Chemical Industry.

A Unified Probabilistic Framework for Dose-Response Assessment of Human Health Effects.

PubMed

Chiu, Weihsueh A; Slob, Wout

2015-12-01

When chemical health hazards have been identified, probabilistic dose-response assessment ("hazard characterization") quantifies uncertainty and/or variability in toxicity as a function of human exposure. Existing probabilistic approaches differ for different types of endpoints or modes-of-action, lacking a unifying framework. We developed a unified framework for probabilistic dose-response assessment. We established a framework based on four principles: a) individual and population dose responses are distinct; b) dose-response relationships for all (including quantal) endpoints can be recast as relating to an underlying continuous measure of response at the individual level; c) for effects relevant to humans, "effect metrics" can be specified to define "toxicologically equivalent" sizes for this underlying individual response; and d) dose-response assessment requires making adjustments and accounting for uncertainty and variability. We then derived a step-by-step probabilistic approach for dose-response assessment of animal toxicology data similar to how nonprobabilistic reference doses are derived, illustrating the approach with example non-cancer and cancer datasets. Probabilistically derived exposure limits are based on estimating a "target human dose" (HDMI), which requires risk management-informed choices for the magnitude (M) of individual effect being protected against, the remaining incidence (I) of individuals with effects ≥ M in the population, and the percent confidence. In the example datasets, probabilistically derived 90% confidence intervals for HDMI values span a 40- to 60-fold range, where I = 1% of the population experiences ≥ M = 1%-10% effect sizes. Although some implementation challenges remain, this unified probabilistic framework can provide substantially more complete and transparent characterization of chemical hazards and support better-informed risk management decisions.

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 ...

Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data

NASA Astrophysics Data System (ADS)

Glüsenkamp, Thorsten

2018-06-01

Parameter estimation in HEP experiments often involves Monte Carlo simulation to model the experimental response function. A typical application are forward-folding likelihood analyses with re-weighting, or time-consuming minimization schemes with a new simulation set for each parameter value. Problematically, the finite size of such Monte Carlo samples carries intrinsic uncertainty that can lead to a substantial bias in parameter estimation if it is neglected and the sample size is small. We introduce a probabilistic treatment of this problem by replacing the usual likelihood functions with novel generalized probability distributions that incorporate the finite statistics via suitable marginalization. These new PDFs are analytic, and can be used to replace the Poisson, multinomial, and sample-based unbinned likelihoods, which covers many use cases in high-energy physics. In the limit of infinite statistics, they reduce to the respective standard probability distributions. In the general case of arbitrary Monte Carlo weights, the expressions involve the fourth Lauricella function FD, for which we find a new finite-sum representation in a certain parameter setting. The result also represents an exact form for Carlson's Dirichlet average Rn with n > 0, and thereby an efficient way to calculate the probability generating function of the Dirichlet-multinomial distribution, the extended divided difference of a monomial, or arbitrary moments of univariate B-splines. We demonstrate the bias reduction of our approach with a typical toy Monte Carlo problem, estimating the normalization of a peak in a falling energy spectrum, and compare the results with previously published methods from the literature.

Probabilistic seismic loss estimation via endurance time method

NASA Astrophysics Data System (ADS)

Tafakori, Ehsan; Pourzeynali, Saeid; Estekanchi, Homayoon E.

2017-01-01

Probabilistic Seismic Loss Estimation is a methodology used as a quantitative and explicit expression of the performance of buildings using terms that address the interests of both owners and insurance companies. Applying the ATC 58 approach for seismic loss assessment of buildings requires using Incremental Dynamic Analysis (IDA), which needs hundreds of time-consuming analyses, which in turn hinders its wide application. The Endurance Time Method (ETM) is proposed herein as part of a demand propagation prediction procedure and is shown to be an economical alternative to IDA. Various scenarios were considered to achieve this purpose and their appropriateness has been evaluated using statistical methods. The most precise and efficient scenario was validated through comparison against IDA driven response predictions of 34 code conforming benchmark structures and was proven to be sufficiently precise while offering a great deal of efficiency. The loss values were estimated by replacing IDA with the proposed ETM-based procedure in the ATC 58 procedure and it was found that these values suffer from varying inaccuracies, which were attributed to the discretized nature of damage and loss prediction functions provided by ATC 58.

Topics in Probabilistic Judgment Aggregation

ERIC Educational Resources Information Center

Wang, Guanchun

2011-01-01

This dissertation is a compilation of several studies that are united by their relevance to probabilistic judgment aggregation. In the face of complex and uncertain events, panels of judges are frequently consulted to provide probabilistic forecasts, and aggregation of such estimates in groups often yield better results than could have been made…

Estimation of the probability of success in petroleum exploration

USGS Publications Warehouse

Davis, J.C.

1977-01-01

A probabilistic model for oil exploration can be developed by assessing the conditional relationship between perceived geologic variables and the subsequent discovery of petroleum. Such a model includes two probabilistic components, the first reflecting the association between a geologic condition (structural closure, for example) and the occurrence of oil, and the second reflecting the uncertainty associated with the estimation of geologic variables in areas of limited control. Estimates of the conditional relationship between geologic variables and subsequent production can be found by analyzing the exploration history of a "training area" judged to be geologically similar to the exploration area. The geologic variables are assessed over the training area using an historical subset of the available data, whose density corresponds to the present control density in the exploration area. The success or failure of wells drilled in the training area subsequent to the time corresponding to the historical subset provides empirical estimates of the probability of success conditional upon geology. Uncertainty in perception of geological conditions may be estimated from the distribution of errors made in geologic assessment using the historical subset of control wells. These errors may be expressed as a linear function of distance from available control. Alternatively, the uncertainty may be found by calculating the semivariogram of the geologic variables used in the analysis: the two procedures will yield approximately equivalent results. The empirical probability functions may then be transferred to the exploration area and used to estimate the likelihood of success of specific exploration plays. These estimates will reflect both the conditional relationship between the geological variables used to guide exploration and the uncertainty resulting from lack of control. The technique is illustrated with case histories from the mid-Continent area of the U.S.A. ?? 1977 Plenum Publishing Corp.

Comparison of multi-subject ICA methods for analysis of fMRI data

PubMed Central

Erhardt, Erik Barry; Rachakonda, Srinivas; Bedrick, Edward; Allen, Elena; Adali, Tülay; Calhoun, Vince D.

2010-01-01

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. PMID:21162045

Probabilistic segmentation and intensity estimation for microarray images.

PubMed

Gottardo, Raphael; Besag, Julian; Stephens, Matthew; Murua, Alejandro

2006-01-01

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

The application of probabilistic fracture analysis to residual life evaluation of embrittled reactor vessels

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

Dickson, T.L.; Simonen, F.A.

1992-05-01

Probabilistic fracture mechanics analysis is a major element of comprehensive probabilistic methodology on which current NRC regulatory requirements for pressurized water reactor vessel integrity evaluation are based. Computer codes such as OCA-P and VISA-II perform probabilistic fracture analyses to estimate the increase in vessel failure probability that occurs as the vessel material accumulates radiation damage over the operating life of the vessel. The results of such analyses, when compared with limits of acceptable failure probabilities, provide an estimation of the residual life of a vessel. Such codes can be applied to evaluate the potential benefits of plant-specific mitigating actions designedmore » to reduce the probability of failure of a reactor vessel. 10 refs.« less

The application of probabilistic fracture analysis to residual life evaluation of embrittled reactor vessels

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

Dickson, T.L.; Simonen, F.A.

1992-01-01

Probabilistic fracture mechanics analysis is a major element of comprehensive probabilistic methodology on which current NRC regulatory requirements for pressurized water reactor vessel integrity evaluation are based. Computer codes such as OCA-P and VISA-II perform probabilistic fracture analyses to estimate the increase in vessel failure probability that occurs as the vessel material accumulates radiation damage over the operating life of the vessel. The results of such analyses, when compared with limits of acceptable failure probabilities, provide an estimation of the residual life of a vessel. Such codes can be applied to evaluate the potential benefits of plant-specific mitigating actions designedmore » to reduce the probability of failure of a reactor vessel. 10 refs.« less

A Unified Probabilistic Framework for Dose–Response Assessment of Human Health Effects

PubMed Central

Slob, Wout

2015-01-01

Background When chemical health hazards have been identified, probabilistic dose–response assessment (“hazard characterization”) quantifies uncertainty and/or variability in toxicity as a function of human exposure. Existing probabilistic approaches differ for different types of endpoints or modes-of-action, lacking a unifying framework. Objectives We developed a unified framework for probabilistic dose–response assessment. Methods We established a framework based on four principles: a) individual and population dose responses are distinct; b) dose–response relationships for all (including quantal) endpoints can be recast as relating to an underlying continuous measure of response at the individual level; c) for effects relevant to humans, “effect metrics” can be specified to define “toxicologically equivalent” sizes for this underlying individual response; and d) dose–response assessment requires making adjustments and accounting for uncertainty and variability. We then derived a step-by-step probabilistic approach for dose–response assessment of animal toxicology data similar to how nonprobabilistic reference doses are derived, illustrating the approach with example non-cancer and cancer datasets. Results Probabilistically derived exposure limits are based on estimating a “target human dose” (HDMI), which requires risk management–informed choices for the magnitude (M) of individual effect being protected against, the remaining incidence (I) of individuals with effects ≥ M in the population, and the percent confidence. In the example datasets, probabilistically derived 90% confidence intervals for HDMI values span a 40- to 60-fold range, where I = 1% of the population experiences ≥ M = 1%–10% effect sizes. Conclusions Although some implementation challenges remain, this unified probabilistic framework can provide substantially more complete and transparent characterization of chemical hazards and support better-informed risk management decisions. Citation Chiu WA, Slob W. 2015. A unified probabilistic framework for dose–response assessment of human health effects. Environ Health Perspect 123:1241–1254; http://dx.doi.org/10.1289/ehp.1409385 PMID:26006063

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

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.

Inference in the brain: Statistics flowing in redundant population codes

PubMed Central

Pitkow, Xaq; Angelaki, Dora E

2017-01-01

It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors. PMID:28595050

A probabilistic framework for single-sensor acoustic emission source localization in thin metallic plates

NASA Astrophysics Data System (ADS)

Ebrahimkhanlou, Arvin; Salamone, Salvatore

2017-09-01

Tracking edge-reflected acoustic emission (AE) waves can allow the localization of their sources. Specifically, in bounded isotropic plate structures, only one sensor may be used to perform these source localizations. The primary goal of this paper is to develop a three-step probabilistic framework to quantify the uncertainties associated with such single-sensor localizations. According to this framework, a probabilistic approach is first used to estimate the direct distances between AE sources and the sensor. Then, an analytical model is used to reconstruct the envelope of edge-reflected AE signals based on the source-to-sensor distance estimations and their first arrivals. Finally, the correlation between the probabilistically reconstructed envelopes and recorded AE signals are used to estimate confidence contours for the location of AE sources. To validate the proposed framework, Hsu-Nielsen pencil lead break (PLB) tests were performed on the surface as well as the edges of an aluminum plate. The localization results show that the estimated confidence contours surround the actual source locations. In addition, the performance of the framework was tested in a noisy environment simulated by two dummy transducers and an arbitrary wave generator. The results show that in low-noise environments, the shape and size of the confidence contours depend on the sources and their locations. However, at highly noisy environments, the size of the confidence contours monotonically increases with the noise floor. Such probabilistic results suggest that the proposed probabilistic framework could thus provide more comprehensive information regarding the location of AE sources.

A MAP-based image interpolation method via Viterbi decoding of Markov chains of interpolation functions.

PubMed

Vedadi, Farhang; Shirani, Shahram

2014-01-01

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

De novo identification of highly diverged protein repeats by probabilistic consistency.

PubMed

Biegert, A; Söding, J

2008-03-15

An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying diverged repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller fragments. We present HHrepID, a method for the de novo identification of repeats in protein sequences. It is able to detect the sequence signature of structural repeats in many proteins that have not yet been known to possess internal sequence symmetry, such as outer membrane beta-barrels. HHrepID uses HMM-HMM comparison to exploit evolutionary information in the form of multiple sequence alignments of homologs. In contrast to a previous method, the new method (1) generates a multiple alignment of repeats; (2) utilizes the transitive nature of homology through a novel merging procedure with fully probabilistic treatment of alignments; (3) improves alignment quality through an algorithm that maximizes the expected accuracy; (4) is able to identify different kinds of repeats within complex architectures by a probabilistic domain boundary detection method and (5) improves sensitivity through a new approach to assess statistical significance. Server: http://toolkit.tuebingen.mpg.de/hhrepid; Executables: ftp://ftp.tuebingen.mpg.de/pub/protevo/HHrepID

Great Balls of Fire: A probabilistic approach to quantify the hazard related to ballistics - A case study at La Fossa volcano, Vulcano Island, Italy

NASA Astrophysics Data System (ADS)

Biass, Sébastien; Falcone, Jean-Luc; Bonadonna, Costanza; Di Traglia, Federico; Pistolesi, Marco; Rosi, Mauro; Lestuzzi, Pierino

2016-10-01

We present a probabilistic approach to quantify the hazard posed by volcanic ballistic projectiles (VBP) and their potential impact on the built environment. A model named Great Balls of Fire (GBF) is introduced to describe ballistic trajectories of VBPs accounting for a variable drag coefficient and topography. It relies on input parameters easily identifiable in the field and is designed to model large numbers of VBPs stochastically. Associated functions come with the GBF code to post-process model outputs into a comprehensive probabilistic hazard assessment for VBP impacts. Outcomes include probability maps to exceed given thresholds of kinetic energies at impact, hazard curves and probabilistic isoenergy maps. Probabilities are calculated either on equally-sized pixels or zones of interest. The approach is calibrated, validated and applied to La Fossa volcano, Vulcano Island (Italy). We constructed a generic eruption scenario based on stratigraphic studies and numerical inversions of the 1888-1890 long-lasting Vulcanian cycle of La Fossa. Results suggest a ~ 10- 2% probability of occurrence of VBP impacts with kinetic energies ≤ 104 J at the touristic locality of Porto. In parallel, the vulnerability to roof perforation was estimated by combining field observations and published literature, allowing for a first estimate of the potential impact of VBPs during future Vulcanian eruptions. Results indicate a high physical vulnerability to the VBP hazard, and, consequently, half of the building stock having a ≥ 2.5 × 10- 3% probability of roof perforation.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

Probabilistic location estimation of acoustic emission sources in isotropic plates with one sensor

NASA Astrophysics Data System (ADS)

Ebrahimkhanlou, Arvin; Salamone, Salvatore

2017-04-01

This paper presents a probabilistic acoustic emission (AE) source localization algorithm for isotropic plate structures. The proposed algorithm requires only one sensor and uniformly monitors the entire area of such plates without any blind zones. In addition, it takes a probabilistic approach and quantifies localization uncertainties. The algorithm combines a modal acoustic emission (MAE) and a reflection-based technique to obtain information pertaining to the location of AE sources. To estimate confidence contours for the location of sources, uncertainties are quantified and propagated through the two techniques. The approach was validated using standard pencil lead break (PLB) tests on an Aluminum plate. The results demonstrate that the proposed source localization algorithm successfully estimates confidence contours for the location of AE sources.

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 atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

PubMed Central

Figley, Teresa D.; Bhullar, Navdeep; Courtney, Susan M.; Figley, Chase R.

2015-01-01

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses. PMID:26578930

DISCOUNTING OF DELAYED AND PROBABILISTIC LOSSES OVER A WIDE RANGE OF AMOUNTS

PubMed Central

Green, Leonard; Myerson, Joel; Oliveira, Luís; Chang, Seo Eun

2014-01-01

The present study examined delay and probability discounting of hypothetical monetary losses over a wide range of amounts (from $20 to $500,000) in order to determine how amount affects the parameters of the hyperboloid discounting function. In separate conditions, college students chose between immediate payments and larger, delayed payments and between certain payments and larger, probabilistic payments. The hyperboloid function accurately described both types of discounting, and amount of loss had little or no systematic effect on the degree of discounting. Importantly, the amount of loss also had little systematic effect on either the rate parameter or the exponent of the delay and probability discounting functions. The finding that the parameters of the hyperboloid function remain relatively constant across a wide range of amounts of delayed and probabilistic loss stands in contrast to the robust amount effects observed with delayed and probabilistic rewards. At the individual level, the degree to which delayed losses were discounted was uncorrelated with the degree to which probabilistic losses were discounted, and delay and probability loaded on two separate factors, similar to what is observed with delayed and probabilistic rewards. Taken together, these findings argue that although delay and probability discounting involve fundamentally different decision-making mechanisms, nevertheless the discounting of delayed and probabilistic losses share an insensitivity to amount that distinguishes it from the discounting of delayed and probabilistic gains. PMID:24745086

Multivariate Probabilistic Analysis of an Hydrological Model

NASA Astrophysics Data System (ADS)

Franceschini, Samuela; Marani, Marco

2010-05-01

Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model response is highly nonlinear. Higher-order approximations can provide more accurate estimations, but reduce the numerical advantage of the LiM. The results of the uncertainty analysis identify the main sources of uncertainty in the computation of river discharge. In this particular case the spatial variability of rainfall and the model parameters uncertainty are shown to have the greatest impact on discharge evaluation. This, in turn, highlights the need to support any estimated hydrological response with probability information and risk analysis results in order to provide a robust, systematic framework for decision making.

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

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

A Control Variate Method for Probabilistic Performance Assessment. Improved Estimates for Mean Performance Quantities of Interest

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

MacKinnon, Robert J.; Kuhlman, Kristopher L

2016-05-01

We present a method of control variates for calculating improved estimates for mean performance quantities of interest, E(PQI) , computed from Monte Carlo probabilistic simulations. An example of a PQI is the concentration of a contaminant at a particular location in a problem domain computed from simulations of transport in porous media. To simplify the presentation, the method is described in the setting of a one- dimensional elliptical model problem involving a single uncertain parameter represented by a probability distribution. The approach can be easily implemented for more complex problems involving multiple uncertain parameters and in particular for application tomore » probabilistic performance assessment of deep geologic nuclear waste repository systems. Numerical results indicate the method can produce estimates of E(PQI)having superior accuracy on coarser meshes and reduce the required number of simulations needed to achieve an acceptable estimate.« less

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Fully probabilistic earthquake source inversion on teleseismic scales

NASA Astrophysics Data System (ADS)

Stähler, Simon; Sigloch, Karin

2017-04-01

Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters but also estimates of their uncertainties are of great practical importance. We have developed a method of fully Bayesian inference for source parameters, based on measurements of waveform cross-correlation between broadband, teleseismic body-wave observations and their modelled counterparts. This approach yields not only depth and moment tensor estimates but also source time functions. These unknowns are parameterised efficiently by harnessing as prior knowledge solutions from a large number of non-Bayesian inversions. The source time function is expressed as a weighted sum of a small number of empirical orthogonal functions, which were derived from a catalogue of >1000 source time functions (STFs) by a principal component analysis. We use a likelihood model based on the cross-correlation misfit between observed and predicted waveforms. The resulting ensemble of solutions provides full uncertainty and covariance information for the source parameters, and permits propagating these source uncertainties into travel time estimates used for seismic tomography. The computational effort is such that routine, global estimation of earthquake mechanisms and source time functions from teleseismic broadband waveforms is feasible. A prerequisite for Bayesian inference is the proper characterisation of the noise afflicting the measurements. We show that, for realistic broadband body-wave seismograms, the systematic error due to an incomplete physical model affects waveform misfits more strongly than random, ambient background noise. In this situation, the waveform cross-correlation coefficient CC, or rather its decorrelation D = 1 - CC, performs more robustly as a misfit criterion than ℓp norms, more commonly used as sample-by-sample measures of misfit based on distances between individual time samples. From a set of over 900 user-supervised, deterministic earthquake source solutions treated as a quality-controlled reference, we derive the noise distribution on signal decorrelation D of the broadband seismogram fits between observed and modelled waveforms. The noise on D is found to approximately follow a log-normal distribution, a fortunate fact that readily accommodates the formulation of an empirical likelihood function for D for our multivariate problem. The first and second moments of this multivariate distribution are shown to depend mostly on the signal-to-noise ratio (SNR) of the CC measurements and on the back-azimuthal distances of seismic stations. References: Stähler, S. C. and Sigloch, K.: Fully probabilistic seismic source inversion - Part 1: Efficient parameterisation, Solid Earth, 5, 1055-1069, doi:10.5194/se-5-1055-2014, 2014. Stähler, S. C. and Sigloch, K.: Fully probabilistic seismic source inversion - Part 2: Modelling errors and station covariances, Solid Earth, 7, 1521-1536, doi:10.5194/se-7-1521-2016, 2016.

A probabilistic approach for the estimation of earthquake source parameters from spectral inversion

NASA Astrophysics Data System (ADS)

Supino, M.; Festa, G.; Zollo, A.

2017-12-01

The amplitude spectrum of a seismic signal related to an earthquake source carries information about the size of the rupture, moment, stress and energy release. Furthermore, it can be used to characterize the Green's function of the medium crossed by the seismic waves. We describe the earthquake amplitude spectrum assuming a generalized Brune's (1970) source model, and direct P- and S-waves propagating in a layered velocity model, characterized by a frequency-independent Q attenuation factor. The observed displacement spectrum depends indeed on three source parameters, the seismic moment (through the low-frequency spectral level), the corner frequency (that is a proxy of the fault length) and the high-frequency decay parameter. These parameters are strongly correlated each other and with the quality factor Q; a rigorous estimation of the associated uncertainties and parameter resolution is thus needed to obtain reliable estimations.In this work, the uncertainties are characterized adopting a probabilistic approach for the parameter estimation. Assuming an L2-norm based misfit function, we perform a global exploration of the parameter space to find the absolute minimum of the cost function and then we explore the cost-function associated joint a-posteriori probability density function around such a minimum, to extract the correlation matrix of the parameters. The global exploration relies on building a Markov chain in the parameter space and on combining a deterministic minimization with a random exploration of the space (basin-hopping technique). The joint pdf is built from the misfit function using the maximum likelihood principle and assuming a Gaussian-like distribution of the parameters. It is then computed on a grid centered at the global minimum of the cost-function. The numerical integration of the pdf finally provides mean, variance and correlation matrix associated with the set of best-fit parameters describing the model. Synthetic tests are performed to investigate the robustness of the method and uncertainty propagation from the data-space to the parameter space. Finally, the method is applied to characterize the source parameters of the earthquakes occurring during the 2016-2017 Central Italy sequence, with the goal of investigating the source parameter scaling with magnitude.

Study on the Evaluation Method for Fault Displacement: Probabilistic Approach Based on Japanese Earthquake Rupture Data - Principal fault displacements -

NASA Astrophysics Data System (ADS)

Kitada, N.; Inoue, N.; Tonagi, M.

2016-12-01

The purpose of Probabilistic Fault Displacement Hazard Analysis (PFDHA) is estimate fault displacement values and its extent of the impact. There are two types of fault displacement related to the earthquake fault: principal fault displacement and distributed fault displacement. Distributed fault displacement should be evaluated in important facilities, such as Nuclear Installations. PFDHA estimates principal fault and distributed fault displacement. For estimation, PFDHA uses distance-displacement functions, which are constructed from field measurement data. We constructed slip distance relation of principal fault displacement based on Japanese strike and reverse slip earthquakes in order to apply to Japan area that of subduction field. However, observed displacement data are sparse, especially reverse faults. Takao et al. (2013) tried to estimate the relation using all type fault systems (reverse fault and strike slip fault). After Takao et al. (2013), several inland earthquakes were occurred in Japan, so in this time, we try to estimate distance-displacement functions each strike slip fault type and reverse fault type especially add new fault displacement data set. To normalized slip function data, several criteria were provided by several researchers. We normalized principal fault displacement data based on several methods and compared slip-distance functions. The normalized by total length of Japanese reverse fault data did not show particular trend slip distance relation. In the case of segmented data, the slip-distance relationship indicated similar trend as strike slip faults. We will also discuss the relation between principal fault displacement distributions with source fault character. According to slip distribution function (Petersen et al., 2011), strike slip fault type shows the ratio of normalized displacement are decreased toward to the edge of fault. However, the data set of Japanese strike slip fault data not so decrease in the end of the fault. This result indicates that the fault displacement is difficult to appear at the edge of the fault displacement in Japan. This research was part of the 2014-2015 research project `Development of evaluating method for fault displacement` by the Secretariat of Nuclear Regulation Authority (NRA), Japan.

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 1. Theory

USGS Publications Warehouse

Yen, Chung-Cheng; Guymon, Gary L.

1990-01-01

An efficient probabilistic model is developed and cascaded with a deterministic model for predicting water table elevations in regional aquifers. The objective is to quantify model uncertainty where precise estimates of water table elevations may be required. The probabilistic model is based on the two-point probability method which only requires prior knowledge of uncertain variables mean and coefficient of variation. The two-point estimate method is theoretically developed and compared with the Monte Carlo simulation method. The results of comparisons using hypothetical determinisitic problems indicate that the two-point estimate method is only generally valid for linear problems where the coefficients of variation of uncertain parameters (for example, storage coefficient and hydraulic conductivity) is small. The two-point estimate method may be applied to slightly nonlinear problems with good results, provided coefficients of variation are small. In such cases, the two-point estimate method is much more efficient than the Monte Carlo method provided the number of uncertain variables is less than eight.

An Efficient Deterministic-Probabilistic Approach to Modeling Regional Groundwater Flow: 1. Theory

NASA Astrophysics Data System (ADS)

Yen, Chung-Cheng; Guymon, Gary L.

1990-07-01

An efficient probabilistic model is developed and cascaded with a deterministic model for predicting water table elevations in regional aquifers. The objective is to quantify model uncertainty where precise estimates of water table elevations may be required. The probabilistic model is based on the two-point probability method which only requires prior knowledge of uncertain variables mean and coefficient of variation. The two-point estimate method is theoretically developed and compared with the Monte Carlo simulation method. The results of comparisons using hypothetical determinisitic problems indicate that the two-point estimate method is only generally valid for linear problems where the coefficients of variation of uncertain parameters (for example, storage coefficient and hydraulic conductivity) is small. The two-point estimate method may be applied to slightly nonlinear problems with good results, provided coefficients of variation are small. In such cases, the two-point estimate method is much more efficient than the Monte Carlo method provided the number of uncertain variables is less than eight.

Essays on variational approximation techniques for stochastic optimization problems

NASA Astrophysics Data System (ADS)

Deride Silva, Julio A.

This dissertation presents five essays on approximation and modeling techniques, based on variational analysis, applied to stochastic optimization problems. It is divided into two parts, where the first is devoted to equilibrium problems and maxinf optimization, and the second corresponds to two essays in statistics and uncertainty modeling. Stochastic optimization lies at the core of this research as we were interested in relevant equilibrium applications that contain an uncertain component, and the design of a solution strategy. In addition, every stochastic optimization problem relies heavily on the underlying probability distribution that models the uncertainty. We studied these distributions, in particular, their design process and theoretical properties such as their convergence. Finally, the last aspect of stochastic optimization that we covered is the scenario creation problem, in which we described a procedure based on a probabilistic model to create scenarios for the applied problem of power estimation of renewable energies. In the first part, Equilibrium problems and maxinf optimization, we considered three Walrasian equilibrium problems: from economics, we studied a stochastic general equilibrium problem in a pure exchange economy, described in Chapter 3, and a stochastic general equilibrium with financial contracts, in Chapter 4; finally from engineering, we studied an infrastructure planning problem in Chapter 5. We stated these problems as belonging to the maxinf optimization class and, in each instance, we provided an approximation scheme based on the notion of lopsided convergence and non-concave duality. This strategy is the foundation of the augmented Walrasian algorithm, whose convergence is guaranteed by lopsided convergence, that was implemented computationally, obtaining numerical results for relevant examples. The second part, Essays about statistics and uncertainty modeling, contains two essays covering a convergence problem for a sequence of estimators, and a problem for creating probabilistic scenarios on renewable energies estimation. In Chapter 7 we re-visited one of the "folk theorems" in statistics, where a family of Bayes estimators under 0-1 loss functions is claimed to converge to the maximum a posteriori estimator. This assertion is studied under the scope of the hypo-convergence theory, and the density functions are included in the class of upper semicontinuous functions. We conclude this chapter with an example in which the convergence does not hold true, and we provided sufficient conditions that guarantee convergence. The last chapter, Chapter 8, addresses the important topic of creating probabilistic scenarios for solar power generation. Scenarios are a fundamental input for the stochastic optimization problem of energy dispatch, especially when incorporating renewables. We proposed a model designed to capture the constraints induced by physical characteristics of the variables based on the application of an epi-spline density estimation along with a copula estimation, in order to account for partial correlations between variables.

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.

Location error uncertainties - an advanced using of probabilistic inverse theory

NASA Astrophysics Data System (ADS)

Debski, Wojciech

2016-04-01

The spatial location of sources of seismic waves is one of the first tasks when transient waves from natural (uncontrolled) sources are analyzed in many branches of physics, including seismology, oceanology, to name a few. Source activity and its spatial variability in time, the geometry of recording network, the complexity and heterogeneity of wave velocity distribution are all factors influencing the performance of location algorithms and accuracy of the achieved results. While estimating of the earthquake foci location is relatively simple a quantitative estimation of the location accuracy is really a challenging task even if the probabilistic inverse method is used because it requires knowledge of statistics of observational, modelling, and apriori uncertainties. In this presentation we addressed this task when statistics of observational and/or modeling errors are unknown. This common situation requires introduction of apriori constraints on the likelihood (misfit) function which significantly influence the estimated errors. Based on the results of an analysis of 120 seismic events from the Rudna copper mine operating in southwestern Poland we illustrate an approach based on an analysis of Shanon's entropy calculated for the aposteriori distribution. We show that this meta-characteristic of the aposteriori distribution carries some information on uncertainties of the solution found.

Probabilistic dietary exposure assessment taking into account variability in both amount and frequency of consumption.

PubMed

Slob, Wout

2006-07-01

Probabilistic dietary exposure assessments that are fully based on Monte Carlo sampling from the raw intake data may not be appropriate. This paper shows that the data should first be analysed by using a statistical model that is able to take the various dimensions of food consumption patterns into account. A (parametric) model is discussed that takes into account the interindividual variation in (daily) consumption frequencies, as well as in amounts consumed. Further, the model can be used to include covariates, such as age, sex, or other individual attributes. Some illustrative examples show how this model may be used to estimate the probability of exceeding an (acute or chronic) exposure limit. These results are compared with the results based on directly counting the fraction of observed intakes exceeding the limit value. This comparison shows that the latter method is not adequate, in particular for the acute exposure situation. A two-step approach for probabilistic (acute) exposure assessment is proposed: first analyse the consumption data by a (parametric) statistical model as discussed in this paper, and then use Monte Carlo techniques for combining the variation in concentrations with the variation in consumption (by sampling from the statistical model). This approach results in an estimate of the fraction of the population as a function of the fraction of days at which the exposure limit is exceeded by the individual.

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

A probabilistic static stress analysis methodology has been developed to estimate the reliability of a composite structure. Closed form stress analysis methods are the primary analytical tools used in this methodology. These structural mechanics methods are used to identify independent variables whose variations significantly affect the performance of the structure. Once these variables are identified, scatter in their values is evaluated and statistically characterized. The scatter in applied loads and the structural parameters are then fitted to appropriate probabilistic distribution functions. Numerical integration techniques are applied to compute the structural reliability. The predicted reliability accounts for scatter due to variability in material strength, applied load, fabrication and assembly processes. The influence of structural geometry and mode of failure are also considerations in the evaluation. Example problems are given to illustrate various levels of analytical complexity.

Bivariate drought frequency analysis using the copula method

NASA Astrophysics Data System (ADS)

Mirabbasi, Rasoul; Fakheri-Fard, Ahmad; Dinpashoh, Yagob

2012-04-01

Droughts are major natural hazards with significant environmental and economic impacts. In this study, two-dimensional copulas were applied to the analysis of the meteorological drought characteristics of the Sharafkhaneh gauge station, located in the northwest of Iran. Two major drought characteristics, duration and severity, as defined by the standardized precipitation index, were abstracted from observed drought events. Since drought duration and severity exhibited a significant correlation and since they were modeled using different distributions, copulas were used to construct the joint distribution function of the drought characteristics. The parameter of copulas was estimated using the method of the Inference Function for Margins. Several copulas were tested in order to determine the best data fit. According to the error analysis and the tail dependence coefficient, the Galambos copula provided the best fit for the observed drought data. Some bivariate probabilistic properties of droughts, based on the derived copula-based joint distribution, were also investigated. These probabilistic properties can provide useful information for water resource planning and management.

A probabilistic-based approach to monitoring tool wear state and assessing its effect on workpiece quality in nickel-based alloys

NASA Astrophysics Data System (ADS)

Akhavan Niaki, Farbod

The objective of this research is first to investigate the applicability and advantage of statistical state estimation methods for predicting tool wear in machining nickel-based superalloys over deterministic methods, and second to study the effects of cutting tool wear on the quality of the part. Nickel-based superalloys are among those classes of materials that are known as hard-to-machine alloys. These materials exhibit a unique combination of maintaining their strength at high temperature and have high resistance to corrosion and creep. These unique characteristics make them an ideal candidate for harsh environments like combustion chambers of gas turbines. However, the same characteristics that make nickel-based alloys suitable for aggressive conditions introduce difficulties when machining them. High strength and low thermal conductivity accelerate the cutting tool wear and increase the possibility of the in-process tool breakage. A blunt tool nominally deteriorates the surface integrity and damages quality of the machined part by inducing high tensile residual stresses, generating micro-cracks, altering the microstructure or leaving a poor roughness profile behind. As a consequence in this case, the expensive superalloy would have to be scrapped. The current dominant solution for industry is to sacrifice the productivity rate by replacing the tool in the early stages of its life or to choose conservative cutting conditions in order to lower the wear rate and preserve workpiece quality. Thus, monitoring the state of the cutting tool and estimating its effects on part quality is a critical task for increasing productivity and profitability in machining superalloys. This work aims to first introduce a probabilistic-based framework for estimating tool wear in milling and turning of superalloys and second to study the detrimental effects of functional state of the cutting tool in terms of wear and wear rate on part quality. In the milling operation, the mechanisms of tool failure were first identified and, based on the rapid catastrophic failure of the tool, a Bayesian inference method (i.e., Markov Chain Monte Carlo, MCMC) was used for parameter calibration of tool wear using a power mechanistic model. The calibrated model was then used in the state space probabilistic framework of a Kalman filter to estimate the tool flank wear. Furthermore, an on-machine laser measuring system was utilized and fused into the Kalman filter to improve the estimation accuracy. In the turning operation the behavior of progressive wear was investigated as well. Due to the nonlinear nature of wear in turning, an extended Kalman filter was designed for tracking progressive wear, and the results of the probabilistic-based method were compared with a deterministic technique, where significant improvement (more than 60% increase in estimation accuracy) was achieved. To fulfill the second objective of this research in understanding the underlying effects of wear on part quality in cutting nickel-based superalloys, a comprehensive study on surface roughness, dimensional integrity and residual stress was conducted. The estimated results derived from a probabilistic filter were used for finding the proper correlations between wear, surface roughness and dimensional integrity, along with a finite element simulation for predicting the residual stress profile for sharp and worn cutting tool conditions. The output of this research provides the essential information on condition monitoring of the tool and its effects on product quality. The low-cost Hall effect sensor used in this work to capture spindle power in the context of the stochastic filter can effectively estimate tool wear in both milling and turning operations, while the estimated wear can be used to generate knowledge of the state of workpiece surface integrity. Therefore the true functionality and efficiency of the tool in superalloy machining can be evaluated without additional high-cost sensing.

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.

Probabilistic Estimates of Global Mean Sea Level and its Underlying Processes

NASA Astrophysics Data System (ADS)

Hay, C.; Morrow, E.; Kopp, R. E.; Mitrovica, J. X.

2015-12-01

Local sea level can vary significantly from the global mean value due to a suite of processes that includes ongoing sea-level changes due to the last ice age, land water storage, ocean circulation changes, and non-uniform sea-level changes that arise when modern-day land ice rapidly melts. Understanding these sources of spatial and temporal variability is critical to estimating past and present sea-level change and projecting future sea-level rise. Using two probabilistic techniques, a multi-model Kalman smoother and Gaussian process regression, we have reanalyzed 20th century tide gauge observations to produce a new estimate of global mean sea level (GMSL). Our methods allow us to extract global information from the sparse tide gauge field by taking advantage of the physics-based and model-derived geometry of the contributing processes. Both methods provide constraints on the sea-level contribution of glacial isostatic adjustment (GIA). The Kalman smoother tests multiple discrete models of glacial isostatic adjustment (GIA), probabilistically computing the most likely GIA model given the observations, while the Gaussian process regression characterizes the prior covariance structure of a suite of GIA models and then uses this structure to estimate the posterior distribution of local rates of GIA-induced sea-level change. We present the two methodologies, the model-derived geometries of the underlying processes, and our new probabilistic estimates of GMSL and GIA.

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.

Role of ionotropic glutamate receptors in delay and probability discounting in the rat.

PubMed

Yates, Justin R; Batten, Seth R; Bardo, Michael T; Beckmann, Joshua S

2015-04-01

Discounting of delayed and probabilistic reinforcement is linked to increased drug use and pathological gambling. Understanding the neurobiology of discounting is important for designing treatments for these disorders. Glutamate is considered to be involved in addiction-like behaviors; however, the role of ionotropic glutamate receptors (iGluRs) in discounting remains unclear. The current study examined the effects of N-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptor blockade on performance in delay and probability discounting tasks. Following training in either delay or probability discounting, rats (n = 12, each task) received pretreatments of the NMDA receptor antagonists MK-801 (0, 0.01, 0.03, 0.1, or 0.3 mg/kg, s.c.) or ketamine (0, 1.0, 5.0, or 10.0 mg/kg, i.p.), as well as the AMPA receptor antagonist CNQX (0, 1.0, 3.0, or 5.6 mg/kg, i.p.). Hyperbolic discounting functions were used to estimate sensitivity to delayed/probabilistic reinforcement and sensitivity to reinforcer amount. An intermediate dose of MK-801 (0.03 mg/kg) decreased sensitivity to both delayed and probabilistic reinforcement. In contrast, ketamine did not affect the rate of discounting in either task but decreased sensitivity to reinforcer amount. CNQX did not alter sensitivity to reinforcer amount or delayed/probabilistic reinforcement. These results show that blockade of NMDA receptors, but not AMPA receptors, decreases sensitivity to delayed/probabilistic reinforcement (MK-801) and sensitivity to reinforcer amount (ketamine). The differential effects of MK-801 and ketamine demonstrate that sensitivities to delayed/probabilistic reinforcement and reinforcer amount are pharmacologically dissociable.

A quantitative model of optimal data selection in Wason's selection task.

PubMed

Hattori, Masasi

2002-10-01

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

Supernova Cosmology Inference with Probabilistic Photometric Redshifts (SCIPPR)

NASA Astrophysics Data System (ADS)

Peters, Christina; Malz, Alex; Hlozek, Renée

2018-01-01

The Bayesian Estimation Applied to Multiple Species (BEAMS) framework employs probabilistic supernova type classifications to do photometric SN cosmology. This work extends BEAMS to replace high-confidence spectroscopic redshifts with photometric redshift probability density functions, a capability that will be essential in the era the Large Synoptic Survey Telescope and other next-generation photometric surveys where it will not be possible to perform spectroscopic follow up on every SN. We present the Supernova Cosmology Inference with Probabilistic Photometric Redshifts (SCIPPR) Bayesian hierarchical model for constraining the cosmological parameters from photometric lightcurves and host galaxy photometry, which includes selection effects and is extensible to uncertainty in the redshift-dependent supernova type proportions. We create a pair of realistic mock catalogs of joint posteriors over supernova type, redshift, and distance modulus informed by photometric supernova lightcurves and over redshift from simulated host galaxy photometry. We perform inference under our model to obtain a joint posterior probability distribution over the cosmological parameters and compare our results with other methods, namely: a spectroscopic subset, a subset of high probability photometrically classified supernovae, and reducing the photometric redshift probability to a single measurement and error bar.

Flood Risk and Probabilistic Benefit Assessment to Support Management of Flood-Prone Lands: Evidence From Candaba Floodplains, Philippines

NASA Astrophysics Data System (ADS)

Juarez, A. M.; Kibler, K. M.; Sayama, T.; Ohara, M.

2016-12-01

Flood management decision-making is often supported by risk assessment, which may overlook the role of coping capacity and the potential benefits derived from direct use of flood-prone land. Alternatively, risk-benefit analysis can support floodplain management to yield maximum socio-ecological benefits for the minimum flood risk. We evaluate flood risk-probabilistic benefit tradeoffs of livelihood practices compatible with direct human use of flood-prone land (agriculture/wild fisheries) and nature conservation (wild fisheries only) in Candaba, Philippines. Located north-west to Metro Manila, Candaba area is a multi-functional landscape that provides a temporally-variable mix of possible land uses, benefits and ecosystem services of local and regional value. To characterize inundation from 1.3- to 100-year recurrence intervals we couple frequency analysis with rainfall-runoff-inundation modelling and remotely-sensed data. By combining simulated probabilistic floods with both damage and benefit functions (e.g. fish capture and rice yield with flood intensity) we estimate potential damages and benefits over varying probabilistic flood hazards. We find that although direct human uses of flood-prone land are associated with damages, for all the investigated magnitudes of flood events with different frequencies, the probabilistic benefits ( 91 million) exceed risks by a large margin ( 33 million). Even considering risk, probabilistic livelihood benefits of direct human uses far exceed benefits provided by scenarios that exclude direct "risky" human uses (difference of 85 million). In addition, we find that individual coping strategies, such as adapting crop planting periods to the flood pulse or fishing rather than cultivating rice in the wet season, minimize flood losses ( 6 million) while allowing for valuable livelihood benefits ($ 125 million) in flood-prone land. Analysis of societal benefits and local capacities to cope with regular floods demonstrate the relevance of accounting for the full range of flood events and their relation to both potential damages and benefits in risk assessments. Management measures may thus be designed to reflect local contexts and support benefits of natural hydrologic processes, while minimizing flood damage.

A probabilistic method for testing and estimating selection differences between populations

PubMed Central

He, Yungang; Wang, Minxian; Huang, Xin; Li, Ran; Xu, Hongyang; Xu, Shuhua; Jin, Li

2015-01-01

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection. PMID:26463656

Residential water demand with endogenous pricing: The Canadian Case

NASA Astrophysics Data System (ADS)

Reynaud, Arnaud; Renzetti, Steven; Villeneuve, Michel

2005-11-01

In this paper, we show that the rate structure endogeneity may result in a misspecification of the residential water demand function. We propose to solve this endogeneity problem by estimating a probabilistic model describing how water rates are chosen by local communities. This model is estimated on a sample of Canadian local communities. We first show that the pricing structure choice reflects efficiency considerations, equity concerns, and, in some cases, a strategy of price discrimination across consumers by Canadian communities. Hence estimating the residential water demand without taking into account the pricing structures' endogeneity leads to a biased estimation of price and income elasticities. We also demonstrate that the pricing structure per se plays a significant role in influencing price responsiveness of Canadian residential consumers.

Toward Probabilistic Risk Analyses - Development of a Probabilistic Tsunami Hazard Assessment of Crescent City, CA

NASA Astrophysics Data System (ADS)

González, F. I.; Leveque, R. J.; Hatheway, D.; Metzger, N.

2011-12-01

Risk is defined in many ways, but most are consistent with Crichton's [1999] definition based on the ''risk triangle'' concept and the explicit identification of three risk elements: ''Risk is the probability of a loss, and this depends on three elements: hazard, vulnerability, and exposure. If any of these three elements in risk increases or decreases, then the risk increases or decreases respectively." The World Meteorological Organization, for example, cites Crichton [1999] and then defines risk as [WMO, 2008] Risk = function (Hazard x Vulnerability x Exposure) while the Asian Disaster Reduction Center adopts the more general expression [ADRC, 2005] Risk = function (Hazard, Vulnerability, Exposure) In practice, probabilistic concepts are invariably invoked, and at least one of the three factors are specified as probabilistic in nature. The Vulnerability and Exposure factors are defined in multiple ways in the relevant literature; but the Hazard factor, which is the focus of our presentation, is generally understood to deal only with the physical aspects of the phenomena and, in particular, the ability of the phenomena to inflict harm [Thywissen, 2006]. A Hazard factor can be estimated by a methodology known as Probabilistic Tsunami Hazard Assessment (PTHA) [González, et al., 2009]. We will describe the PTHA methodology and provide an example -- the results of a previous application to Seaside, OR. We will also present preliminary results for a PTHA of Crescent City, CA -- a pilot project and coastal modeling/mapping effort funded by the Federal Emergency Management Agency (FEMA) Region IX office as part of the new California Coastal Analysis and Mapping Project (CCAMP). CCAMP and the PTHA in Crescent City are being conducted under the nationwide FEMA Risk Mapping, Assessment, and Planning (Risk MAP) Program which focuses on providing communities with flood information and tools they can use to enhance their mitigation plans and better protect their citizens.

Is probabilistic bias analysis approximately Bayesian?

PubMed Central

MacLehose, Richard F.; Gustafson, Paul

2011-01-01

Case-control studies are particularly susceptible to differential exposure misclassification when exposure status is determined following incident case status. Probabilistic bias analysis methods have been developed as ways to adjust standard effect estimates based on the sensitivity and specificity of exposure misclassification. The iterative sampling method advocated in probabilistic bias analysis bears a distinct resemblance to a Bayesian adjustment; however, it is not identical. Furthermore, without a formal theoretical framework (Bayesian or frequentist), the results of a probabilistic bias analysis remain somewhat difficult to interpret. We describe, both theoretically and empirically, the extent to which probabilistic bias analysis can be viewed as approximately Bayesian. While the differences between probabilistic bias analysis and Bayesian approaches to misclassification can be substantial, these situations often involve unrealistic prior specifications and are relatively easy to detect. Outside of these special cases, probabilistic bias analysis and Bayesian approaches to exposure misclassification in case-control studies appear to perform equally well. PMID:22157311

Multi-Detection Events, Probability Density Functions, and Reduced Location Area

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

Eslinger, Paul W.; Schrom, Brian T.

2016-03-01

Abstract Several efforts have been made in the Comprehensive Nuclear-Test-Ban Treaty (CTBT) community to assess the benefits of combining detections of radionuclides to improve the location estimates available from atmospheric transport modeling (ATM) backtrack calculations. We present a Bayesian estimation approach rather than a simple dilution field of regard approach to allow xenon detections and non-detections to be combined mathematically. This system represents one possible probabilistic approach to radionuclide event formation. Application of this method to a recent interesting radionuclide event shows a substantial reduction in the location uncertainty of that event.

Probabilistic Metrology Attains Macroscopic Cloning of Quantum Clocks

NASA Astrophysics Data System (ADS)

Gendra, B.; Calsamiglia, J.; Muñoz-Tapia, R.; Bagan, E.; Chiribella, G.

2014-12-01

It has recently been shown that probabilistic protocols based on postselection boost the performances of the replication of quantum clocks and phase estimation. Here we demonstrate that the improvements in these two tasks have to match exactly in the macroscopic limit where the number of clones grows to infinity, preserving the equivalence between asymptotic cloning and state estimation for arbitrary values of the success probability. Remarkably, the cloning fidelity depends critically on the number of rationally independent eigenvalues of the clock Hamiltonian. We also prove that probabilistic metrology can simulate cloning in the macroscopic limit for arbitrary sets of states when the performance of the simulation is measured by testing small groups of clones.

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.

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.

Probabilistic Risk Assessment to Inform Decision Making: Frequently Asked Questions

EPA Pesticide Factsheets

General concepts and principles of Probabilistic Risk Assessment (PRA), describe how PRA can improve the bases of Agency decisions, and provide illustrations of how PRA has been used in risk estimation and in describing the uncertainty in decision making.

Meta-heuristic CRPS minimization for the calibration of short-range probabilistic forecasts

NASA Astrophysics Data System (ADS)

Mohammadi, Seyedeh Atefeh; Rahmani, Morteza; Azadi, Majid

2016-08-01

This paper deals with the probabilistic short-range temperature forecasts over synoptic meteorological stations across Iran using non-homogeneous Gaussian regression (NGR). NGR creates a Gaussian forecast probability density function (PDF) from the ensemble output. The mean of the normal predictive PDF is a bias-corrected weighted average of the ensemble members and its variance is a linear function of the raw ensemble variance. The coefficients for the mean and variance are estimated by minimizing the continuous ranked probability score (CRPS) during a training period. CRPS is a scoring rule for distributional forecasts. In the paper of Gneiting et al. (Mon Weather Rev 133:1098-1118, 2005), Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used to minimize the CRPS. Since BFGS is a conventional optimization method with its own limitations, we suggest using the particle swarm optimization (PSO), a robust meta-heuristic method, to minimize the CRPS. The ensemble prediction system used in this study consists of nine different configurations of the weather research and forecasting model for 48-h forecasts of temperature during autumn and winter 2011 and 2012. The probabilistic forecasts were evaluated using several common verification scores including Brier score, attribute diagram and rank histogram. Results show that both BFGS and PSO find the optimal solution and show the same evaluation scores, but PSO can do this with a feasible random first guess and much less computational complexity.

Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2018-03-01

The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.

Corroded Anchor Structure Stability/Reliability (CAS_Stab-R) Software for Hydraulic Structures

DTIC Science & Technology

2017-12-01

This report describes software that provides a probabilistic estimate of time -to-failure for a corroding anchor strand system. These anchor...stability to the structure. A series of unique pull-test experiments conducted by Ebeling et al. (2016) at the U.S. Army Engineer Research and...Reliability (CAS_Stab-R) produces probabilistic Remaining Anchor Life time estimates for anchor cables based upon the direct corrosion rate for the

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

Probabilistic liquefaction hazard analysis at liquefied sites of 1956 Dunaharaszti earthquake, in Hungary

NASA Astrophysics Data System (ADS)

Győri, Erzsébet; Gráczer, Zoltán; Tóth, László; Bán, Zoltán; Horváth, Tibor

2017-04-01

Liquefaction potential evaluations are generally made to assess the hazard from specific scenario earthquakes. These evaluations may estimate the potential in a binary fashion (yes/no), define a factor of safety or predict the probability of liquefaction given a scenario event. Usually the level of ground shaking is obtained from the results of PSHA. Although it is determined probabilistically, a single level of ground shaking is selected and used within the liquefaction potential evaluation. In contrary, the fully probabilistic liquefaction potential assessment methods provide a complete picture of liquefaction hazard, namely taking into account the joint probability distribution of PGA and magnitude of earthquake scenarios; both of which are key inputs in the stress-based simplified methods. Kramer and Mayfield (2007) has developed a fully probabilistic liquefaction potential evaluation method using a performance-based earthquake engineering (PBEE) framework. The results of the procedure are the direct estimate of the return period of liquefaction and the liquefaction hazard curves in function of depth. The method combines the disaggregation matrices computed for different exceedance frequencies during probabilistic seismic hazard analysis with one of the recent models for the conditional probability of liquefaction. We have developed a software for the assessment of performance-based liquefaction triggering on the basis of Kramer and Mayfield method. Originally the SPT based probabilistic method of Cetin et al. (2004) was built-in into the procedure of Kramer and Mayfield to compute the conditional probability however there is no professional consensus about its applicability. Therefore we have included not only Cetin's method but Idriss and Boulanger (2012) SPT based moreover Boulanger and Idriss (2014) CPT based procedures into our computer program. In 1956, a damaging earthquake of magnitude 5.6 occurred in Dunaharaszti, in Hungary. Its epicenter was located about 5 km from the southern boundary of Budapest. The quake caused serious damages in the epicentral area and in the southern districts of the capital. The epicentral area of the earthquake is located along the Danube River. Sand boils were observed in some locations that indicated the occurrence of liquefaction. Because their exact locations were recorded at the time of the earthquake, in situ geotechnical measurements (CPT and SPT) could be performed at two (Dunaharaszti and Taksony) sites. The different types of measurements enabled the probabilistic liquefaction hazard computations at the two studied sites. We have compared the return periods of liquefaction that were computed using different built-in simplified stress based methods.

Opportunities of probabilistic flood loss models

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Lüdtke, Stefan; Vogel, Kristin; Merz, Bruno

2016-04-01

Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. However, reliable flood damage models are a prerequisite for the practical usefulness of the model results. 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. 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 sharpness of the predictions the reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The comparison of the uni-variable Stage damage function and the multivariable model approach emphasises the importance to quantify predictive uncertainty. With each explanatory variable, the multi-variable model reveals an additional source of uncertainty. However, the predictive performance in terms of precision (mbe), accuracy (mae) and reliability (HR) is clearly improved in comparison to uni-variable Stage damage function. Overall, 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.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

PubMed

Sharifi, Soroosh; Murthy, Sudhir; Takács, Imre; Massoudieh, Arash

2014-03-01

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters. Copyright © 2013 Elsevier Ltd. All rights reserved.

PROBABILISTIC ASSESSMENT OF GROUNDWATER VULNERABILITY TO NONPOINT SOURCE POLLUTION IN AGRICULTURAL WATERSHEDS

EPA Science Inventory

This paper presents a probabilistic framework for the assessment of groundwater pollution potential by pesticides in two adjacent agricultural watersheds in the Mid-Altantic Coastal Plain. Indices for estimating streams vulnerability to pollutants' load from the surficial aquifer...

A probabilistic framework for single-station location of seismicity on Earth and Mars

NASA Astrophysics Data System (ADS)

Böse, M.; Clinton, J. F.; Ceylan, S.; Euchner, F.; van Driel, M.; Khan, A.; Giardini, D.; Lognonné, P.; Banerdt, W. B.

2017-01-01

Locating the source of seismic energy from a single three-component seismic station is associated with large uncertainties, originating from challenges in identifying seismic phases, as well as inevitable pick and model uncertainties. The challenge is even higher for planets such as Mars, where interior structure is a priori largely unknown. In this study, we address the single-station location problem by developing a probabilistic framework that combines location estimates from multiple algorithms to estimate the probability density function (PDF) for epicentral distance, back azimuth, and origin time. Each algorithm uses independent and complementary information in the seismic signals. Together, the algorithms allow locating seismicity ranging from local to teleseismic quakes. Distances and origin times of large regional and teleseismic events (M > 5.5) are estimated from observed and theoretical body- and multi-orbit surface-wave travel times. The latter are picked from the maxima in the waveform envelopes in various frequency bands. For smaller events at local and regional distances, only first arrival picks of body waves are used, possibly in combination with fundamental Rayleigh R1 waveform maxima where detectable; depth phases, such as pP or PmP, help constrain source depth and improve distance estimates. Back azimuth is determined from the polarization of the Rayleigh- and/or P-wave phases. When seismic signals are good enough for multiple approaches to be used, estimates from the various methods are combined through the product of their PDFs, resulting in an improved event location and reduced uncertainty range estimate compared to the results obtained from each algorithm independently. To verify our approach, we use both earthquake recordings from existing Earth stations and synthetic Martian seismograms. The Mars synthetics are generated with a full-waveform scheme (AxiSEM) using spherically-symmetric seismic velocity, density and attenuation models of Mars that incorporate existing knowledge of Mars internal structure, and include expected ambient and instrumental noise. While our probabilistic framework is developed mainly for application to Mars in the context of the upcoming InSight mission, it is also relevant for locating seismic events on Earth in regions with sparse instrumentation.

A probabilistic method for testing and estimating selection differences between populations.

PubMed

He, Yungang; Wang, Minxian; Huang, Xin; Li, Ran; Xu, Hongyang; Xu, Shuhua; Jin, Li

2015-12-01

Human populations around the world encounter various environmental challenges and, consequently, develop genetic adaptations to different selection forces. Identifying the differences in natural selection between populations is critical for understanding the roles of specific genetic variants in evolutionary adaptation. Although numerous methods have been developed to detect genetic loci under recent directional selection, a probabilistic solution for testing and quantifying selection differences between populations is lacking. Here we report the development of a probabilistic method for testing and estimating selection differences between populations. By use of a probabilistic model of genetic drift and selection, we showed that logarithm odds ratios of allele frequencies provide estimates of the differences in selection coefficients between populations. The estimates approximate a normal distribution, and variance can be estimated using genome-wide variants. This allows us to quantify differences in selection coefficients and to determine the confidence intervals of the estimate. Our work also revealed the link between genetic association testing and hypothesis testing of selection differences. It therefore supplies a solution for hypothesis testing of selection differences. This method was applied to a genome-wide data analysis of Han and Tibetan populations. The results confirmed that both the EPAS1 and EGLN1 genes are under statistically different selection in Han and Tibetan populations. We further estimated differences in the selection coefficients for genetic variants involved in melanin formation and determined their confidence intervals between continental population groups. Application of the method to empirical data demonstrated the outstanding capability of this novel approach for testing and quantifying differences in natural selection. © 2015 He et al.; Published by Cold Spring Harbor Laboratory Press.

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.

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.

BAYESIAN PROTEIN STRUCTURE ALIGNMENT.

PubMed

Rodriguez, Abel; Schmidler, Scott C

The analysis of the three-dimensional structure of proteins is an important topic in molecular biochemistry. Structure plays a critical role in defining the function of proteins and is more strongly conserved than amino acid sequence over evolutionary timescales. A key challenge is the identification and evaluation of structural similarity between proteins; such analysis can aid in understanding the role of newly discovered proteins and help elucidate evolutionary relationships between organisms. Computational biologists have developed many clever algorithmic techniques for comparing protein structures, however, all are based on heuristic optimization criteria, making statistical interpretation somewhat difficult. Here we present a fully probabilistic framework for pairwise structural alignment of proteins. Our approach has several advantages, including the ability to capture alignment uncertainty and to estimate key "gap" parameters which critically affect the quality of the alignment. We show that several existing alignment methods arise as maximum a posteriori estimates under specific choices of prior distributions and error models. Our probabilistic framework is also easily extended to incorporate additional information, which we demonstrate by including primary sequence information to generate simultaneous sequence-structure alignments that can resolve ambiguities obtained using structure alone. This combined model also provides a natural approach for the difficult task of estimating evolutionary distance based on structural alignments. The model is illustrated by comparison with well-established methods on several challenging protein alignment examples.

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera.

PubMed

Kim, Hyungjin; Lee, Donghwa; Oh, Taekjun; Choi, Hyun-Taek; Myung, Hyun

2015-08-31

Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

PubMed Central

Kim, Hyungjin; Lee, Donghwa; Oh, Taekjun; Choi, Hyun-Taek; Myung, Hyun

2015-01-01

Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments. PMID:26404284

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.

Methodological framework for the probabilistic risk assessment of multi-hazards at a municipal scale: a case study in the Fella river valley, Eastern Italian Alps

NASA Astrophysics Data System (ADS)

Hussin, Haydar; van Westen, Cees; Reichenbach, Paola

2013-04-01

Local and regional authorities in mountainous areas that deal with hydro-meteorological hazards like landslides and floods try to set aside budgets for emergencies and risk mitigation. However, future losses are often not calculated in a probabilistic manner when allocating budgets or determining how much risk is acceptable. The absence of probabilistic risk estimates can create a lack of preparedness for reconstruction and risk reduction costs and a deficiency in promoting risk mitigation and prevention in an effective way. The probabilistic risk of natural hazards at local scale is usually ignored all together due to the difficulty in acknowledging, processing and incorporating uncertainties in the estimation of losses (e.g. physical damage, fatalities and monetary loss). This study attempts to set up a working framework for a probabilistic risk assessment (PRA) of landslides and floods at a municipal scale using the Fella river valley (Eastern Italian Alps) as a multi-hazard case study area. The emphasis is on the evaluation and determination of the uncertainty in the estimation of losses from multi-hazards. To carry out this framework some steps are needed: (1) by using physically based stochastic landslide and flood models we aim to calculate the probability of the physical impact on individual elements at risk, (2) this is then combined with a statistical analysis of the vulnerability and monetary value of the elements at risk in order to include their uncertainty in the risk assessment, (3) finally the uncertainty from each risk component is propagated into the loss estimation. The combined effect of landslides and floods on the direct risk to communities in narrow alpine valleys is also one of important aspects that needs to be studied.

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.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Automated liver segmentation using a normalized probabilistic atlas

NASA Astrophysics Data System (ADS)

Linguraru, Marius George; Li, Zhixi; Shah, Furhawn; Chin, See; Summers, Ronald M.

2009-02-01

Probabilistic atlases of anatomical organs, especially the brain and the heart, have become popular in medical image analysis. We propose the construction of probabilistic atlases which retain structural variability by using a size-preserving modified affine registration. The organ positions are modeled in the physical space by normalizing the physical organ locations to an anatomical landmark. In this paper, a liver probabilistic atlas is constructed and exploited to automatically segment liver volumes from abdominal CT data. The atlas is aligned with the patient data through a succession of affine and non-linear registrations. The overlap and correlation with manual segmentations are 0.91 (0.93 DICE coefficient) and 0.99 respectively. Little work has taken place on the integration of volumetric measures of liver abnormality to clinical evaluations, which rely on linear estimates of liver height. Our application measures the liver height at the mid-hepatic line (0.94 correlation with manual measurements) and indicates that its combination with volumetric estimates could assist the development of a noninvasive tool to assess hepatomegaly.

Risk analysis of analytical validations by probabilistic modification of FMEA.

PubMed

Barends, D M; Oldenhof, M T; Vredenbregt, M J; Nauta, M J

2012-05-01

Risk analysis is a valuable addition to validation of an analytical chemistry process, enabling not only detecting technical risks, but also risks related to human failures. Failure Mode and Effect Analysis (FMEA) can be applied, using a categorical risk scoring of the occurrence, detection and severity of failure modes, and calculating the Risk Priority Number (RPN) to select failure modes for correction. We propose a probabilistic modification of FMEA, replacing the categorical scoring of occurrence and detection by their estimated relative frequency and maintaining the categorical scoring of severity. In an example, the results of traditional FMEA of a Near Infrared (NIR) analytical procedure used for the screening of suspected counterfeited tablets are re-interpretated by this probabilistic modification of FMEA. Using this probabilistic modification of FMEA, the frequency of occurrence of undetected failure mode(s) can be estimated quantitatively, for each individual failure mode, for a set of failure modes, and the full analytical procedure. Copyright © 2012 Elsevier B.V. All rights reserved.

INFERRING THE ECCENTRICITY DISTRIBUTION

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

Hogg, David W.; Bovy, Jo; Myers, Adam D., E-mail: david.hogg@nyu.ed

2010-12-20

Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementationmore » of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision-other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts-so long as the measurements have been communicated as a likelihood function or a posterior sampling.« less

Probabilistic Tractography of the Cranial Nerves in Vestibular Schwannoma.

PubMed

Zolal, Amir; Juratli, Tareq A; Podlesek, Dino; Rieger, Bernhard; Kitzler, Hagen H; Linn, Jennifer; Schackert, Gabriele; Sobottka, Stephan B

2017-11-01

Multiple recent studies have reported on diffusion tensor-based fiber tracking of cranial nerves in vestibular schwannoma, with conflicting results as to the accuracy of the method and the occurrence of cochlear nerve depiction. Probabilistic nontensor-based tractography might offer advantages in terms of better extraction of directional information from the underlying data in cranial nerves, which are of subvoxel size. Twenty-one patients with large vestibular schwannomas were recruited. The probabilistic tracking was run preoperatively and the position of the potential depictions of the facial and cochlear nerves was estimated postoperatively by 3 independent observers in a blinded fashion. The true position of the nerve was determined intraoperatively by the surgeon. Thereafter, the imaging-based estimated position was compared with the intraoperatively determined position. Tumor size, cystic appearance, and postoperative House-Brackmann score were analyzed with regard to the accuracy of the depiction of the nerves. The probabilistic tracking showed a connection that correlated to the position of the facial nerve in 81% of the cases and to the position of the cochlear nerve in 33% of the cases. Altogether, the resulting depiction did not correspond to the intraoperative position of any of the nerves in 3 cases. In a majority of cases, the position of the facial nerve, but not of the cochlear nerve, could be estimated by evaluation of the probabilistic tracking results. However, false depictions not corresponding to any nerve do occur and cannot be discerned as such from the image only. Copyright © 2017 Elsevier Inc. All rights reserved.

A rational model of function learning.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Williams, Joseph J; Kalish, Michael L

2015-10-01

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

Porphyry copper assessment of the Tethys region of western and southern Asia: Chapter V in Global mineral resource assessment

USGS Publications Warehouse

Zürcher, Lukas; Bookstrom, Arthur A.; Hammarstrom, Jane M.; Mars, John C.; Ludington, Stephen; Zientek, Michael L.; Dunlap, Pamela; Wallis, John C.; Drew, Lawrence J.; Sutphin, David M.; Berger, Byron R.; Herrington, Richard J.; Billa, Mario; Kuşcu, Ilkay; Moon, Charles J.; Richards, Jeremy P.; Zientek, Michael L.; Hammarstrom, Jane M.; Johnson, Kathleen M.

2015-11-18

The assessment estimates that the Tethys region contains 47 undiscovered deposits within 1 kilometer of the surface. Probabilistic estimates of numbers of undiscovered deposits were combined with grade and tonnage models in a Monte Carlo simulation to estimate probable amounts of contained metal. The 47 undiscovered deposits are estimated to contain a mean of 180 million metric tons (Mt) of copper distributed among the 18 tracts for which probabilistic estimates were made, in addition to the 62 Mt of copper already identified in the 42 known porphyry deposits in the study area. Results of Monte Carlo simulations show that 80 percent of the estimated undiscovered porphyry copper resources in the Tethys region are located in four tracts or sub-tracts.

Estimating parameters for probabilistic linkage of privacy-preserved datasets.

PubMed

Brown, Adrian P; Randall, Sean M; Ferrante, Anna M; Semmens, James B; Boyd, James H

2017-07-10

Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher than the F-measure using calculated probabilities. Further, the threshold estimation yielded results for F-measure that were only slightly below the highest possible for those probabilities. The method appears highly accurate across a spectrum of datasets with varying degrees of error. As there are few alternatives for parameter estimation, the approach is a major step towards providing a complete operational approach for probabilistic linkage of privacy-preserved datasets.

Probabilistic simulation of stress concentration in composite laminates

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Murthy, P. L. N.; Liaw, L.

1993-01-01

A computational methodology is described to probabilistically simulate the stress concentration factors in composite laminates. This new approach consists of coupling probabilistic composite mechanics with probabilistic finite element structural analysis. The probabilistic composite mechanics is used to probabilistically describe all the uncertainties inherent in composite material properties while probabilistic finite element is used to probabilistically describe the uncertainties associated with methods to experimentally evaluate stress concentration factors such as loads, geometry, and supports. The effectiveness of the methodology is demonstrated by using it to simulate the stress concentration factors in composite laminates made from three different composite systems. Simulated results match experimental data for probability density and for cumulative distribution functions. The sensitivity factors indicate that the stress concentration factors are influenced by local stiffness variables, by load eccentricities and by initial stress fields.

Use of adjoint methods in the probabilistic finite element approach to fracture mechanics

NASA Technical Reports Server (NTRS)

Liu, Wing Kam; Besterfield, Glen; Lawrence, Mark; Belytschko, Ted

1988-01-01

The adjoint method approach to probabilistic finite element methods (PFEM) is presented. When the number of objective functions is small compared to the number of random variables, the adjoint method is far superior to the direct method in evaluating the objective function derivatives with respect to the random variables. The PFEM is extended to probabilistic fracture mechanics (PFM) using an element which has the near crack-tip singular strain field embedded. Since only two objective functions (i.e., mode I and II stress intensity factors) are needed for PFM, the adjoint method is well suited.

Probabilistic framework for the estimation of the adult and child toxicokinetic intraspecies uncertainty factors.

PubMed

Pelekis, Michael; Nicolich, Mark J; Gauthier, Joseph S

2003-12-01

Human health risk assessments use point values to develop risk estimates and thus impart a deterministic character to risk, which, by definition, is a probability phenomenon. The risk estimates are calculated based on individuals and then, using uncertainty factors (UFs), are extrapolated to the population that is characterized by variability. Regulatory agencies have recommended the quantification of the impact of variability in risk assessments through the application of probabilistic methods. In the present study, a framework that deals with the quantitative analysis of uncertainty (U) and variability (V) in target tissue dose in the population was developed by applying probabilistic analysis to physiologically-based toxicokinetic models. The mechanistic parameters that determine kinetics were described with probability density functions (PDFs). Since each PDF depicts the frequency of occurrence of all expected values of each parameter in the population, the combined effects of multiple sources of U/V were accounted for in the estimated distribution of tissue dose in the population, and a unified (adult and child) intraspecies toxicokinetic uncertainty factor UFH-TK was determined. The results show that the proposed framework accounts effectively for U/V in population toxicokinetics. The ratio of the 95th percentile to the 50th percentile of the annual average concentration of the chemical at the target tissue organ (i.e., the UFH-TK) varies with age. The ratio is equivalent to a unified intraspecies toxicokinetic UF, and it is one of the UFs by which the NOAEL can be divided to obtain the RfC/RfD. The 10-fold intraspecies UF is intended to account for uncertainty and variability in toxicokinetics (3.2x) and toxicodynamics (3.2x). This article deals exclusively with toxicokinetic component of UF. The framework provides an alternative to the default methodology and is advantageous in that the evaluation of toxicokinetic variability is based on the distribution of the effective target tissue dose, rather than applied dose. It allows for the replacement of the default adult and children intraspecies UF with toxicokinetic data-derived values and provides accurate chemical-specific estimates for their magnitude. It shows that proper application of probability and toxicokinetic theories can reduce uncertainties when establishing exposure limits for specific compounds and provide better assurance that established limits are adequately protective. It contributes to the development of a probabilistic noncancer risk assessment framework and will ultimately lead to the unification of cancer and noncancer risk assessment methodologies.

Probabilistic Analysis and Density Parameter Estimation Within Nessus

NASA Astrophysics Data System (ADS)

Godines, Cody R.; Manteufel, Randall D.

2002-12-01

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can be obtained by using NESSUS' LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS. These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.

Probabilistic Analysis and Density Parameter Estimation Within Nessus

NASA Technical Reports Server (NTRS)

Godines, Cody R.; Manteufel, Randall D.; Chamis, Christos C. (Technical Monitor)

2002-01-01

This NASA educational grant has the goal of promoting probabilistic analysis methods to undergraduate and graduate UTSA engineering students. Two undergraduate-level and one graduate-level course were offered at UTSA providing a large number of students exposure to and experience in probabilistic techniques. The grant provided two research engineers from Southwest Research Institute the opportunity to teach these courses at UTSA, thereby exposing a large number of students to practical applications of probabilistic methods and state-of-the-art computational methods. In classroom activities, students were introduced to the NESSUS computer program, which embodies many algorithms in probabilistic simulation and reliability analysis. Because the NESSUS program is used at UTSA in both student research projects and selected courses, a student version of a NESSUS manual has been revised and improved, with additional example problems being added to expand the scope of the example application problems. This report documents two research accomplishments in the integration of a new sampling algorithm into NESSUS and in the testing of the new algorithm. The new Latin Hypercube Sampling (LHS) subroutines use the latest NESSUS input file format and specific files for writing output. The LHS subroutines are called out early in the program so that no unnecessary calculations are performed. Proper correlation between sets of multidimensional coordinates can be obtained by using NESSUS' LHS capabilities. Finally, two types of correlation are written to the appropriate output file. The program enhancement was tested by repeatedly estimating the mean, standard deviation, and 99th percentile of four different responses using Monte Carlo (MC) and LHS. These test cases, put forth by the Society of Automotive Engineers, are used to compare probabilistic methods. For all test cases, it is shown that LHS has a lower estimation error than MC when used to estimate the mean, standard deviation, and 99th percentile of the four responses at the 50 percent confidence level and using the same number of response evaluations for each method. In addition, LHS requires fewer calculations than MC in order to be 99.7 percent confident that a single mean, standard deviation, or 99th percentile estimate will be within at most 3 percent of the true value of the each parameter. Again, this is shown for all of the test cases studied. For that reason it can be said that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ; furthermore, the newest LHS module is a valuable new enhancement of the program.

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

PubMed Central

Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-01-01

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets’ state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems. PMID:29113085

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association.

PubMed

Liu, Yu; Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-11-05

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets' state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems.

Probabilistic divergence time estimation without branch lengths: dating the origins of dinosaurs, avian flight and crown birds.

PubMed

Lloyd, G T; Bapst, D W; Friedman, M; Davis, K E

2016-11-01

Branch lengths-measured in character changes-are an essential requirement of clock-based divergence estimation, regardless of whether the fossil calibrations used represent nodes or tips. However, a separate set of divergence time approaches are typically used to date palaeontological trees, which may lack such branch lengths. Among these methods, sophisticated probabilistic approaches have recently emerged, in contrast with simpler algorithms relying on minimum node ages. Here, using a novel phylogenetic hypothesis for Mesozoic dinosaurs, we apply two such approaches to estimate divergence times for: (i) Dinosauria, (ii) Avialae (the earliest birds) and (iii) Neornithes (crown birds). We find: (i) the plausibility of a Permian origin for dinosaurs to be dependent on whether Nyasasaurus is the oldest dinosaur, (ii) a Middle to Late Jurassic origin of avian flight regardless of whether Archaeopteryx or Aurornis is considered the first bird and (iii) a Late Cretaceous origin for Neornithes that is broadly congruent with other node- and tip-dating estimates. Demonstrating the feasibility of probabilistic time-scaling further opens up divergence estimation to the rich histories of extinct biodiversity in the fossil record, even in the absence of detailed character data. © 2016 The Authors.

Probabilistic properties of the date of maximum river flow, an approach based on circular statistics in lowland, highland and mountainous catchment

NASA Astrophysics Data System (ADS)

Rutkowska, Agnieszka; Kohnová, Silvia; Banasik, Kazimierz

2018-04-01

Probabilistic properties of dates of winter, summer and annual maximum flows were studied using circular statistics in three catchments differing in topographic conditions; a lowland, highland and mountainous catchment. The circular measures of location and dispersion were used in the long-term samples of dates of maxima. The mixture of von Mises distributions was assumed as the theoretical distribution function of the date of winter, summer and annual maximum flow. The number of components was selected on the basis of the corrected Akaike Information Criterion and the parameters were estimated by means of the Maximum Likelihood method. The goodness of fit was assessed using both the correlation between quantiles and a version of the Kuiper's and Watson's test. Results show that the number of components varied between catchments and it was different for seasonal and annual maxima. Differences between catchments in circular characteristics were explained using climatic factors such as precipitation and temperature. Further studies may include circular grouping catchments based on similarity between distribution functions and the linkage between dates of maximum precipitation and maximum flow.

Probabilistic Analysis of Space Shuttle Body Flap Actuator Ball Bearings

NASA Technical Reports Server (NTRS)

Oswald, Fred B.; Jett, Timothy R.; Predmore, Roamer E.; Zaretsky, Erin V.

2007-01-01

A probabilistic analysis, using the 2-parameter Weibull-Johnson method, was performed on experimental life test data from space shuttle actuator bearings. Experiments were performed on a test rig under simulated conditions to determine the life and failure mechanism of the grease lubricated bearings that support the input shaft of the space shuttle body flap actuators. The failure mechanism was wear that can cause loss of bearing preload. These tests established life and reliability data for both shuttle flight and ground operation. Test data were used to estimate the failure rate and reliability as a function of the number of shuttle missions flown. The Weibull analysis of the test data for a 2-bearing shaft assembly in each body flap actuator established a reliability level of 99.6 percent for a life of 12 missions. A probabilistic system analysis for four shuttles, each of which has four actuators, predicts a single bearing failure in one actuator of one shuttle after 22 missions (a total of 88 missions for a 4-shuttle fleet). This prediction is comparable with actual shuttle flight history in which a single actuator bearing was found to have failed by wear at 20 missions.

Probabilistic Analysis of Space Shuttle Body Flap Actuator Ball Bearings

NASA Technical Reports Server (NTRS)

Oswald, Fred B.; Jett, Timothy R.; Predmore, Roamer E.; Zaretsky, Erwin V.

2008-01-01

A probabilistic analysis, using the 2-parameter Weibull-Johnson method, was performed on experimental life test data from space shuttle actuator bearings. Experiments were performed on a test rig under simulated conditions to determine the life and failure mechanism of the grease lubricated bearings that support the input shaft of the space shuttle body flap actuators. The failure mechanism was wear that can cause loss of bearing preload. These tests established life and reliability data for both shuttle flight and ground operation. Test data were used to estimate the failure rate and reliability as a function of the number of shuttle missions flown. The Weibull analysis of the test data for the four actuators on one shuttle, each with a 2-bearing shaft assembly, established a reliability level of 96.9 percent for a life of 12 missions. A probabilistic system analysis for four shuttles, each of which has four actuators, predicts a single bearing failure in one actuator of one shuttle after 22 missions (a total of 88 missions for a 4-shuttle fleet). This prediction is comparable with actual shuttle flight history in which a single actuator bearing was found to have failed by wear at 20 missions.

Probabilistic Fatigue Damage Program (FATIG)

NASA Technical Reports Server (NTRS)

Michalopoulos, Constantine

2012-01-01

FATIG computes fatigue damage/fatigue life using the stress rms (root mean square) value, the total number of cycles, and S-N curve parameters. The damage is computed by the following methods: (a) traditional method using Miner s rule with stress cycles determined from a Rayleigh distribution up to 3*sigma; and (b) classical fatigue damage formula involving the Gamma function, which is derived from the integral version of Miner's rule. The integration is carried out over all stress amplitudes. This software solves the problem of probabilistic fatigue damage using the integral form of the Palmgren-Miner rule. The software computes fatigue life using an approach involving all stress amplitudes, up to N*sigma, as specified by the user. It can be used in the design of structural components subjected to random dynamic loading, or by any stress analyst with minimal training for fatigue life estimates of structural components.

A Probabilistic Model for Estimating the Depth and Threshold Temperature of C-fiber Nociceptors

PubMed Central

Dezhdar, Tara; Moshourab, Rabih A.; Fründ, Ingo; Lewin, Gary R.; Schmuker, Michael

2015-01-01

The subjective experience of thermal pain follows the detection and encoding of noxious stimuli by primary afferent neurons called nociceptors. However, nociceptor morphology has been hard to access and the mechanisms of signal transduction remain unresolved. In order to understand how heat transducers in nociceptors are activated in vivo, it is important to estimate the temperatures that directly activate the skin-embedded nociceptor membrane. Hence, the nociceptor’s temperature threshold must be estimated, which in turn will depend on the depth at which transduction happens in the skin. Since the temperature at the receptor cannot be accessed experimentally, such an estimation can currently only be achieved through modeling. However, the current state-of-the-art model to estimate temperature at the receptor suffers from the fact that it cannot account for the natural stochastic variability of neuronal responses. We improve this model using a probabilistic approach which accounts for uncertainties and potential noise in system. Using a data set of 24 C-fibers recorded in vitro, we show that, even without detailed knowledge of the bio-thermal properties of the system, the probabilistic model that we propose here is capable of providing estimates of threshold and depth in cases where the classical method fails. PMID:26638830

Probabilistic estimation of residential air exchange rates for ...

EPA Pesticide Factsheets

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 measurements. An algorithm for probabilistically estimating AER was developed based on the Lawrence Berkley National Laboratory Infiltration model utilizing housing characteristics and meteorological data with adjustment for window opening behavior. The algorithm was evaluated by comparing modeled and measured AERs in four US cities (Los Angeles, CA; Detroit, MI; Elizabeth, NJ; and Houston, TX) inputting study-specific data. The impact on the modeled AER of using publically available housing data representative of the region for each city was also assessed. Finally, modeled AER based on region-specific inputs was compared with those estimated using literature-based distributions. While modeled AERs were similar in magnitude to the measured AER they were consistently lower for all cities except Houston. AERs estimated using region-specific inputs were lower than those using study-specific inputs due to differences in window opening probabilities. The algorithm produced more spatially and temporally variable AERs compared with literature-based distributions reflecting within- and between-city differences, helping reduce error in estimates of air pollutant exposure. Published in the Journal of

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...

Probabilistic Evaluation of Blade Impact Damage

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Abumeri, G. H.

2003-01-01

The response to high velocity impact of a composite blade is probabilistically evaluated. The evaluation is focused on quantifying probabilistically the effects of uncertainties (scatter) in the variables that describe the impact, the blade make-up (geometry and material), the blade response (displacements, strains, stresses, frequencies), the blade residual strength after impact, and the blade damage tolerance. The results of probabilistic evaluations results are in terms of probability cumulative distribution functions and probabilistic sensitivities. Results show that the blade has relatively low damage tolerance at 0.999 probability of structural failure and substantial at 0.01 probability.

Probabilistic Aeroelastic Analysis of Turbomachinery Components

NASA Technical Reports Server (NTRS)

Reddy, T. S. R.; Mital, S. K.; Stefko, G. L.

2004-01-01

A probabilistic approach is described for aeroelastic analysis of turbomachinery blade rows. Blade rows with subsonic flow and blade rows with supersonic flow with subsonic leading edge are considered. To demonstrate the probabilistic approach, the flutter frequency, damping and forced response of a blade row representing a compressor geometry is considered. The analysis accounts for uncertainties in structural and aerodynamic design variables. The results are presented in the form of probabilistic density function (PDF) and sensitivity factors. For subsonic flow cascade, comparisons are also made with different probabilistic distributions, probabilistic methods, and Monte-Carlo simulation. The approach shows that the probabilistic approach provides a more realistic and systematic way to assess the effect of uncertainties in design variables on the aeroelastic instabilities and response.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic 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. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

PubMed Central

Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W. F.; Jeelani, Owase; Dunaway, David J.; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face. PMID:29742139

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling.

PubMed

Knoops, Paul G M; Borghi, Alessandro; Ruggiero, Federica; Badiali, Giovanni; Bianchi, Alberto; Marchetti, Claudio; Rodriguez-Florez, Naiara; Breakey, Richard W F; Jeelani, Owase; Dunaway, David J; Schievano, Silvia

2018-01-01

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.

Fast, Nonlinear, Fully Probabilistic Inversion of Large Geophysical Problems

NASA Astrophysics Data System (ADS)

Curtis, A.; Shahraeeni, M.; Trampert, J.; Meier, U.; Cho, G.

2010-12-01

Almost all Geophysical inverse problems are in reality nonlinear. Fully nonlinear inversion including non-approximated physics, and solving for probability distribution functions (pdf’s) that describe the solution uncertainty, generally requires sampling-based Monte-Carlo style methods that are computationally intractable in most large problems. In order to solve such problems, physical relationships are usually linearized leading to efficiently-solved, (possibly iterated) linear inverse problems. However, it is well known that linearization can lead to erroneous solutions, and in particular to overly optimistic uncertainty estimates. What is needed across many Geophysical disciplines is a method to invert large inverse problems (or potentially tens of thousands of small inverse problems) fully probabilistically and without linearization. This talk shows how very large nonlinear inverse problems can be solved fully probabilistically and incorporating any available prior information using mixture density networks (driven by neural network banks), provided the problem can be decomposed into many small inverse problems. In this talk I will explain the methodology, compare multi-dimensional pdf inversion results to full Monte Carlo solutions, and illustrate the method with two applications: first, inverting surface wave group and phase velocities for a fully-probabilistic global tomography model of the Earth’s crust and mantle, and second inverting industrial 3D seismic data for petrophysical properties throughout and around a subsurface hydrocarbon reservoir. The latter problem is typically decomposed into 104 to 105 individual inverse problems, each solved fully probabilistically and without linearization. The results in both cases are sufficiently close to the Monte Carlo solution to exhibit realistic uncertainty, multimodality and bias. This provides far greater confidence in the results, and in decisions made on their basis.

Assessment of a Tsunami Hazard for Mediterranean Coast of Egypt

NASA Astrophysics Data System (ADS)

Zaytsev, Andrey; Babeyko, Andrey; Yalciner, Ahmet; Pelinovsky, Efim

2017-04-01

Analysis of tsunami hazard for Egypt based on historic data and numerical modelling of historic and prognostic events is given. There are 13 historic events for 4000 years, including one instrumental record (1956). Tsunami database includes 12 earthquake tsunamis and 1 event of volcanic origin (Santorini eruption). Tsunami intensity of events (365, 881, 1303, 1870) is estimated as I = 3 led to tsunami wave height more than 6 m. Numerical simulation of some possible scenario of tsunamis of seismic and landslide origin is done with use of NAMI-DANCE software solved the shallow-water equations. The PTHA method (Probabilistic Tsunami Hazard Assessment - Probabilistic assessment of a tsunami hazard) for the Mediterranean Sea developed in (Sorensen M.B., Spada M., Babeyko A., Wiemer S., Grunthal G. Probabilistic tsunami hazard in the Mediterranean Sea. J Geophysical Research, 2012, vol. 117, B01305) is used to evaluate the probability of tsunami occurrence on the Egyptian coast. The synthetic catalogue of prognostic tsunamis of seismic origin with magnitude more than 6.5 includes 84 920 events for 100000 years. For the wave heights more 1 m the curve: exceedance probability - tsunami height can be approximated by exponential Gumbel function with two parameters which are determined for each coastal location in Egypt (totally. 24 points). Prognostic extreme highest events with probability less 10-4 are not satisfied to the Gumbel function (approximately 10 events) and required the special analysis. Acknowledgements: This work was supported EU FP7 ASTARTE Project [603839], and for EP - NS6637.2016.5.

The integration of probabilistic information during sensorimotor estimation is unimpaired in children with Cerebral Palsy

PubMed Central

Sokhey, Taegh; Gaebler-Spira, Deborah; Kording, Konrad P.

2017-01-01

Background It is important to understand the motor deficits of children with Cerebral Palsy (CP). Our understanding of this motor disorder can be enriched by computational models of motor control. One crucial stage in generating movement involves combining uncertain information from different sources, and deficits in this process could contribute to reduced motor function in children with CP. Healthy adults can integrate previously-learned information (prior) with incoming sensory information (likelihood) in a close-to-optimal way when estimating object location, consistent with the use of Bayesian statistics. However, there are few studies investigating how children with CP perform sensorimotor integration. We compare sensorimotor estimation in children with CP and age-matched controls using a model-based analysis to understand the process. Methods and findings We examined Bayesian sensorimotor integration in children with CP, aged between 5 and 12 years old, with Gross Motor Function Classification System (GMFCS) levels 1–3 and compared their estimation behavior with age-matched typically-developing (TD) children. We used a simple sensorimotor estimation task which requires participants to combine probabilistic information from different sources: a likelihood distribution (current sensory information) with a prior distribution (learned target information). In order to examine sensorimotor integration, we quantified how participants weighed statistical information from the two sources (prior and likelihood) and compared this to the statistical optimal weighting. We found that the weighing of statistical information in children with CP was as statistically efficient as that of TD children. Conclusions We conclude that Bayesian sensorimotor integration is not impaired in children with CP and therefore, does not contribute to their motor deficits. Future research has the potential to enrich our understanding of motor disorders by investigating the stages of motor processing set out by computational models. Therapeutic interventions should exploit the ability of children with CP to use statistical information. PMID:29186196

Real-time realizations of the Bayesian Infrasonic Source Localization Method

NASA Astrophysics Data System (ADS)

Pinsky, V.; Arrowsmith, S.; Hofstetter, A.; Nippress, A.

2015-12-01

The Bayesian Infrasonic Source Localization method (BISL), introduced by Mordak et al. (2010) and upgraded by Marcillo et al. (2014) is destined for the accurate estimation of the atmospheric event origin at local, regional and global scales by the seismic and infrasonic networks and arrays. The BISL is based on probabilistic models of the source-station infrasonic signal propagation time, picking time and azimuth estimate merged with a prior knowledge about celerity distribution. It requires at each hypothetical source location, integration of the product of the corresponding source-station likelihood functions multiplied by a prior probability density function of celerity over the multivariate parameter space. The present BISL realization is generally time-consuming procedure based on numerical integration. The computational scheme proposed simplifies the target function so that integrals are taken exactly and are represented via standard functions. This makes the procedure much faster and realizable in real-time without practical loss of accuracy. The procedure executed as PYTHON-FORTRAN code demonstrates high performance on a set of the model and real data.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

The case for probabilistic forecasting in hydrology

NASA Astrophysics Data System (ADS)

Krzysztofowicz, Roman

2001-08-01

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the 'best' estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

A Case Study for Probabilistic Methods Validation (MSFC Center Director's Discretionary Fund, Project No. 94-26)

NASA Technical Reports Server (NTRS)

Price J. M.; Ortega, R.

1998-01-01

Probabilistic method is not a universally accepted approach for the design and analysis of aerospace structures. The validity of this approach must be demonstrated to encourage its acceptance as it viable design and analysis tool to estimate structural reliability. The objective of this Study is to develop a well characterized finite population of similar aerospace structures that can be used to (1) validate probabilistic codes, (2) demonstrate the basic principles behind probabilistic methods, (3) formulate general guidelines for characterization of material drivers (such as elastic modulus) when limited data is available, and (4) investigate how the drivers affect the results of sensitivity analysis at the component/failure mode level.

Estimating earthquake-induced failure probability and downtime of critical facilities.

PubMed

Porter, Keith; Ramer, Kyle

2012-01-01

Fault trees have long been used to estimate failure risk in earthquakes, especially for nuclear power plants (NPPs). One interesting application is that one can assess and manage the probability that two facilities - a primary and backup - would be simultaneously rendered inoperative in a single earthquake. Another is that one can calculate the probabilistic time required to restore a facility to functionality, and the probability that, during any given planning period, the facility would be rendered inoperative for any specified duration. A large new peer-reviewed library of component damageability and repair-time data for the first time enables fault trees to be used to calculate the seismic risk of operational failure and downtime for a wide variety of buildings other than NPPs. With the new library, seismic risk of both the failure probability and probabilistic downtime can be assessed and managed, considering the facility's unique combination of structural and non-structural components, their seismic installation conditions, and the other systems on which the facility relies. An example is offered of real computer data centres operated by a California utility. The fault trees were created and tested in collaboration with utility operators, and the failure probability and downtime results validated in several ways.

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.

Hierarchical Probabilistic Inference of Cosmic Shear

NASA Astrophysics Data System (ADS)

Schneider, Michael D.; Hogg, David W.; Marshall, Philip J.; Dawson, William A.; Meyers, Joshua; Bard, Deborah J.; Lang, Dustin

2015-07-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.

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 approaches to accounting for data variability in the practical application of bioavailability in predicting aquatic risks from metals.

PubMed

Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura

2013-07-01

The biotic ligand model (BLM) theoretically enables the derivation of environmental quality standards that are based on true bioavailable fractions of metals. Several physicochemical variables (especially pH, major cations, dissolved organic carbon, and dissolved metal concentrations) must, however, be assigned to run the BLM, but they are highly variable in time and space in natural systems. This article describes probabilistic approaches for integrating such variability during the derivation of risk indexes. To describe each variable using a probability density function (PDF), several methods were combined to 1) treat censored data (i.e., data below the limit of detection), 2) incorporate the uncertainty of the solid-to-liquid partitioning of metals, and 3) detect outliers. From a probabilistic perspective, 2 alternative approaches that are based on log-normal and Γ distributions were tested to estimate the probability of the predicted environmental concentration (PEC) exceeding the predicted non-effect concentration (PNEC), i.e., p(PEC/PNEC>1). The probabilistic approach was tested on 4 real-case studies based on Cu-related data collected from stations on the Loire and Moselle rivers. The approach described in this article is based on BLM tools that are freely available for end-users (i.e., the Bio-Met software) and on accessible statistical data treatments. This approach could be used by stakeholders who are involved in risk assessments of metals for improving site-specific studies. Copyright © 2013 SETAC.

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.

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

A probabilistic assessment of health risks associated with short-term exposure to tropospheric ozone

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

Whitfield, R.G; Biller, W.F.; Jusko, M.J.

1996-06-01

The work described in this report is part of a larger risk assessment sponsored by the U.S. Environmental Protection Agency. Earlier efforts developed exposure-response relationships for acute health effects among populations engaged in heavy exertion. Those efforts also developed a probabilistic national ambient air quality standards exposure model and a general methodology for integrating probabilistic exposure-response relation- ships and exposure estimates to calculate overall risk results. Recently published data make it possible to model additional health endpoints (for exposure at moderate exertion), including hospital admissions. New air quality and exposure estimates for alternative national ambient air quality standards for ozonemore » are combined with exposure-response models to produce the risk results for hospital admissions and acute health effects. Sample results explain the methodology and introduce risk output formats.« less

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

Probabilistic/Fracture-Mechanics Model For Service Life

NASA Technical Reports Server (NTRS)

Watkins, T., Jr.; Annis, C. G., Jr.

1991-01-01

Computer program makes probabilistic estimates of lifetime of engine and components thereof. Developed to fill need for more accurate life-assessment technique that avoids errors in estimated lives and provides for statistical assessment of levels of risk created by engineering decisions in designing system. Implements mathematical model combining techniques of statistics, fatigue, fracture mechanics, nondestructive analysis, life-cycle cost analysis, and management of engine parts. Used to investigate effects of such engine-component life-controlling parameters as return-to-service intervals, stresses, capabilities for nondestructive evaluation, and qualities of materials.

Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

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

Zhang, Jie; Draxl, Caroline; Hopson, Thomas

Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5 degrees x 0.67 degrees) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applicationsmore » (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.« less

Probabilistic description of probable maximum precipitation

NASA Astrophysics Data System (ADS)

Ben Alaya, Mohamed Ali; Zwiers, Francis W.; Zhang, Xuebin

2017-04-01

Probable Maximum Precipitation (PMP) is the key parameter used to estimate probable Maximum Flood (PMF). PMP and PMF are important for dam safety and civil engineering purposes. Even if the current knowledge of storm mechanisms remains insufficient to properly evaluate limiting values of extreme precipitation, PMP estimation methods are still based on deterministic consideration, and give only single values. This study aims to provide a probabilistic description of the PMP based on the commonly used method, the so-called moisture maximization. To this end, a probabilistic bivariate extreme values model is proposed to address the limitations of traditional PMP estimates via moisture maximization namely: (i) the inability to evaluate uncertainty and to provide a range PMP values, (ii) the interpretation that a maximum of a data series as a physical upper limit (iii) and the assumption that a PMP event has maximum moisture availability. Results from simulation outputs of the Canadian Regional Climate Model CanRCM4 over North America reveal the high uncertainties inherent in PMP estimates and the non-validity of the assumption that PMP events have maximum moisture availability. This later assumption leads to overestimation of the PMP by an average of about 15% over North America, which may have serious implications for engineering design.

Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods☆

PubMed Central

Bucci, Monica; Mandelli, Maria Luisa; Berman, Jeffrey I.; Amirbekian, Bagrat; Nguyen, Christopher; Berger, Mitchel S.; Henry, Roland G.

2013-01-01

Introduction Diffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways. Methods We used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm2) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex. Results We obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex). Discussion This study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions. PMID:24273719

Distribution functions of probabilistic automata

NASA Technical Reports Server (NTRS)

Vatan, F.

2001-01-01

Each probabilistic automaton M over an alphabet A defines a probability measure Prob sub(M) on the set of all finite and infinite words over A. We can identify a k letter alphabet A with the set {0, 1,..., k-1}, and, hence, we can consider every finite or infinite word w over A as a radix k expansion of a real number X(w) in the interval [0, 1]. This makes X(w) a random variable and the distribution function of M is defined as usual: F(x) := Prob sub(M) { w: X(w) < x }. Utilizing the fixed-point semantics (denotational semantics), extended to probabilistic computations, we investigate the distribution functions of probabilistic automata in detail. Automata with continuous distribution functions are characterized. By a new, and much more easier method, it is shown that the distribution function F(x) is an analytic function if it is a polynomial. Finally, answering a question posed by D. Knuth and A. Yao, we show that a polynomial distribution function F(x) on [0, 1] can be generated by a prob abilistic automaton iff all the roots of F'(x) = 0 in this interval, if any, are rational numbers. For this, we define two dynamical systems on the set of polynomial distributions and study attracting fixed points of random composition of these two systems.

Identifying a Probabilistic Boolean Threshold Network From Samples.

PubMed

Melkman, Avraham A; Cheng, Xiaoqing; Ching, Wai-Ki; Akutsu, Tatsuya

2018-04-01

This paper studies the problem of exactly identifying the structure of a probabilistic Boolean network (PBN) from a given set of samples, where PBNs are probabilistic extensions of Boolean networks. Cheng et al. studied the problem while focusing on PBNs consisting of pairs of AND/OR functions. This paper considers PBNs consisting of Boolean threshold functions while focusing on those threshold functions that have unit coefficients. The treatment of Boolean threshold functions, and triplets and -tuplets of such functions, necessitates a deepening of the theoretical analyses. It is shown that wide classes of PBNs with such threshold functions can be exactly identified from samples under reasonable constraints, which include: 1) PBNs in which any number of threshold functions can be assigned provided that all have the same number of input variables and 2) PBNs consisting of pairs of threshold functions with different numbers of input variables. It is also shown that the problem of deciding the equivalence of two Boolean threshold functions is solvable in pseudopolynomial time but remains co-NP complete.

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.

Probabilistic Simulation of Stress Concentration in Composite Laminates

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Murthy, P. L. N.; Liaw, D. G.

1994-01-01

A computational methodology is described to probabilistically simulate the stress concentration factors (SCF's) in composite laminates. This new approach consists of coupling probabilistic composite mechanics with probabilistic finite element structural analysis. The composite mechanics is used to probabilistically describe all the uncertainties inherent in composite material properties, whereas the finite element is used to probabilistically describe the uncertainties associated with methods to experimentally evaluate SCF's, such as loads, geometry, and supports. The effectiveness of the methodology is demonstrated by using is to simulate the SCF's in three different composite laminates. Simulated results match experimental data for probability density and for cumulative distribution functions. The sensitivity factors indicate that the SCF's are influenced by local stiffness variables, by load eccentricities, and by initial stress fields.

Speech Enhancement Using Gaussian Scale Mixture Models

PubMed Central

Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.

2011-01-01

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress. PMID:21359139

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.

Dominating Scale-Free Networks Using Generalized Probabilistic Methods

PubMed Central

Molnár,, F.; Derzsy, N.; Czabarka, É.; Székely, L.; Szymanski, B. K.; Korniss, G.

2014-01-01

We study ensemble-based graph-theoretical methods aiming to approximate the size of the minimum dominating set (MDS) in scale-free networks. We analyze both analytical upper bounds of dominating sets and numerical realizations for applications. We propose two novel probabilistic dominating set selection strategies that are applicable to heterogeneous networks. One of them obtains the smallest probabilistic dominating set and also outperforms the deterministic degree-ranked method. We show that a degree-dependent probabilistic selection method becomes optimal in its deterministic limit. In addition, we also find the precise limit where selecting high-degree nodes exclusively becomes inefficient for network domination. We validate our results on several real-world networks, and provide highly accurate analytical estimates for our methods. PMID:25200937

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...

NHEXAS PHASE I ARIZONA STUDY--STANDARD OPERATING PROCEDURE FOR PROBABILISTIC APPROACH FOR ESTIMATING INHALATION EXPOSURES TO CHLORPYRIFOS AND DIAZINON (IIT-A-14.0)

EPA Science Inventory

The purpose of this SOP is to describe the procedures undertaken to calculate the inhalation exposures to chlorpyrifos and diazinon using the probabilistic approach. This SOP uses data that have been properly coded and certified with appropriate QA/QC procedures by the University...

Fully probabilistic seismic source inversion - Part 2: Modelling errors and station covariances

NASA Astrophysics Data System (ADS)

Stähler, Simon C.; Sigloch, Karin

2016-11-01

Seismic source inversion, a central task in seismology, is concerned with the estimation of earthquake source parameters and their uncertainties. Estimating uncertainties is particularly challenging because source inversion is a non-linear problem. In a companion paper, Stähler and Sigloch (2014) developed a method of fully Bayesian inference for source parameters, based on measurements of waveform cross-correlation between broadband, teleseismic body-wave observations and their modelled counterparts. This approach yields not only depth and moment tensor estimates but also source time functions. A prerequisite for Bayesian inference is the proper characterisation of the noise afflicting the measurements, a problem we address here. We show that, for realistic broadband body-wave seismograms, the systematic error due to an incomplete physical model affects waveform misfits more strongly than random, ambient background noise. In this situation, the waveform cross-correlation coefficient CC, or rather its decorrelation D = 1 - CC, performs more robustly as a misfit criterion than ℓp norms, more commonly used as sample-by-sample measures of misfit based on distances between individual time samples. From a set of over 900 user-supervised, deterministic earthquake source solutions treated as a quality-controlled reference, we derive the noise distribution on signal decorrelation D = 1 - CC of the broadband seismogram fits between observed and modelled waveforms. The noise on D is found to approximately follow a log-normal distribution, a fortunate fact that readily accommodates the formulation of an empirical likelihood function for D for our multivariate problem. The first and second moments of this multivariate distribution are shown to depend mostly on the signal-to-noise ratio (SNR) of the CC measurements and on the back-azimuthal distances of seismic stations. By identifying and quantifying this likelihood function, we make D and thus waveform cross-correlation measurements usable for fully probabilistic sampling strategies, in source inversion and related applications such as seismic tomography.

Fracture mechanics analysis of cracked structures using weight function and neural network method

NASA Astrophysics Data System (ADS)

Chen, J. G.; Zang, F. G.; Yang, Y.; Shi, K. K.; Fu, X. L.

2018-06-01

Stress intensity factors(SIFs) due to thermal-mechanical load has been established by using weight function method. Two reference stress states sere used to determine the coefficients in the weight function. Results were evaluated by using data from literature and show a good agreement between them. So, the SIFs can be determined quickly using the weight function obtained when cracks subjected to arbitrary loads, and presented method can be used for probabilistic fracture mechanics analysis. A probabilistic methodology considering Monte-Carlo with neural network (MCNN) has been developed. The results indicate that an accurate probabilistic characteristic of the KI can be obtained by using the developed method. The probability of failure increases with the increasing of loads, and the relationship between is nonlinear.

Probabilistic dual heuristic programming-based adaptive critic

NASA Astrophysics Data System (ADS)

Herzallah, Randa

2010-02-01

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.

Variability in perceived satisfaction of reservoir management objectives

USGS Publications Warehouse

Owen, W.J.; Gates, T.K.; Flug, M.

1997-01-01

Fuzzy set theory provides a useful model to address imprecision in interpreting linguistically described objectives for reservoir management. Fuzzy membership functions can be used to represent degrees of objective satisfaction for different values of management variables. However, lack of background information, differing experiences and qualifications, and complex interactions of influencing factors can contribute to significant variability among membership functions derived from surveys of multiple experts. In the present study, probabilistic membership functions are used to model variability in experts' perceptions of satisfaction of objectives for hydropower generation, fish habitat, kayaking, rafting, and scenery preservation on the Green River through operations of Flaming Gorge Dam. Degree of variability in experts' perceptions differed among objectives but resulted in substantial uncertainty in estimation of optimal reservoir releases.

Bayesian probabilistic population projections for all countries.

PubMed

Raftery, Adrian E; Li, Nan; Ševčíková, Hana; Gerland, Patrick; Heilig, Gerhard K

2012-08-28

Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950-1990 are used for estimation, and applied to predict 1990-2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20-64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades.

Probabilistic confidence for decisions based on uncertain reliability estimates

NASA Astrophysics Data System (ADS)

Reid, Stuart G.

2013-05-01

Reliability assessments are commonly carried out to provide a rational basis for risk-informed decisions concerning the design or maintenance of engineering systems and structures. However, calculated reliabilities and associated probabilities of failure often have significant uncertainties associated with the possible estimation errors relative to the 'true' failure probabilities. For uncertain probabilities of failure, a measure of 'probabilistic confidence' has been proposed to reflect the concern that uncertainty about the true probability of failure could result in a system or structure that is unsafe and could subsequently fail. The paper describes how the concept of probabilistic confidence can be applied to evaluate and appropriately limit the probabilities of failure attributable to particular uncertainties such as design errors that may critically affect the dependability of risk-acceptance decisions. This approach is illustrated with regard to the dependability of structural design processes based on prototype testing with uncertainties attributable to sampling variability.

Probabilistic approach for decay heat uncertainty estimation using URANIE platform and MENDEL depletion code

NASA Astrophysics Data System (ADS)

Tsilanizara, A.; Gilardi, N.; Huynh, T. D.; Jouanne, C.; Lahaye, S.; Martinez, J. M.; Diop, C. M.

2014-06-01

The knowledge of the decay heat quantity and the associated uncertainties are important issues for the safety of nuclear facilities. Many codes are available to estimate the decay heat. ORIGEN, FISPACT, DARWIN/PEPIN2 are part of them. MENDEL is a new depletion code developed at CEA, with new software architecture, devoted to the calculation of physical quantities related to fuel cycle studies, in particular decay heat. The purpose of this paper is to present a probabilistic approach to assess decay heat uncertainty due to the decay data uncertainties from nuclear data evaluation like JEFF-3.1.1 or ENDF/B-VII.1. This probabilistic approach is based both on MENDEL code and URANIE software which is a CEA uncertainty analysis platform. As preliminary applications, single thermal fission of uranium 235, plutonium 239 and PWR UOx spent fuel cell are investigated.

Probabilistic sampling of protein conformations: new hope for brute force?

PubMed

Feldman, Howard J; Hogue, Christopher W V

2002-01-01

Protein structure prediction from sequence alone by "brute force" random methods is a computationally expensive problem. Estimates have suggested that it could take all the computers in the world longer than the age of the universe to compute the structure of a single 200-residue protein. Here we investigate the use of a faster version of our FOLDTRAJ probabilistic all-atom protein-structure-sampling algorithm. We have improved the method so that it is now over twenty times faster than originally reported, and capable of rapidly sampling conformational space without lattices. It uses geometrical constraints and a Leonard-Jones type potential for self-avoidance. We have also implemented a novel method to add secondary structure-prediction information to make protein-like amounts of secondary structure in sampled structures. In a set of 100,000 probabilistic conformers of 1VII, 1ENH, and 1PMC generated, the structures with smallest Calpha RMSD from native are 3.95, 5.12, and 5.95A, respectively. Expanding this test to a set of 17 distinct protein folds, we find that all-helical structures are "hit" by brute force more frequently than beta or mixed structures. For small helical proteins or very small non-helical ones, this approach should have a "hit" close enough to detect with a good scoring function in a pool of several million conformers. By fitting the distribution of RMSDs from the native state of each of the 17 sets of conformers to the extreme value distribution, we are able to estimate the size of conformational space for each. With a 0.5A RMSD cutoff, the number of conformers is roughly 2N where N is the number of residues in the protein. This is smaller than previous estimates, indicating an average of only two possible conformations per residue when sterics are accounted for. Our method reduces the effective number of conformations available at each residue by probabilistic bias, without requiring any particular discretization of residue conformational space, and is the fastest method of its kind. With computer speeds doubling every 18 months and parallel and distributed computing becoming more practical, the brute force approach to protein structure prediction may yet have some hope in the near future. Copyright 2001 Wiley-Liss, Inc.

Development of a Probabilistic Tsunami Hazard Analysis in Japan

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

Toshiaki Sakai; Tomoyoshi Takeda; Hiroshi Soraoka

2006-07-01

It is meaningful for tsunami assessment to evaluate phenomena beyond the design basis as well as seismic design. Because once we set the design basis tsunami height, we still have possibilities tsunami height may exceeds the determined design tsunami height due to uncertainties regarding the tsunami phenomena. Probabilistic tsunami risk assessment consists of estimating for tsunami hazard and fragility of structures and executing system analysis. In this report, we apply a method for probabilistic tsunami hazard analysis (PTHA). We introduce a logic tree approach to estimate tsunami hazard curves (relationships between tsunami height and probability of excess) and present anmore » example for Japan. Examples of tsunami hazard curves are illustrated, and uncertainty in the tsunami hazard is displayed by 5-, 16-, 50-, 84- and 95-percentile and mean hazard curves. The result of PTHA will be used for quantitative assessment of the tsunami risk for important facilities located on coastal area. Tsunami hazard curves are the reasonable input data for structures and system analysis. However the evaluation method for estimating fragility of structures and the procedure of system analysis is now being developed. (authors)« less

Model based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach

PubMed Central

Rasmussen, Peter M.; Smith, Amy F.; Sakadžić, Sava; Boas, David A.; Pries, Axel R.; Secomb, Timothy W.; Østergaard, Leif

2017-01-01

Objective In vivo imaging of the microcirculation and network-oriented modeling have emerged as powerful means of studying microvascular function and understanding its physiological significance. Network-oriented modeling may provide the means of summarizing vast amounts of data produced by high-throughput imaging techniques in terms of key, physiological indices. To estimate such indices with sufficient certainty, however, network-oriented analysis must be robust to the inevitable presence of uncertainty due to measurement errors as well as model errors. Methods We propose the Bayesian probabilistic data analysis framework as a means of integrating experimental measurements and network model simulations into a combined and statistically coherent analysis. The framework naturally handles noisy measurements and provides posterior distributions of model parameters as well as physiological indices associated with uncertainty. Results We applied the analysis framework to experimental data from three rat mesentery networks and one mouse brain cortex network. We inferred distributions for more than five hundred unknown pressure and hematocrit boundary conditions. Model predictions were consistent with previous analyses, and remained robust when measurements were omitted from model calibration. Conclusion Our Bayesian probabilistic approach may be suitable for optimizing data acquisition and for analyzing and reporting large datasets acquired as part of microvascular imaging studies. PMID:27987383

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)

PubMed Central

Lechevalier, D.; Ak, R.; Ferguson, M.; Law, K. H.; Lee, Y.-T. T.; Rachuri, S.

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain. PMID:29202125

Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML).

PubMed

Park, J; Lechevalier, D; Ak, R; Ferguson, M; Law, K H; Lee, Y-T T; Rachuri, S

2017-01-01

This paper describes Gaussian process regression (GPR) models presented in predictive model markup language (PMML). PMML is an extensible-markup-language (XML) -based standard language used to represent data-mining and predictive analytic models, as well as pre- and post-processed data. The previous PMML version, PMML 4.2, did not provide capabilities for representing probabilistic (stochastic) machine-learning algorithms that are widely used for constructing predictive models taking the associated uncertainties into consideration. The newly released PMML version 4.3, which includes the GPR model, provides new features: confidence bounds and distribution for the predictive estimations. Both features are needed to establish the foundation for uncertainty quantification analysis. Among various probabilistic machine-learning algorithms, GPR has been widely used for approximating a target function because of its capability of representing complex input and output relationships without predefining a set of basis functions, and predicting a target output with uncertainty quantification. GPR is being employed to various manufacturing data-analytics applications, which necessitates representing this model in a standardized form for easy and rapid employment. In this paper, we present a GPR model and its representation in PMML. Furthermore, we demonstrate a prototype using a real data set in the manufacturing domain.

Probabilistic classifiers with high-dimensional data

PubMed Central

Kim, Kyung In; Simon, Richard

2011-01-01

For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not “anticonservative” using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set. PMID:21087946

An Improved Method for Seismic Event Depth and Moment Tensor Determination: CTBT Related Application

NASA Astrophysics Data System (ADS)

Stachnik, J.; Rozhkov, M.; Baker, B.

2016-12-01

According to the Protocol to CTBT, International Data Center is required to conduct expert technical analysis and special studies to improve event parameters and assist State Parties in identifying the source of specific event. Determination of seismic event source mechanism and its depth is a part of these tasks. It is typically done through a strategic linearized inversion of the waveforms for a complete or subset of source parameters, or similarly defined grid search through precomputed Greens Functions created for particular source models. We show preliminary results using the latter approach from an improved software design and applied on a moderately powered computer. In this development we tried to be compliant with different modes of CTBT monitoring regime and cover wide range of source-receiver distances (regional to teleseismic), resolve shallow source depths, provide full moment tensor solution based on body and surface waves recordings, be fast to satisfy both on-demand studies and automatic processing and properly incorporate observed waveforms and any uncertainties a priori as well as accurately estimate posteriori uncertainties. Implemented HDF5 based Green's Functions pre-packaging allows much greater flexibility in utilizing different software packages and methods for computation. Further additions will have the rapid use of Instaseis/AXISEM full waveform synthetics added to a pre-computed GF archive. Along with traditional post processing analysis of waveform misfits through several objective functions and variance reduction, we follow a probabilistic approach to assess the robustness of moment tensor solution. In a course of this project full moment tensor and depth estimates are determined for DPRK 2009, 2013 and 2016 events and shallow earthquakes using a new implementation of waveform fitting of teleseismic P waves. A full grid search over the entire moment tensor space is used to appropriately sample all possible solutions. A recent method by Tape & Tape (2012) to discretize the complete moment tensor space from a geometric perspective is used. Moment tensors for DPRK events show isotropic percentages greater than 50%. Depth estimates for the DPRK events range from 1.0-1.4 km. Probabilistic uncertainty estimates on the moment tensor parameters provide robustness to solution.

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

Mining disease fingerprints from within genetic pathways.

PubMed

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Mining Disease Fingerprints From Within Genetic Pathways

PubMed Central

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components (‘fingerprints’) of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ∼77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways. PMID:23304411

Damage and Loss Estimation for Natural Gas Networks: The Case of Istanbul

NASA Astrophysics Data System (ADS)

Çaktı, Eser; Hancılar, Ufuk; Şeşetyan, Karin; Bıyıkoǧlu, Hikmet; Şafak, Erdal

2017-04-01

Natural gas networks are one of the major lifeline systems to support human, urban and industrial activities. The continuity of gas supply is critical for almost all functions of modern life. Under natural phenomena such as earthquakes and landslides the damages to the system elements may lead to explosions and fires compromising human life and damaging physical environment. Furthermore, the disruption in the gas supply puts human activities at risk and also results in economical losses. This study is concerned with the performance of one of the largest natural gas distribution systems in the world. Physical damages to Istanbul's natural gas network are estimated under the most recent probabilistic earthquake hazard models available, as well as under simulated ground motions from physics based models. Several vulnerability functions are used in modelling damages to system elements. A first-order assessment of monetary losses to Istanbul's natural gas distribution network is also attempted.

Effects and risk assessment of linear alkylbenzene sulfonates in agricultural soil. 5. Probabilistic risk assessment of linear alkylbenzene sulfonates in sludge-amended soils.

PubMed

Jensen, J; Løkke, H; Holmstrup, M; Krogh, P H; Elsgaard, L

2001-08-01

Linear alkylbenzene sulfonates (LAS) can be found in high concentrations in sewage sludge and, hence, may enter the soil compartment as a result of sludge application. Here, LAS may pose a risk for soil-dwelling organisms. In the present probabilistic risk assessment, statistical extrapolation has been used to assess the risk of LAS to soil ecosystems. By use of a log-normal distribution model, the predicted no-effect concentration (PNEC) was estimated for soil fauna, plants, and a combination of these. Due to the heterogeneous endpoints for microorganisms, including functional as well as structural parameters, the use of sensitivity distributions is not considered to be applicable to this group of organisms, and a direct, expert evaluation of toxicity data was used instead. The soil concentration after sludge application was predicted for a number of scenarios and used as the predicted environmental concentration (PEC) in the risk characterization and calculation of risk quotients (RQ = PEC/PNEC). A LAS concentration of 4.6 mg/kg was used as the current best estimate of PNEC in all RQ calculations. Three levels of LAS contamination (530, 2,600, and 16,100 mg/kg), three half-lives (10, 25, and 40 d), and five different sludge loads (2, 4, 6, 8, and 10 t/ha) were included in the risk scenarios. In Denmark, the initial risk ratio would reach 1.5 in a realistic worst-case consideration. For countries not having similar sludge regulations, the estimated risk ratio may initially be considerably higher. However, even in the most extreme scenarios, the level of LAS is expected to be well beyond the estimated PNEC one year after application. The present risk assessment, therefore, concludes that LAS does not pose a significant risk to fauna, plants, and essential functions of agricultural soils as a result of normal sewage sludge amendment. However, risks have been identified in worst-case scenarios.

Sampling studies to estimate the HIV prevalence rate in female commercial sex workers.

PubMed

Pascom, Ana Roberta Pati; Szwarcwald, Célia Landmann; Barbosa Júnior, Aristides

2010-01-01

We investigated sampling methods being used to estimate the HIV prevalence rate among female commercial sex workers. The studies were classified according to the adequacy or not of the sample size to estimate HIV prevalence rate and according to the sampling method (probabilistic or convenience). We identified 75 studies that estimated the HIV prevalence rate among female sex workers. Most of the studies employed convenience samples. The sample size was not adequate to estimate HIV prevalence rate in 35 studies. The use of convenience sample limits statistical inference for the whole group. It was observed that there was an increase in the number of published studies since 2005, as well as in the number of studies that used probabilistic samples. This represents a large advance in the monitoring of risk behavior practices and HIV prevalence rate in this group.

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

An advanced probabilistic structural analysis method for implicit performance functions

NASA Technical Reports Server (NTRS)

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

1989-01-01

In probabilistic structural analysis, the performance or response functions usually are implicitly defined and must be solved by numerical analysis methods such as finite element methods. In such cases, the most commonly used probabilistic analysis tool is the mean-based, second-moment method which provides only the first two statistical moments. This paper presents a generalized advanced mean value (AMV) method which is capable of establishing the distributions to provide additional information for reliability design. The method requires slightly more computations than the second-moment method but is highly efficient relative to the other alternative methods. In particular, the examples show that the AMV method can be used to solve problems involving non-monotonic functions that result in truncated distributions.

Inference for Continuous-Time Probabilistic Programming

DTIC Science & Technology

2017-12-01

Parzen window density estimator to jointly model the inter-camera travel time intervals, locations of exit/entrances, and velocities of ob- jects...asked to travel across the scene multiple times . Even in such a scenario they formed groups and made social interactions, which Fig. 7: Topology of...INFERENCE FOR CONTINUOUS- TIME PROBABILISTIC PROGRAMMING UNIVERSITY OF CALIFORNIA AT RIVERSIDE DECEMBER 2017 FINAL TECHNICAL REPORT APPROVED FOR

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA.

PubMed

Zhao, Yuchao; Frey, H Christopher

2004-11-01

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

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

Chowdhary, Kenny; Najm, Habib N.

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach

DOE PAGES

Chowdhary, Kenny; Najm, Habib N.

2016-04-13

One of the most widely-used statistical procedures for dimensionality reduction of high dimensional random fields is Principal Component Analysis (PCA), which is based on the Karhunen-Lo eve expansion (KLE) of a stochastic process with finite variance. The KLE is analogous to a Fourier series expansion for a random process, where the goal is to find an orthogonal transformation for the data such that the projection of the data onto this orthogonal subspace is optimal in the L 2 sense, i.e, which minimizes the mean square error. In practice, this orthogonal transformation is determined by performing an SVD (Singular Value Decomposition)more » on the sample covariance matrix or on the data matrix itself. Sampling error is typically ignored when quantifying the principal components, or, equivalently, basis functions of the KLE. Furthermore, it is exacerbated when the sample size is much smaller than the dimension of the random field. In this paper, we introduce a Bayesian KLE procedure, allowing one to obtain a probabilistic model on the principal components, which can account for inaccuracies due to limited sample size. The probabilistic model is built via Bayesian inference, from which the posterior becomes the matrix Bingham density over the space of orthonormal matrices. We use a modified Gibbs sampling procedure to sample on this space and then build a probabilistic Karhunen-Lo eve expansions over random subspaces to obtain a set of low-dimensional surrogates of the stochastic process. We illustrate this probabilistic procedure with a finite dimensional stochastic process inspired by Brownian motion.« less

Probabilistic Material Strength Degradation Model for Inconel 718 Components Subjected to High Temperature, Mechanical Fatigue, Creep and Thermal Fatigue Effects

NASA Technical Reports Server (NTRS)

Bast, Callie Corinne Scheidt

1994-01-01

This thesis presents the on-going development of methodology for a probabilistic material strength degradation model. The probabilistic model, in the form of a postulated randomized multifactor equation, provides for quantification of uncertainty in the lifetime material strength of aerospace propulsion system components subjected to a number of diverse random effects. This model is embodied in the computer program entitled PROMISS, which can include up to eighteen different effects. Presently, the model includes four effects that typically reduce lifetime strength: high temperature, mechanical fatigue, creep, and thermal fatigue. Statistical analysis was conducted on experimental Inconel 718 data obtained from the open literature. This analysis provided regression parameters for use as the model's empirical material constants, thus calibrating the model specifically for Inconel 718. Model calibration was carried out for four variables, namely, high temperature, mechanical fatigue, creep, and thermal fatigue. Methodology to estimate standard deviations of these material constants for input into the probabilistic material strength model was developed. Using the current version of PROMISS, entitled PROMISS93, a sensitivity study for the combined effects of mechanical fatigue, creep, and thermal fatigue was performed. Results, in the form of cumulative distribution functions, illustrated the sensitivity of lifetime strength to any current value of an effect. In addition, verification studies comparing a combination of mechanical fatigue and high temperature effects by model to the combination by experiment were conducted. Thus, for Inconel 718, the basic model assumption of independence between effects was evaluated. Results from this limited verification study strongly supported this assumption.

Structural reliability methods: Code development status

NASA Astrophysics Data System (ADS)

Millwater, Harry R.; Thacker, Ben H.; Wu, Y.-T.; Cruse, T. A.

1991-05-01

The Probabilistic Structures Analysis Method (PSAM) program integrates state of the art probabilistic algorithms with structural analysis methods in order to quantify the behavior of Space Shuttle Main Engine structures subject to uncertain loadings, boundary conditions, material parameters, and geometric conditions. An advanced, efficient probabilistic structural analysis software program, NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) was developed as a deliverable. NESSUS contains a number of integrated software components to perform probabilistic analysis of complex structures. A nonlinear finite element module NESSUS/FEM is used to model the structure and obtain structural sensitivities. Some of the capabilities of NESSUS/FEM are shown. A Fast Probability Integration module NESSUS/FPI estimates the probability given the structural sensitivities. A driver module, PFEM, couples the FEM and FPI. NESSUS, version 5.0, addresses component reliability, resistance, and risk.

Structural reliability methods: Code development status

NASA Technical Reports Server (NTRS)

Millwater, Harry R.; Thacker, Ben H.; Wu, Y.-T.; Cruse, T. A.

1991-01-01

The Probabilistic Structures Analysis Method (PSAM) program integrates state of the art probabilistic algorithms with structural analysis methods in order to quantify the behavior of Space Shuttle Main Engine structures subject to uncertain loadings, boundary conditions, material parameters, and geometric conditions. An advanced, efficient probabilistic structural analysis software program, NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) was developed as a deliverable. NESSUS contains a number of integrated software components to perform probabilistic analysis of complex structures. A nonlinear finite element module NESSUS/FEM is used to model the structure and obtain structural sensitivities. Some of the capabilities of NESSUS/FEM are shown. A Fast Probability Integration module NESSUS/FPI estimates the probability given the structural sensitivities. A driver module, PFEM, couples the FEM and FPI. NESSUS, version 5.0, addresses component reliability, resistance, and risk.

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.

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

PubMed Central

Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

2007-01-01

Background Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org. PMID:17912365

Modeling landslide recurrence in Seattle, Washington, USA

USGS Publications Warehouse

Salciarini, Diana; Godt, Jonathan W.; Savage, William Z.; Baum, Rex L.; Conversini, Pietro

2008-01-01

To manage the hazard associated with shallow landslides, decision makers need an understanding of where and when landslides may occur. A variety of approaches have been used to estimate the hazard from shallow, rainfall-triggered landslides, such as empirical rainfall threshold methods or probabilistic methods based on historical records. The wide availability of Geographic Information Systems (GIS) and digital topographic data has led to the development of analytic methods for landslide hazard estimation that couple steady-state hydrological models with slope stability calculations. Because these methods typically neglect the transient effects of infiltration on slope stability, results cannot be linked with historical or forecasted rainfall sequences. Estimates of the frequency of conditions likely to cause landslides are critical for quantitative risk and hazard assessments. We present results to demonstrate how a transient infiltration model coupled with an infinite slope stability calculation may be used to assess shallow landslide frequency in the City of Seattle, Washington, USA. A module called CRF (Critical RainFall) for estimating deterministic rainfall thresholds has been integrated in the TRIGRS (Transient Rainfall Infiltration and Grid-based Slope-Stability) model that combines a transient, one-dimensional analytic solution for pore-pressure response to rainfall infiltration with an infinite slope stability calculation. Input data for the extended model include topographic slope, colluvial thickness, initial water-table depth, material properties, and rainfall durations. This approach is combined with a statistical treatment of rainfall using a GEV (General Extreme Value) probabilistic distribution to produce maps showing the shallow landslide recurrence induced, on a spatially distributed basis, as a function of rainfall duration and hillslope characteristics.

Decision making generalized by a cumulative probability weighting function

NASA Astrophysics Data System (ADS)

dos Santos, Lindomar Soares; Destefano, Natália; Martinez, Alexandre Souto

2018-01-01

Typical examples of intertemporal decision making involve situations in which individuals must choose between a smaller reward, but more immediate, and a larger one, delivered later. Analogously, probabilistic decision making involves choices between options whose consequences differ in relation to their probability of receiving. In Economics, the expected utility theory (EUT) and the discounted utility theory (DUT) are traditionally accepted normative models for describing, respectively, probabilistic and intertemporal decision making. A large number of experiments confirmed that the linearity assumed by the EUT does not explain some observed behaviors, as nonlinear preference, risk-seeking and loss aversion. That observation led to the development of new theoretical models, called non-expected utility theories (NEUT), which include a nonlinear transformation of the probability scale. An essential feature of the so-called preference function of these theories is that the probabilities are transformed by decision weights by means of a (cumulative) probability weighting function, w(p) . We obtain in this article a generalized function for the probabilistic discount process. This function has as particular cases mathematical forms already consecrated in the literature, including discount models that consider effects of psychophysical perception. We also propose a new generalized function for the functional form of w. The limiting cases of this function encompass some parametric forms already proposed in the literature. Far beyond a mere generalization, our function allows the interpretation of probabilistic decision making theories based on the assumption that individuals behave similarly in the face of probabilities and delays and is supported by phenomenological models.

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 2. Application to Owens Valley, California

USGS Publications Warehouse

Guymon, Gary L.; Yen, Chung-Cheng

1990-01-01

The applicability of a deterministic-probabilistic model for predicting water tables in southern Owens Valley, California, is evaluated. The model is based on a two-layer deterministic model that is cascaded with a two-point probability model. To reduce the potentially large number of uncertain variables in the deterministic model, lumping of uncertain variables was evaluated by sensitivity analysis to reduce the total number of uncertain variables to three variables: hydraulic conductivity, storage coefficient or specific yield, and source-sink function. Results demonstrate that lumping of uncertain parameters reduces computational effort while providing sufficient precision for the case studied. Simulated spatial coefficients of variation for water table temporal position in most of the basin is small, which suggests that deterministic models can predict water tables in these areas with good precision. However, in several important areas where pumping occurs or the geology is complex, the simulated spatial coefficients of variation are over estimated by the two-point probability method.

An efficient deterministic-probabilistic approach to modeling regional groundwater flow: 2. Application to Owens Valley, California

NASA Astrophysics Data System (ADS)

Guymon, Gary L.; Yen, Chung-Cheng

1990-07-01

The applicability of a deterministic-probabilistic model for predicting water tables in southern Owens Valley, California, is evaluated. The model is based on a two-layer deterministic model that is cascaded with a two-point probability model. To reduce the potentially large number of uncertain variables in the deterministic model, lumping of uncertain variables was evaluated by sensitivity analysis to reduce the total number of uncertain variables to three variables: hydraulic conductivity, storage coefficient or specific yield, and source-sink function. Results demonstrate that lumping of uncertain parameters reduces computational effort while providing sufficient precision for the case studied. Simulated spatial coefficients of variation for water table temporal position in most of the basin is small, which suggests that deterministic models can predict water tables in these areas with good precision. However, in several important areas where pumping occurs or the geology is complex, the simulated spatial coefficients of variation are over estimated by the two-point probability method.

Methods for estimating the amount of vernal pool habitat in the northeastern United States

USGS Publications Warehouse

Van Meter, R.; Bailey, L.L.; Grant, E.H.C.

2008-01-01

The loss of small, seasonal wetlands is a major concern for a variety of state, local, and federal organizations in the northeastern U.S. Identifying and estimating the number of vernal pools within a given region is critical to developing long-term conservation and management strategies for these unique habitats and their faunal communities. We use three probabilistic sampling methods (simple random sampling, adaptive cluster sampling, and the dual frame method) to estimate the number of vernal pools on protected, forested lands. Overall, these methods yielded similar values of vernal pool abundance for each study area, and suggest that photographic interpretation alone may grossly underestimate the number of vernal pools in forested habitats. We compare the relative efficiency of each method and discuss ways of improving precision. Acknowledging that the objectives of a study or monitoring program ultimately determine which sampling designs are most appropriate, we recommend that some type of probabilistic sampling method be applied. We view the dual-frame method as an especially useful way of combining incomplete remote sensing methods, such as aerial photograph interpretation, with a probabilistic sample of the entire area of interest to provide more robust estimates of the number of vernal pools and a more representative sample of existing vernal pool habitats.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

A probabilistic model for detecting rigid domains in protein structures.

PubMed

Nguyen, Thach; Habeck, Michael

2016-09-01

Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states. We present a probabilistic model for detecting rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy. We assess the power of our method on several thousand entries of the DynDom database and discuss applications to various complex biomolecular systems. The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection : mhabeck@gwdg.de. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

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

Schneider, Michael D.; Dawson, William A.; Hogg, David W.

2015-07-01

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxymore » properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.« less

Uncertainty Estimation in Tsunami Initial Condition From Rapid Bayesian Finite Fault Modeling

NASA Astrophysics Data System (ADS)

Benavente, R. F.; Dettmer, J.; Cummins, P. R.; Urrutia, A.; Cienfuegos, R.

2017-12-01

It is well known that kinematic rupture models for a given earthquake can present discrepancies even when similar datasets are employed in the inversion process. While quantifying this variability can be critical when making early estimates of the earthquake and triggered tsunami impact, "most likely models" are normally used for this purpose. In this work, we quantify the uncertainty of the tsunami initial condition for the great Illapel earthquake (Mw = 8.3, 2015, Chile). We focus on utilizing data and inversion methods that are suitable to rapid source characterization yet provide meaningful and robust results. Rupture models from teleseismic body and surface waves as well as W-phase are derived and accompanied by Bayesian uncertainty estimates from linearized inversion under positivity constraints. We show that robust and consistent features about the rupture kinematics appear when working within this probabilistic framework. Moreover, by using static dislocation theory, we translate the probabilistic slip distributions into seafloor deformation which we interpret as a tsunami initial condition. After considering uncertainty, our probabilistic seafloor deformation models obtained from different data types appear consistent with each other providing meaningful results. We also show that selecting just a single "representative" solution from the ensemble of initial conditions for tsunami propagation may lead to overestimating information content in the data. Our results suggest that rapid, probabilistic rupture models can play a significant role during emergency response by providing robust information about the extent of the disaster.

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.

Estimates of reservoir methane emissions based on a spatially balanced probabilistic-survey

EPA Science Inventory

Global estimates of methane (CH4) emissions from reservoirs are poorly constrained, partly due to the challenges of accounting for intra-reservoir spatial variability. Reservoir-scale emission rates are often estimated by extrapolating from measurement made at a few locations; h...

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.

Protocol and Demonstrations of Probabilistic Reliability Assessment for Structural Health Monitoring Systems (Preprint)

DTIC Science & Technology

2011-11-01

assessment to quality of localization/characterization estimates. This protocol includes four critical components: (1) a procedure to identify the...critical factors impacting SHM system performance; (2) a multistage or hierarchical approach to SHM system validation; (3) a model -assisted evaluation...Lindgren, E. A ., Buynak, C. F., Steffes, G., Derriso, M., “ Model -assisted Probabilistic Reliability Assessment for Structural Health Monitoring

NHEXAS PHASE I ARIZONA STUDY--STANDARD OPERATING PROCEDURE FOR PROBABILISTIC APPROACH FOR CALCULATING INGESTION EXPOSURE FROM DAY 4 COMPOSITE MEASUREMENTS, THE DIRECT METHOD OF EXPOSURE ESTIMATION (IIT-A-15.0)

EPA Science Inventory

The purpose of this SOP is to describe the procedures undertaken to calculate the ingestion exposure using composite food chemical residue values from the day of direct measurements. The calculation is based on the probabilistic approach. This SOP uses data that have been proper...

Review of methods for developing regional probabilistic risk assessments, part 2: modeling invasive plant, insect, and pathogen species

Treesearch

P. B. Woodbury; D. A. Weinstein

2010-01-01

We reviewed probabilistic regional risk assessment methodologies to identify the methods that are currently in use and are capable of estimating threats to ecosystems from fire and fuels, invasive species, and their interactions with stressors. In a companion chapter, we highlight methods useful for evaluating risks from fire. In this chapter, we highlight methods...

Bayesian probabilistic population projections for all countries

PubMed Central

Raftery, Adrian E.; Li, Nan; Ševčíková, Hana; Gerland, Patrick; Heilig, Gerhard K.

2012-01-01

Projections of countries’ future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950–1990 are used for estimation, and applied to predict 1990–2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20–64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades. PMID:22908249

An investigation into the probabilistic combination of quasi-static and random accelerations

NASA Technical Reports Server (NTRS)

Schock, R. W.; Tuell, L. P.

1984-01-01

The development of design load factors for aerospace and aircraft components and experiment support structures, which are subject to a simultaneous vehicle dynamic vibration (quasi-static) and acoustically generated random vibration, require the selection of a combination methodology. Typically, the procedure is to define the quasi-static and the random generated response separately, and arithmetically add or root sum square to get combined accelerations. Since the combination of a probabilistic and a deterministic function yield a probabilistic function, a viable alternate approach would be to determine the characteristics of the combined acceleration probability density function and select an appropriate percentile level for the combined acceleration. The following paper develops this mechanism and provides graphical data to select combined accelerations for most popular percentile levels.

R package to estimate intracluster correlation coefficient with confidence interval for binary data.

PubMed

Chakraborty, Hrishikesh; Hossain, Akhtar

2018-03-01

The Intracluster Correlation Coefficient (ICC) is a major parameter of interest in cluster randomized trials that measures the degree to which responses within the same cluster are correlated. There are several types of ICC estimators and its confidence intervals (CI) suggested in the literature for binary data. Studies have compared relative weaknesses and advantages of ICC estimators as well as its CI for binary data and suggested situations where one is advantageous in practical research. The commonly used statistical computing systems currently facilitate estimation of only a very few variants of ICC and its CI. To address the limitations of current statistical packages, we developed an R package, ICCbin, to facilitate estimating ICC and its CI for binary responses using different methods. The ICCbin package is designed to provide estimates of ICC in 16 different ways including analysis of variance methods, moments based estimation, direct probabilistic methods, correlation based estimation, and resampling method. CI of ICC is estimated using 5 different methods. It also generates cluster binary data using exchangeable correlation structure. ICCbin package provides two functions for users. The function rcbin() generates cluster binary data and the function iccbin() estimates ICC and it's CI. The users can choose appropriate ICC and its CI estimate from the wide selection of estimates from the outputs. The R package ICCbin presents very flexible and easy to use ways to generate cluster binary data and to estimate ICC and it's CI for binary response using different methods. The package ICCbin is freely available for use with R from the CRAN repository (https://cran.r-project.org/package=ICCbin). We believe that this package can be a very useful tool for researchers to design cluster randomized trials with binary outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Modeling stochastic frontier based on vine copulas

NASA Astrophysics Data System (ADS)

Constantino, Michel; Candido, Osvaldo; Tabak, Benjamin M.; da Costa, Reginaldo Brito

2017-11-01

This article models a production function and analyzes the technical efficiency of listed companies in the United States, Germany and England between 2005 and 2012 based on the vine copula approach. Traditional estimates of the stochastic frontier assume that data is multivariate normally distributed and there is no source of asymmetry. The proposed method based on vine copulas allow us to explore different types of asymmetry and multivariate distribution. Using data on product, capital and labor, we measure the relative efficiency of the vine production function and estimate the coefficient used in the stochastic frontier literature for comparison purposes. This production vine copula predicts the value added by firms with given capital and labor in a probabilistic way. It thereby stands in sharp contrast to the production function, where the output of firms is completely deterministic. The results show that, on average, S&P500 companies are more efficient than companies listed in England and Germany, which presented similar average efficiency coefficients. For comparative purposes, the traditional stochastic frontier was estimated and the results showed discrepancies between the coefficients obtained by the application of the two methods, traditional and frontier-vine, opening new paths of non-linear research.

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.

PubMed

Herzallah, Randa

2013-06-01

Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Káarnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Copyright © 2013 Elsevier Ltd. All rights reserved.

Probabilistic simulation of uncertainties in thermal structures

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Shiao, Michael

1990-01-01

Development of probabilistic structural analysis methods for hot structures is a major activity at Lewis Research Center. It consists of five program elements: (1) probabilistic loads; (2) probabilistic finite element analysis; (3) probabilistic material behavior; (4) assessment of reliability and risk; and (5) probabilistic structural performance evaluation. Recent progress includes: (1) quantification of the effects of uncertainties for several variables on high pressure fuel turbopump (HPFT) blade temperature, pressure, and torque of the Space Shuttle Main Engine (SSME); (2) the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; (3) evaluation of the failure probability; (4) reliability and risk-cost assessment, and (5) an outline of an emerging approach for eventual hot structures certification. Collectively, the results demonstrate that the structural durability/reliability of hot structural components can be effectively evaluated in a formal probabilistic framework. In addition, the approach can be readily extended to computationally simulate certification of hot structures for aerospace environments.

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 at Lewis Research Center is presented. Three elements of the research program are: (1) composite load spectra methodology; (2) probabilistic structural analysis methodology; and (3) probabilistic structural analysis application. Recent progress includes: (1) quantification of the effects of uncertainties for several variables on high pressure fuel turbopump (HPFT) turbine blade temperature, pressure, and torque of the space shuttle main engine (SSME); (2) the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; and (3) evaluation of the failure probability. Collectively, the results demonstrate that the structural durability of hot engine structural components can be effectively evaluated in a formal probabilistic/reliability framework.

Displaying uncertainty: investigating the effects of display format and specificity.

PubMed

Bisantz, Ann M; Marsiglio, Stephanie Schinzing; Munch, Jessica

2005-01-01

We conducted four studies regarding the representation of probabilistic information. Experiments 1 through 3 compared performance on a simulated stock purchase task, in which information regarding stock profitability was probabilistic. Two variables were manipulated: display format for probabilistic information (blurred and colored icons, linguistic phrases, numeric expressions, and combinations) and specificity level (in which the number and size of discrete steps into which the probabilistic information was mapped differed). Results indicated few performance differences attributable to display format; however, performance did improve with greater specificity. Experiment 4, in which participants generated membership functions corresponding to three display formats, found a high degree of similarity in functions across formats and participants and a strong relationship between the shape of the membership function and the intended meaning of the representation. These results indicate that participants can successfully interpret nonnumeric representations of uncertainty and can use such representations in a manner similar to the way numeric expressions are used in a decision-making task. Actual or potential applications of this research include the use of graphical representations of uncertainty in systems such as command and control and situation displays.

Aggregate exposure approaches for parabens in personal care products: a case assessment for children between 0 and 3 years old

PubMed Central

Gosens, Ilse; Delmaar, Christiaan J E; ter Burg, Wouter; de Heer, Cees; Schuur, A Gerlienke

2014-01-01

In the risk assessment of chemical substances, aggregation of exposure to a substance from different sources via different pathways is not common practice. Focusing the exposure assessment on a substance from a single source can lead to a significant underestimation of the risk. To gain more insight on how to perform an aggregate exposure assessment, we applied a deterministic (tier 1) and a person-oriented probabilistic approach (tier 2) for exposure to the four most common parabens through personal care products in children between 0 and 3 years old. Following a deterministic approach, a worst-case exposure estimate is calculated for methyl-, ethyl-, propyl- and butylparaben. As an illustration for risk assessment, Margins of Exposure (MoE) are calculated. These are 991 and 4966 for methyl- and ethylparaben, and 8 and 10 for propyl- and butylparaben, respectively. In tier 2, more detailed information on product use has been obtained from a small survey on product use of consumers. A probabilistic exposure assessment is performed to estimate the variability and uncertainty of exposure in a population. Results show that the internal exposure for each paraben is below the level determined in tier 1. However, for propyl- and butylparaben, the percentile of the population with an exposure probability above the assumed “safe” MoE of 100, is 13% and 7%, respectively. In conclusion, a tier 1 approach can be performed using simple equations and default point estimates, and serves as a starting point for exposure and risk assessment. If refinement is warranted, the more data demanding person-oriented probabilistic approach should be used. This probabilistic approach results in a more realistic exposure estimate, including the uncertainty, and allows determining the main drivers of exposure. Furthermore, it allows to estimate the percentage of the population for which the exposure is likely to be above a specific value. PMID:23801276

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

PubMed

Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

2017-10-07

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Reliability-Based Control Design for Uncertain Systems

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a robust control design methodology for systems with probabilistic parametric uncertainty. Control design is carried out by solving a reliability-based multi-objective optimization problem where the probability of violating design requirements is minimized. Simultaneously, failure domains are optimally enlarged to enable global improvements in the closed-loop performance. To enable an efficient numerical implementation, a hybrid approach for estimating reliability metrics is developed. This approach, which integrates deterministic sampling and asymptotic approximations, greatly reduces the numerical burden associated with complex probabilistic computations without compromising the accuracy of the results. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Statistical description of non-Gaussian samples in the F2 layer of the ionosphere during heliogeophysical disturbances

NASA Astrophysics Data System (ADS)

Sergeenko, N. P.

2017-11-01

An adequate statistical method should be developed in order to predict probabilistically the range of ionospheric parameters. This problem is solved in this paper. The time series of the critical frequency of the layer F2- foF2( t) were subjected to statistical processing. For the obtained samples {δ foF2}, statistical distributions and invariants up to the fourth order are calculated. The analysis shows that the distributions differ from the Gaussian law during the disturbances. At levels of sufficiently small probability distributions, there are arbitrarily large deviations from the model of the normal process. Therefore, it is attempted to describe statistical samples {δ foF2} based on the Poisson model. For the studied samples, the exponential characteristic function is selected under the assumption that time series are a superposition of some deterministic and random processes. Using the Fourier transform, the characteristic function is transformed into a nonholomorphic excessive-asymmetric probability-density function. The statistical distributions of the samples {δ foF2} calculated for the disturbed periods are compared with the obtained model distribution function. According to the Kolmogorov's criterion, the probabilities of the coincidence of a posteriori distributions with the theoretical ones are P 0.7-0.9. The conducted analysis makes it possible to draw a conclusion about the applicability of a model based on the Poisson random process for the statistical description and probabilistic variation estimates during heliogeophysical disturbances of the variations {δ foF2}.

Propagating Water Quality Analysis Uncertainty Into Resource Management Decisions Through Probabilistic Modeling

NASA Astrophysics Data System (ADS)

Gronewold, A. D.; Wolpert, R. L.; Reckhow, K. H.

2007-12-01

Most probable number (MPN) and colony-forming-unit (CFU) are two estimates of fecal coliform bacteria concentration commonly used as measures of water quality in United States shellfish harvesting waters. The MPN is the maximum likelihood estimate (or MLE) of the true fecal coliform concentration based on counts of non-sterile tubes in serial dilution of a sample aliquot, indicating bacterial metabolic activity. The CFU is the MLE of the true fecal coliform concentration based on the number of bacteria colonies emerging on a growth plate after inoculation from a sample aliquot. Each estimating procedure has intrinsic variability and is subject to additional uncertainty arising from minor variations in experimental protocol. Several versions of each procedure (using different sized aliquots or different numbers of tubes, for example) are in common use, each with its own levels of probabilistic and experimental error and uncertainty. It has been observed empirically that the MPN procedure is more variable than the CFU procedure, and that MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies. We construct a probabilistic model that provides a clear theoretical explanation for the observed variability in, and discrepancy between, MPN and CFU measurements. We then explore how this variability and uncertainty might propagate into shellfish harvesting area management decisions through a two-phased modeling strategy. First, we apply our probabilistic model in a simulation-based analysis of future water quality standard violation frequencies under alternative land use scenarios, such as those evaluated under guidelines of the total maximum daily load (TMDL) program. Second, we apply our model to water quality data from shellfish harvesting areas which at present are closed (either conditionally or permanently) to shellfishing, to determine if alternative laboratory analysis procedures might have led to different management decisions. Our research results indicate that the (often large) observed differences between MPN and CFU values for the same water body are well within the ranges predicted by our probabilistic model. Our research also indicates that the probability of violating current water quality guidelines at specified true fecal coliform concentrations depends on the laboratory procedure used. As a result, quality-based management decisions, such as opening or closing a shellfishing area, may also depend on the laboratory procedure used.

Derivation of Hunt equation for suspension distribution using Shannon entropy theory

NASA Astrophysics Data System (ADS)

Kundu, Snehasis

2017-12-01

In this study, the Hunt equation for computing suspension concentration in sediment-laden flows is derived using Shannon entropy theory. Considering the inverse of the void ratio as a random variable and using principle of maximum entropy, probability density function and cumulative distribution function of suspension concentration is derived. A new and more general cumulative distribution function for the flow domain is proposed which includes several specific other models of CDF reported in literature. This general form of cumulative distribution function also helps to derive the Rouse equation. The entropy based approach helps to estimate model parameters using suspension data of sediment concentration which shows the advantage of using entropy theory. Finally model parameters in the entropy based model are also expressed as functions of the Rouse number to establish a link between the parameters of the deterministic and probabilistic approaches.

A partially reflecting random walk on spheres algorithm for electrical impedance tomography

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

Maire, Sylvain, E-mail: maire@univ-tln.fr; Simon, Martin, E-mail: simon@math.uni-mainz.de

2015-12-15

In this work, we develop a probabilistic estimator for the voltage-to-current map arising in electrical impedance tomography. This novel so-called partially reflecting random walk on spheres estimator enables Monte Carlo methods to compute the voltage-to-current map in an embarrassingly parallel manner, which is an important issue with regard to the corresponding inverse problem. Our method uses the well-known random walk on spheres algorithm inside subdomains where the diffusion coefficient is constant and employs replacement techniques motivated by finite difference discretization to deal with both mixed boundary conditions and interface transmission conditions. We analyze the global bias and the variance ofmore » the new estimator both theoretically and experimentally. Subsequently, the variance of the new estimator is considerably reduced via a novel control variate conditional sampling technique which yields a highly efficient hybrid forward solver coupling probabilistic and deterministic algorithms.« less

Phase estimation of coherent states with a noiseless linear amplifier

NASA Astrophysics Data System (ADS)

Assad, Syed M.; Bradshaw, Mark; Lam, Ping Koy

Amplification of quantum states is inevitably accompanied with the introduction of noise at the output. For protocols that are probabilistic with heralded success, noiseless linear amplification in theory may still be possible. When the protocol is successful, it can lead to an output that is a noiselessly amplified copy of the input. When the protocol is unsuccessful, the output state is degraded and is usually discarded. Probabilistic protocols may improve the performance of some quantum information protocols, but not for metrology if the whole statistics is taken into consideration. We calculate the precision limits on estimating the phase of coherent states using a noiseless linear amplifier by computing its quantum Fisher information and we show that on average, the noiseless linear amplifier does not improve the phase estimate. We also discuss the case where abstention from measurement can reduce the cost for estimation.

Monitoring and exposure assessment of pesticide residues in cowpea (Vigna unguiculata L. Walp) from five provinces of southern China.

PubMed

Huan, Zhibo; Xu, Zhi; Luo, Jinhui; Xie, Defang

2016-11-01

Residues of 14 pesticides were determined in 150 cowpea samples collected in five southern Chinese provinces in 2013 and 2014.70% samples were detected one or more residues. 61.3% samples were illegal mainly because of detection of unauthorized pesticides. 14.0% samples contained more than three pesticides. Deterministic and probabilistic methods were used to assess the chronic and acute risk of pesticides in cowpea to eight subgroups of people. Deterministic assessment showed that the estimated short-term intakes (ESTIs) of carbofuran were 1199.4%-2621.9% of the acute reference doses (ARfD) while the rates were 985.9%-4114.7% using probabilistic assessment. Probabilistic assessment showed 4.2%-7.8% subjects may suffer from unacceptable acute risk from carbofuran contaminated cowpeas from the five provinces (especially children). But undue concern is not necessary, because all the estimations are based on conservative assumption. Copyright © 2016 Elsevier Inc. All rights reserved.

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 risk analysis of building contamination.

PubMed

Bolster, D T; Tartakovsky, D M

2008-10-01

We present a general framework for probabilistic risk assessment (PRA) of building contamination. PRA provides a powerful tool for the rigorous quantification of risk in contamination of building spaces. A typical PRA starts by identifying relevant components of a system (e.g. ventilation system components, potential sources of contaminants, remediation methods) and proceeds by using available information and statistical inference to estimate the probabilities of their failure. These probabilities are then combined by means of fault-tree analyses to yield probabilistic estimates of the risk of system failure (e.g. building contamination). A sensitivity study of PRAs can identify features and potential problems that need to be addressed with the most urgency. Often PRAs are amenable to approximations, which can significantly simplify the approach. All these features of PRA are presented in this paper via a simple illustrative example, which can be built upon in further studies. The tool presented here can be used to design and maintain adequate ventilation systems to minimize exposure of occupants to contaminants.

A probabilistic multi-criteria decision making technique for conceptual and preliminary aerospace systems design

NASA Astrophysics Data System (ADS)

Bandte, Oliver

It has always been the intention of systems engineering to invent or produce the best product possible. Many design techniques have been introduced over the course of decades that try to fulfill this intention. Unfortunately, no technique has succeeded in combining multi-criteria decision making with probabilistic design. The design technique developed in this thesis, the Joint Probabilistic Decision Making (JPDM) technique, successfully overcomes this deficiency by generating a multivariate probability distribution that serves in conjunction with a criterion value range of interest as a universally applicable objective function for multi-criteria optimization and product selection. This new objective function constitutes a meaningful Xnetric, called Probability of Success (POS), that allows the customer or designer to make a decision based on the chance of satisfying the customer's goals. In order to incorporate a joint probabilistic formulation into the systems design process, two algorithms are created that allow for an easy implementation into a numerical design framework: the (multivariate) Empirical Distribution Function and the Joint Probability Model. The Empirical Distribution Function estimates the probability that an event occurred by counting how many times it occurred in a given sample. The Joint Probability Model on the other hand is an analytical parametric model for the multivariate joint probability. It is comprised of the product of the univariate criterion distributions, generated by the traditional probabilistic design process, multiplied with a correlation function that is based on available correlation information between pairs of random variables. JPDM is an excellent tool for multi-objective optimization and product selection, because of its ability to transform disparate objectives into a single figure of merit, the likelihood of successfully meeting all goals or POS. The advantage of JPDM over other multi-criteria decision making techniques is that POS constitutes a single optimizable function or metric that enables a comparison of all alternative solutions on an equal basis. Hence, POS allows for the use of any standard single-objective optimization technique available and simplifies a complex multi-criteria selection problem into a simple ordering problem, where the solution with the highest POS is best. By distinguishing between controllable and uncontrollable variables in the design process, JPDM can account for the uncertain values of the uncontrollable variables that are inherent to the design problem, while facilitating an easy adjustment of the controllable ones to achieve the highest possible POS. Finally, JPDM's superiority over current multi-criteria decision making techniques is demonstrated with an optimization of a supersonic transport concept and ten contrived equations as well as a product selection example, determining an airline's best choice among Boeing's B-747, B-777, Airbus' A340, and a Supersonic Transport. The optimization examples demonstrate JPDM's ability to produce a better solution with a higher POS than an Overall Evaluation Criterion or Goal Programming approach. Similarly, the product selection example demonstrates JPDM's ability to produce a better solution with a higher POS and different ranking than the Overall Evaluation Criterion or Technique for Order Preferences by Similarity to the Ideal Solution (TOPSIS) approach.

A Logical Approach to Multilevel Security of Probabilistic Systems

DTIC Science & Technology

1998-01-01

their usefulness in security analysis. Key Words: Formal modeling, Veri cation, Knowledge, Security, Probabilistic Systems Supported by grant HKUST 608...94E from the Hong Kong Research Grants Council. yAuthor for correspondence (syverson@itd.nrl.navy.mil). Supported by ONR. 1 Report Documentation Page...Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the

Probabilistic liver atlas construction.

PubMed

Dura, Esther; Domingo, Juan; Ayala, Guillermo; Marti-Bonmati, Luis; Goceri, E

2017-01-13

Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step. We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

Exposure Estimation and Interpretation of Occupational Risk: Enhanced Information for the Occupational Risk Manager

PubMed Central

Waters, Martha; McKernan, Lauralynn; Maier, Andrew; Jayjock, Michael; Schaeffer, Val; Brosseau, Lisa

2015-01-01

The fundamental goal of this article is to describe, define, and analyze the components of the risk characterization process for occupational exposures. Current methods are described for the probabilistic characterization of exposure, including newer techniques that have increasing applications for assessing data from occupational exposure scenarios. In addition, since the probability of health effects reflects variability in the exposure estimate as well as the dose-response curve—the integrated considerations of variability surrounding both components of the risk characterization provide greater information to the occupational hygienist. Probabilistic tools provide a more informed view of exposure as compared to use of discrete point estimates for these inputs to the risk characterization process. Active use of such tools for exposure and risk assessment will lead to a scientifically supported worker health protection program. Understanding the bases for an occupational risk assessment, focusing on important sources of variability and uncertainty enables characterizing occupational risk in terms of a probability, rather than a binary decision of acceptable risk or unacceptable risk. A critical review of existing methods highlights several conclusions: (1) exposure estimates and the dose-response are impacted by both variability and uncertainty and a well-developed risk characterization reflects and communicates this consideration; (2) occupational risk is probabilistic in nature and most accurately considered as a distribution, not a point estimate; and (3) occupational hygienists have a variety of tools available to incorporate concepts of risk characterization into occupational health and practice. PMID:26302336

Validation analysis of probabilistic models of dietary exposure to food additives.

PubMed

Gilsenan, M B; Thompson, R L; Lambe, J; Gibney, M J

2003-10-01

The validity of a range of simple conceptual models designed specifically for the estimation of food additive intakes using probabilistic analysis was assessed. Modelled intake estimates that fell below traditional conservative point estimates of intake and above 'true' additive intakes (calculated from a reference database at brand level) were considered to be in a valid region. Models were developed for 10 food additives by combining food intake data, the probability of an additive being present in a food group and additive concentration data. Food intake and additive concentration data were entered as raw data or as a lognormal distribution, and the probability of an additive being present was entered based on the per cent brands or the per cent eating occasions within a food group that contained an additive. Since the three model components assumed two possible modes of input, the validity of eight (2(3)) model combinations was assessed. All model inputs were derived from the reference database. An iterative approach was employed in which the validity of individual model components was assessed first, followed by validation of full conceptual models. While the distribution of intake estimates from models fell below conservative intakes, which assume that the additive is present at maximum permitted levels (MPLs) in all foods in which it is permitted, intake estimates were not consistently above 'true' intakes. These analyses indicate the need for more complex models for the estimation of food additive intakes using probabilistic analysis. Such models should incorporate information on market share and/or brand loyalty.

Development and comparison of metrics for evaluating climate models and estimation of projection uncertainty

NASA Astrophysics Data System (ADS)

Ring, Christoph; Pollinger, Felix; Kaspar-Ott, Irena; Hertig, Elke; Jacobeit, Jucundus; Paeth, Heiko

2017-04-01

The COMEPRO project (Comparison of Metrics for Probabilistic Climate Change Projections of Mediterranean Precipitation), funded by the Deutsche Forschungsgemeinschaft (DFG), is dedicated to the development of new evaluation metrics for state-of-the-art climate models. Further, we analyze implications for probabilistic projections of climate change. This study focuses on the results of 4-field matrix metrics. Here, six different approaches are compared. We evaluate 24 models of the Coupled Model Intercomparison Project Phase 3 (CMIP3), 40 of CMIP5 and 18 of the Coordinated Regional Downscaling Experiment (CORDEX). In addition to the annual and seasonal precipitation the mean temperature is analysed. We consider both 50-year trend and climatological mean for the second half of the 20th century. For the probabilistic projections of climate change A1b, A2 (CMIP3) and RCP4.5, RCP8.5 (CMIP5,CORDEX) scenarios are used. The eight main study areas are located in the Mediterranean. However, we apply our metrics to globally distributed regions as well. The metrics show high simulation quality of temperature trend and both precipitation and temperature mean for most climate models and study areas. In addition, we find high potential for model weighting in order to reduce uncertainty. These results are in line with other accepted evaluation metrics and studies. The comparison of the different 4-field approaches reveals high correlations for most metrics. The results of the metric-weighted probabilistic density functions of climate change are heterogeneous. We find for different regions and seasons both increases and decreases of uncertainty. The analysis of global study areas is consistent with the regional study areas of the Medeiterrenean.

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.

Probabilistic Aeroelastic Analysis Developed for Turbomachinery Components

NASA Technical Reports Server (NTRS)

Reddy, T. S. R.; Mital, Subodh K.; Stefko, George L.; Pai, Shantaram S.

2003-01-01

Aeroelastic analyses for advanced turbomachines are being developed for use at the NASA Glenn Research Center and industry. However, these analyses at present are used for turbomachinery design with uncertainties accounted for by using safety factors. This approach may lead to overly conservative designs, thereby reducing the potential of designing higher efficiency engines. An integration of the deterministic aeroelastic analysis methods with probabilistic analysis methods offers the potential to design efficient engines with fewer aeroelastic problems and to make a quantum leap toward designing safe reliable engines. In this research, probabilistic analysis is integrated with aeroelastic analysis: (1) to determine the parameters that most affect the aeroelastic characteristics (forced response and stability) of a turbomachine component such as a fan, compressor, or turbine and (2) to give the acceptable standard deviation on the design parameters for an aeroelastically stable system. The approach taken is to combine the aeroelastic analysis of the MISER (MIStuned Engine Response) code with the FPI (fast probability integration) code. The role of MISER is to provide the functional relationships that tie the structural and aerodynamic parameters (the primitive variables) to the forced response amplitudes and stability eigenvalues (the response properties). The role of FPI is to perform probabilistic analyses by utilizing the response properties generated by MISER. The results are a probability density function for the response properties. The probabilistic sensitivities of the response variables to uncertainty in primitive variables are obtained as a byproduct of the FPI technique. The combined analysis of aeroelastic and probabilistic analysis is applied to a 12-bladed cascade vibrating in bending and torsion. Out of the total 11 design parameters, 6 are considered as having probabilistic variation. The six parameters are space-to-chord ratio (SBYC), stagger angle (GAMA), elastic axis (ELAXS), Mach number (MACH), mass ratio (MASSR), and frequency ratio (WHWB). The cascade is considered to be in subsonic flow with Mach 0.7. The results of the probabilistic aeroelastic analysis are the probability density function of predicted aerodynamic damping and frequency for flutter and the response amplitudes for forced response.

Probabilistic models of genetic variation in structured populations applied to global human studies.

PubMed

Hao, Wei; Song, Minsun; Storey, John D

2016-03-01

Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation. We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions. A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html jstorey@princeton.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Reliability and risk assessment of structures

NASA Technical Reports Server (NTRS)

Chamis, C. C.

1991-01-01

Development of reliability and risk assessment of structural components and structures is a major activity at Lewis Research Center. It consists of five program elements: (1) probabilistic loads; (2) probabilistic finite element analysis; (3) probabilistic material behavior; (4) assessment of reliability and risk; and (5) probabilistic structural performance evaluation. Recent progress includes: (1) the evaluation of the various uncertainties in terms of cumulative distribution functions for various structural response variables based on known or assumed uncertainties in primitive structural variables; (2) evaluation of the failure probability; (3) reliability and risk-cost assessment; and (4) an outline of an emerging approach for eventual certification of man-rated structures by computational methods. Collectively, the results demonstrate that the structural durability/reliability of man-rated structural components and structures can be effectively evaluated by using formal probabilistic methods.

Brain function during probabilistic learning in relation to IQ and level of education.

PubMed

van den Bos, Wouter; Crone, Eveline A; Güroğlu, Berna

2012-02-15

Knowing how to adapt your behavior based on feedback lies at the core of successful learning. We investigated the relation between brain function, grey matter volume, educational level and IQ in a Dutch adolescent sample. In total 45 healthy volunteers between ages 13 and 16 were recruited from schools for pre-vocational and pre-university education. For each individual, IQ was estimated using two subtests from the WISC-III-R (similarities and block design). While in the magnetic resonance imaging (MRI) scanner, participants performed a probabilistic learning task. Behavioral comparisons showed that participants with higher IQ used a more adaptive learning strategy after receiving positive feedback. Analysis of neural activation revealed that higher IQ was associated with increased activation in DLPFC and dACC when receiving positive feedback, specifically for rules with low reward probability (i.e., unexpected positive feedback). Furthermore, VBM analyses revealed that IQ correlated positively with grey matter volume within these regions. These results provide support for IQ-related individual differences in the developmental time courses of neural circuitry supporting feedback-based learning. Current findings are interpreted in terms of a prolonged window of flexibility and opportunity for adolescents with higher IQ scores. Copyright © 2011 Elsevier Ltd. All rights reserved.

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques

NASA Technical Reports Server (NTRS)

Hardy, Terry L.; Rapp, Douglas C.

1994-01-01

The reliability of rocket engine systems was analyzed by using probabilistic and fuzzy logic techniques. Fault trees were developed for integrated modular engine (IME) and discrete engine systems, and then were used with the two techniques to quantify reliability. The IRRAS (Integrated Reliability and Risk Analysis System) computer code, developed for the U.S. Nuclear Regulatory Commission, was used for the probabilistic analyses, and FUZZYFTA (Fuzzy Fault Tree Analysis), a code developed at NASA Lewis Research Center, was used for the fuzzy logic analyses. Although both techniques provided estimates of the reliability of the IME and discrete systems, probabilistic techniques emphasized uncertainty resulting from randomness in the system whereas fuzzy logic techniques emphasized uncertainty resulting from vagueness in the system. Because uncertainty can have both random and vague components, both techniques were found to be useful tools in the analysis of rocket engine system reliability.

Global assessment of predictability of water availability: A bivariate probabilistic Budyko analysis

NASA Astrophysics Data System (ADS)

Wang, Weiguang; Fu, Jianyu

2018-02-01

Estimating continental water availability is of great importance for water resources management, in terms of maintaining ecosystem integrity and sustaining society development. To more accurately quantify the predictability of water availability, on the basis of univariate probabilistic Budyko framework, a bivariate probabilistic Budyko approach was developed using copula-based joint distribution model for considering the dependence between parameter ω of Wang-Tang's equation and the Normalized Difference Vegetation Index (NDVI), and was applied globally. The results indicate the predictive performance in global water availability is conditional on the climatic condition. In comparison with simple univariate distribution, the bivariate one produces the lower interquartile range under the same global dataset, especially in the regions with higher NDVI values, highlighting the importance of developing the joint distribution by taking into account the dependence structure of parameter ω and NDVI, which can provide more accurate probabilistic evaluation of water availability.

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.

Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology

PubMed Central

Martin, Andrea E.

2016-01-01

I argue that cue integration, a psychophysiological mechanism from vision and multisensory perception, offers a computational linking hypothesis between psycholinguistic theory and neurobiological models of language. I propose that this mechanism, which incorporates probabilistic estimates of a cue's reliability, might function in language processing from the perception of a phoneme to the comprehension of a phrase structure. I briefly consider the implications of the cue integration hypothesis for an integrated theory of language that includes acquisition, production, dialogue and bilingualism, while grounding the hypothesis in canonical neural computation. PMID:26909051

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.

An overview of engineering concepts and current design algorithms for probabilistic structural analysis

NASA Technical Reports Server (NTRS)

Duffy, S. F.; Hu, J.; Hopkins, D. A.

1995-01-01

The article begins by examining the fundamentals of traditional deterministic design philosophy. The initial section outlines the concepts of failure criteria and limit state functions two traditional notions that are embedded in deterministic design philosophy. This is followed by a discussion regarding safety factors (a possible limit state function) and the common utilization of statistical concepts in deterministic engineering design approaches. Next the fundamental aspects of a probabilistic failure analysis are explored and it is shown that deterministic design concepts mentioned in the initial portion of the article are embedded in probabilistic design methods. For components fabricated from ceramic materials (and other similarly brittle materials) the probabilistic design approach yields the widely used Weibull analysis after suitable assumptions are incorporated. The authors point out that Weibull analysis provides the rare instance where closed form solutions are available for a probabilistic failure analysis. Since numerical methods are usually required to evaluate component reliabilities, a section on Monte Carlo methods is included to introduce the concept. The article concludes with a presentation of the technical aspects that support the numerical method known as fast probability integration (FPI). This includes a discussion of the Hasofer-Lind and Rackwitz-Fiessler approximations.

Probabilistic Assessment of Cancer Risk from Solar Particle Events

NASA Astrophysics Data System (ADS)

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

For long duration missions outside of the protection of the Earth's magnetic field, space radi-ation presents significant health risks including cancer mortality. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which include high energy protons and heavy ions. While the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle, the individual SPE occurrences themselves are random in nature. We es-timated the probability of SPE occurrence using a non-homogeneous Poisson model to fit the historical database of proton measurements. Distributions of particle fluences of SPEs for a specified mission period were simulated ranging from its 5th to 95th percentile to assess the cancer risk distribution. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. We estimated the overall cumulative probability of GCR environment for a specified mission period using a solar modulation model for the temporal characterization of the GCR environment represented by the deceleration po-tential (φ). Probabilistic assessment of cancer fatal risk was calculated for various periods of lunar and Mars missions. This probabilistic approach to risk assessment from space radiation is in support of mission design and operational planning for future manned space exploration missions. In future work, this probabilistic approach to the space radiation will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Probabilistic Assessment of Cancer Risk from Solar Particle Events

NASA Technical Reports Server (NTRS)

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

2010-01-01

For long duration missions outside of the protection of the Earth s magnetic field, space radiation presents significant health risks including cancer mortality. Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which include high energy protons and heavy ions. While the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle, the individual SPE occurrences themselves are random in nature. We estimated the probability of SPE occurrence using a non-homogeneous Poisson model to fit the historical database of proton measurements. Distributions of particle fluences of SPEs for a specified mission period were simulated ranging from its 5 th to 95th percentile to assess the cancer risk distribution. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. We estimated the overall cumulative probability of GCR environment for a specified mission period using a solar modulation model for the temporal characterization of the GCR environment represented by the deceleration potential (^). Probabilistic assessment of cancer fatal risk was calculated for various periods of lunar and Mars missions. This probabilistic approach to risk assessment from space radiation is in support of mission design and operational planning for future manned space exploration missions. In future work, this probabilistic approach to the space radiation will be combined with a probabilistic approach to the radiobiological factors that contribute to the uncertainties in projecting cancer risks.

Accurate reconstruction of viral quasispecies spectra through improved estimation of strain richness

PubMed Central

2015-01-01

Background Estimating the number of different species (richness) in a mixed microbial population has been a main focus in metagenomic research. Existing methods of species richness estimation ride on the assumption that the reads in each assembled contig correspond to only one of the microbial genomes in the population. This assumption and the underlying probabilistic formulations of existing methods are not useful for quasispecies populations where the strains are highly genetically related. The lack of knowledge on the number of different strains in a quasispecies population is observed to hinder the precision of existing Viral Quasispecies Spectrum Reconstruction (QSR) methods due to the uncontrolled reconstruction of a large number of in silico false positives. In this work, we formulated a novel probabilistic method for strain richness estimation specifically targeting viral quasispecies. By using this approach we improved our recently proposed spectrum reconstruction pipeline ViQuaS to achieve higher levels of precision in reconstructed quasispecies spectra without compromising the recall rates. We also discuss how one other existing popular QSR method named ShoRAH can be improved using this new approach. Results On benchmark data sets, our estimation method provided accurate richness estimates (< 0.2 median estimation error) and improved the precision of ViQuaS by 2%-13% and F-score by 1%-9% without compromising the recall rates. We also demonstrate that our estimation method can be used to improve the precision and F-score of ShoRAH by 0%-7% and 0%-5% respectively. Conclusions The proposed probabilistic estimation method can be used to estimate the richness of viral populations with a quasispecies behavior and to improve the accuracy of the quasispecies spectra reconstructed by the existing methods ViQuaS and ShoRAH in the presence of a moderate level of technical sequencing errors. Availability http://sourceforge.net/projects/viquas/ PMID:26678073

Use of Probabilistic Engineering Methods in the Detailed Design and Development Phases of the NASA Ares Launch Vehicle

NASA Technical Reports Server (NTRS)

Fayssal, Safie; Weldon, Danny

2008-01-01

The United States National Aeronautics and Space Administration (NASA) is in the midst of a space exploration program called Constellation to send crew and cargo to the international Space Station, to the moon, and beyond. As part of the Constellation program, a new launch vehicle, Ares I, is being developed by NASA Marshall Space Flight Center. Designing a launch vehicle with high reliability and increased safety requires a significant effort in understanding design variability and design uncertainty at the various levels of the design (system, element, subsystem, component, etc.) and throughout the various design phases (conceptual, preliminary design, etc.). In a previous paper [1] we discussed a probabilistic functional failure analysis approach intended mainly to support system requirements definition, system design, and element design during the early design phases. This paper provides an overview of the application of probabilistic engineering methods to support the detailed subsystem/component design and development as part of the "Design for Reliability and Safety" approach for the new Ares I Launch Vehicle. Specifically, the paper discusses probabilistic engineering design analysis cases that had major impact on the design and manufacturing of the Space Shuttle hardware. The cases represent important lessons learned from the Space Shuttle Program and clearly demonstrate the significance of probabilistic engineering analysis in better understanding design deficiencies and identifying potential design improvement for Ares I. The paper also discusses the probabilistic functional failure analysis approach applied during the early design phases of Ares I and the forward plans for probabilistic design analysis in the detailed design and development phases.

PROcEED: Probabilistic reverse dosimetry approaches for estimating exposure distributions

EPA Science Inventory

As increasing amounts of biomonitoring survey data become available, a new discipline focused on converting such data into estimates of chemical exposures has developed. Reverse dosimetry uses a pharmacokinetic model along with measured biomarker concentrations to determine the p...

CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential

USGS Publications Warehouse

Moss, R.E.S.; Seed, R.B.; Kayen, R.E.; Stewart, J.P.; Der Kiureghian, A.; Cetin, K.O.

2006-01-01

This paper presents a complete methodology for both probabilistic and deterministic assessment of seismic soil liquefaction triggering potential based on the cone penetration test (CPT). A comprehensive worldwide set of CPT-based liquefaction field case histories were compiled and back analyzed, and the data then used to develop probabilistic triggering correlations. Issues investigated in this study include improved normalization of CPT resistance measurements for the influence of effective overburden stress, and adjustment to CPT tip resistance for the potential influence of "thin" liquefiable layers. The effects of soil type and soil character (i.e., "fines" adjustment) for the new correlations are based on a combination of CPT tip and sleeve resistance. To quantify probability for performancebased engineering applications, Bayesian "regression" methods were used, and the uncertainties of all variables comprising both the seismic demand and the liquefaction resistance were estimated and included in the analysis. The resulting correlations were developed using a Bayesian framework and are presented in both probabilistic and deterministic formats. The results are compared to previous probabilistic and deterministic correlations. ?? 2006 ASCE.

Probabilistic and deterministic aspects of linear estimation in geodesy. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Dermanis, A.

1976-01-01

Recent advances in observational techniques related to geodetic work (VLBI, laser ranging) make it imperative that more consideration should be given to modeling problems. Uncertainties in the effect of atmospheric refraction, polar motion and precession-nutation parameters, cannot be dispensed with in the context of centimeter level geodesy. Even physical processes that have generally been previously altogether neglected (station motions) must now be taken into consideration. The problem of modeling functions of time or space, or at least their values at observation points (epochs) is explored. When the nature of the function to be modeled is unknown. The need to include a limited number of terms and to a priori decide upon a specific form may result in a representation which fails to sufficiently approximate the unknown function. An alternative approach of increasing application is the modeling of unknown functions as stochastic processes.

Probabilistic data integration and computational complexity

NASA Astrophysics Data System (ADS)

Hansen, T. M.; Cordua, K. S.; Mosegaard, K.

2016-12-01

Inverse problems in Earth Sciences typically refer to the problem of inferring information about properties of the Earth from observations of geophysical data (the result of nature's solution to the `forward' problem). This problem can be formulated more generally as a problem of `integration of information'. A probabilistic formulation of data integration is in principle simple: If all information available (from e.g. geology, geophysics, remote sensing, chemistry…) can be quantified probabilistically, then different algorithms exist that allow solving the data integration problem either through an analytical description of the combined probability function, or sampling the probability function. In practice however, probabilistic based data integration may not be easy to apply successfully. This may be related to the use of sampling methods, which are known to be computationally costly. But, another source of computational complexity is related to how the individual types of information are quantified. In one case a data integration problem is demonstrated where the goal is to determine the existence of buried channels in Denmark, based on multiple sources of geo-information. Due to one type of information being too informative (and hence conflicting), this leads to a difficult sampling problems with unrealistic uncertainty. Resolving this conflict prior to data integration, leads to an easy data integration problem, with no biases. In another case it is demonstrated how imperfections in the description of the geophysical forward model (related to solving the wave-equation) can lead to a difficult data integration problem, with severe bias in the results. If the modeling error is accounted for, the data integration problems becomes relatively easy, with no apparent biases. Both examples demonstrate that biased information can have a dramatic effect on the computational efficiency solving a data integration problem and lead to biased results, and under-estimation of uncertainty. However, in both examples, one can also analyze the performance of the sampling methods used to solve the data integration problem to indicate the existence of biased information. This can be used actively to avoid biases in the available information and subsequently in the final uncertainty evaluation.

Near-source mobile methane emission estimates using EPA Method33a and a novel probabilistic approach as a basis for leak quantification in urban areas

NASA Astrophysics Data System (ADS)

Albertson, J. D.

2015-12-01

Methane emissions from underground pipeline leaks remain an ongoing issue in the development of accurate methane emission inventories for the natural gas supply chain. Application of mobile methods during routine street surveys would help address this issue, but there are large uncertainties in current approaches. In this paper, we describe results from a series of near-source (< 30 m) controlled methane releases where an instrumented van was used to measure methane concentrations during both fixed location sampling and during mobile traverses immediately downwind of the source. The measurements were used to evaluate the application of EPA Method 33A for estimating methane emissions downwind of a source and also to test the application of a new probabilistic approach for estimating emission rates from mobile traverse data.

Software risk estimation and management techniques at JPL

NASA Technical Reports Server (NTRS)

Hihn, J.; Lum, K.

2002-01-01

In this talk we will discuss how uncertainty has been incorporated into the JPL software model, probabilistic-based estimates, and how risk is addressed, how cost risk is currently being explored via a variety of approaches, from traditional risk lists, to detailed WBS-based risk estimates to the Defect Detection and Prevention (DDP) tool.

Statistical inference for remote sensing-based estimates of net deforestation

Treesearch

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Ecohydrology of agroecosystems: probabilistic description of yield reduction risk under limited water availability

NASA Astrophysics Data System (ADS)

Vico, Giulia; Porporato, Amilcare

2013-04-01

Supplemental irrigation represents one of the main strategies to mitigate the effects of climate variability and stabilize yields. Irrigated agriculture currently provides 40% of food production and its relevance is expected to further increase in the near future, in face of the projected alterations of rainfall patterns and increase in food, fiber, and biofuel demand. Because of the significant investments and water requirements involved in irrigation, strategic choices are needed to preserve productivity and profitability, while maintaining a sustainable water management - a nontrivial task given the unpredictability of the rainfall forcing. To facilitate decision making under uncertainty, a widely applicable probabilistic framework is proposed. The occurrence of rainfall events and irrigation applications are linked probabilistically to crop development during the growing season and yields at harvest. Based on these linkages, the probability density function of yields and corresponding probability density function of required irrigation volumes, as well as the probability density function of yields under the most common case of limited water availability are obtained analytically, as a function of irrigation strategy, climate, soil and crop parameters. The full probabilistic description of the frequency of occurrence of yields and water requirements is a crucial tool for decision making under uncertainty, e.g., via expected utility analysis. Furthermore, the knowledge of the probability density function of yield allows us to quantify the yield reduction hydrologic risk. Two risk indices are defined and quantified: the long-term risk index, suitable for long-term irrigation strategy assessment and investment planning, and the real-time risk index, providing a rigorous probabilistic quantification of the emergence of drought conditions during a single growing season in an agricultural setting. Our approach employs relatively few parameters and is thus easily and broadly applicable to different crops and sites, under current and future climate scenarios. Hence, the proposed probabilistic framework provides a quantitative tool to assess the impact of irrigation strategy and water allocation on the risk of not meeting a certain target yield, thus guiding the optimal allocation of water resources for human and environmental needs.

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.

Probabilistic Modeling of High-Temperature Material Properties of a 5-Harness 0/90 Sylramic Fiber/ CVI-SiC/ MI-SiC Woven Composite

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.; Tong, Michael; Murthy, P. L. N.; Mital, Subodh

1998-01-01

An integrated probabilistic approach has been developed to assess composites for high temperature applications. This approach was used to determine thermal and mechanical properties and their probabilistic distributions of a 5-harness 0/90 Sylramic fiber/CVI-SiC/Mi-SiC woven Ceramic Matrix Composite (CMC) at high temperatures. The purpose of developing this approach was to generate quantitative probabilistic information on this CMC to help complete the evaluation for its potential application for HSCT combustor liner. This approach quantified the influences of uncertainties inherent in constituent properties called primitive variables on selected key response variables of the CMC at 2200 F. The quantitative information is presented in the form of Cumulative Density Functions (CDFs). Probability Density Functions (PDFS) and primitive variable sensitivities on response. Results indicate that the scatters in response variables were reduced by 30-50% when the uncertainties in the primitive variables, which showed the most influence, were reduced by 50%.

Reliability-Based Stability Analysis of Rock Slopes Using Numerical Analysis and Response Surface Method

NASA Astrophysics Data System (ADS)

Dadashzadeh, N.; Duzgun, H. S. B.; Yesiloglu-Gultekin, N.

2017-08-01

While advanced numerical techniques in slope stability analysis are successfully used in deterministic studies, they have so far found limited use in probabilistic analyses due to their high computation cost. The first-order reliability method (FORM) is one of the most efficient probabilistic techniques to perform probabilistic stability analysis by considering the associated uncertainties in the analysis parameters. However, it is not possible to directly use FORM in numerical slope stability evaluations as it requires definition of a limit state performance function. In this study, an integrated methodology for probabilistic numerical modeling of rock slope stability is proposed. The methodology is based on response surface method, where FORM is used to develop an explicit performance function from the results of numerical simulations. The implementation of the proposed methodology is performed by considering a large potential rock wedge in Sumela Monastery, Turkey. The accuracy of the developed performance function to truly represent the limit state surface is evaluated by monitoring the slope behavior. The calculated probability of failure is compared with Monte Carlo simulation (MCS) method. The proposed methodology is found to be 72% more efficient than MCS, while the accuracy is decreased with an error of 24%.

In search of a statistical probability model for petroleum-resource assessment : a critique of the probabilistic significance of certain concepts and methods used in petroleum-resource assessment : to that end, a probabilistic model is sketched

USGS Publications Warehouse

Grossling, Bernardo F.

1975-01-01

Exploratory drilling is still in incipient or youthful stages in those areas of the world where the bulk of the potential petroleum resources is yet to be discovered. Methods of assessing resources from projections based on historical production and reserve data are limited to mature areas. For most of the world's petroleum-prospective areas, a more speculative situation calls for a critical review of resource-assessment methodology. The language of mathematical statistics is required to define more rigorously the appraisal of petroleum resources. Basically, two approaches have been used to appraise the amounts of undiscovered mineral resources in a geologic province: (1) projection models, which use statistical data on the past outcome of exploration and development in the province; and (2) estimation models of the overall resources of the province, which use certain known parameters of the province together with the outcome of exploration and development in analogous provinces. These two approaches often lead to widely different estimates. Some of the controversy that arises results from a confusion of the probabilistic significance of the quantities yielded by each of the two approaches. Also, inherent limitations of analytic projection models-such as those using the logistic and Gomperts functions --have often been ignored. The resource-assessment problem should be recast in terms that provide for consideration of the probability of existence of the resource and of the probability of discovery of a deposit. Then the two above-mentioned models occupy the two ends of the probability range. The new approach accounts for (1) what can be expected with reasonably high certainty by mere projections of what has been accomplished in the past; (2) the inherent biases of decision-makers and resource estimators; (3) upper bounds that can be set up as goals for exploration; and (4) the uncertainties in geologic conditions in a search for minerals. Actual outcomes can then be viewed as phenomena subject to statistical uncertainty and responsive to changes in economic and technologic factors.

Probabilistic track coverage in cooperative sensor networks.

PubMed

Ferrari, Silvia; Zhang, Guoxian; Wettergren, Thomas A

2010-12-01

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Estimating the Rate of Occurrence of Renal Stones in Astronauts

NASA Technical Reports Server (NTRS)

Myers, J.; Goodenow, D.; Gokoglu, S.; Kassemi, M.

2016-01-01

Changes in urine chemistry, during and post flight, potentially increases the risk of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidence of renal stones in US astronauts exists and the question "How much does this risk change with space flight?" remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone presentation. The modeling process utilizes a Population Balance Equation based model developed in the companion IWS abstract by Kassemi et al. (2016) to evaluate the maximum growth and agglomeration potential from a specified set of urine chemistry values. Changes in renal stone occurrence rates are obtained from this model in a probabilistic simulation that interrogates the range of possible urine chemistries using Monte Carlo techniques. Subsequently, each randomly sampled urine chemistry undergoes speciation analysis using the well-established Joint Expert Speciation System (JESS) code to calculate critical values, such as ionic strength and relative supersaturation. The Kassemi model utilizes this information to predict the mean and maximum stone size. We close the assessment loop by using a transfer function that estimates the rate of stone formation from combining the relative supersaturation and both the mean and maximum free stone growth sizes. The transfer function is established by a simulation analysis which combines population stone formation rates and Poisson regression. Training this transfer function requires using the output of the aforementioned assessment steps with inputs from known non-stone-former and known stone-former urine chemistries. Established in a Monte Carlo system, the entire renal stone analysis model produces a probability distribution of the stone formation rate and an expected uncertainty in the estimate. The utility of this analysis will be demonstrated by showing the change in renal stone occurrence predicted by this method using urine chemistry distributions published in Whitson et al. 2009. A comparison to the model predictions to previous assessments of renal stone risk will be used to illustrate initial validation of the model.

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities.

PubMed

Gutiérrez, David; Ramírez-Moreno, Mauricio A

2016-04-01

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

PubMed

Fusar-Poli, P; Schultze-Lutter, F

2016-02-01

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Hierarchical Probabilistic Inference of the Color-Magnitude Diagram and Shrinkage of Stellar Distance Uncertainties

NASA Astrophysics Data System (ADS)

Leistedt, Boris; Hogg, David W.

2017-12-01

We present a hierarchical probabilistic model for improving geometric stellar distance estimates using color-magnitude information. This is achieved with a data-driven model of the color-magnitude diagram, not relying on stellar models but instead on the relative abundances of stars in color-magnitude cells, which are inferred from very noisy magnitudes and parallaxes. While the resulting noise-deconvolved color-magnitude diagram can be useful for a range of applications, we focus on deriving improved stellar distance estimates relying on both parallax and photometric information. We demonstrate the efficiency of this approach on the 1.4 million stars of the Gaia TGAS sample that also have AAVSO Photometric All Sky Survey magnitudes. Our hierarchical model has 4 million parameters in total, most of which are marginalized out numerically or analytically. We find that distance estimates are significantly improved for the noisiest parallaxes and densest regions of the color-magnitude diagram. In particular, the average distance signal-to-noise ratio (S/N) and uncertainty improve by 19% and 36%, respectively, with 8% of the objects improving in S/N by a factor greater than 2. This computationally efficient approach fully accounts for both parallax and photometric noise and is a first step toward a full hierarchical probabilistic model of the Gaia data.

A tesselated probabilistic representation for spatial robot perception and navigation

NASA Technical Reports Server (NTRS)

Elfes, Alberto

1989-01-01

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.

Reduced activation in ventral striatum and ventral tegmental area during probabilistic decision-making in schizophrenia.

PubMed

Rausch, Franziska; Mier, Daniela; Eifler, Sarah; Esslinger, Christine; Schilling, Claudia; Schirmbeck, Frederike; Englisch, Susanne; Meyer-Lindenberg, Andreas; Kirsch, Peter; Zink, Mathias

2014-07-01

Patients with schizophrenia suffer from deficits in monitoring and controlling their own thoughts. Within these so-called metacognitive impairments, alterations in probabilistic reasoning might be one cognitive phenomenon disposing to delusions. However, so far little is known about alterations in associated brain functionality. A previously established task for functional magnetic resonance imaging (fMRI), which requires a probabilistic decision after a variable amount of stimuli, was applied to 23 schizophrenia patients and 28 healthy controls matched for age, gender and educational levels. We compared activation patterns during decision-making under conditions of certainty versus uncertainty and evaluated the process of final decision-making in ventral striatum (VS) and ventral tegmental area (VTA). We replicated a pre-described extended cortical activation pattern during probabilistic reasoning. During final decision-making, activations in several fronto- and parietocortical areas, as well as in VS and VTA became apparent. In both of these regions schizophrenia patients showed a significantly reduced activation. These results further define the network underlying probabilistic decision-making. The observed hypo-activation in regions commonly associated with dopaminergic neurotransmission fits into current concepts of disrupted prediction error signaling in schizophrenia and suggests functional links to reward anticipation. Forthcoming studies with patients at risk for psychosis and drug-naive first episode patients are necessary to elucidate the development of these findings over time and the interplay with associated clinical symptoms. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Mobile sensing of point-source fugitive methane emissions using Bayesian inference: the determination of the likelihood function

NASA Astrophysics Data System (ADS)

Zhou, X.; Albertson, J. D.

2016-12-01

Natural gas is considered as a bridge fuel towards clean energy due to its potential lower greenhouse gas emission comparing with other fossil fuels. Despite numerous efforts, an efficient and cost-effective approach to monitor fugitive methane emissions along the natural gas production-supply chain has not been developed yet. Recently, mobile methane measurement has been introduced which applies a Bayesian approach to probabilistically infer methane emission rates and update estimates recursively when new measurements become available. However, the likelihood function, especially the error term which determines the shape of the estimate uncertainty, is not rigorously defined and evaluated with field data. To address this issue, we performed a series of near-source (< 30 m) controlled methane release experiments using a specialized vehicle mounted with fast response methane analyzers and a GPS unit. Methane concentrations were measured at two different heights along mobile traversals downwind of the sources, and concurrent wind and temperature data are recorded by nearby 3-D sonic anemometers. With known methane release rates, the measurements were used to determine the functional form and the parameterization of the likelihood function in the Bayesian inference scheme under different meteorological conditions.

A Probabilistic Collocation Based Iterative Kalman Filter for Landfill Data Assimilation

NASA Astrophysics Data System (ADS)

Qiang, Z.; Zeng, L.; Wu, L.

2016-12-01

Due to the strong spatial heterogeneity of landfill, uncertainty is ubiquitous in gas transport process in landfill. To accurately characterize the landfill properties, the ensemble Kalman filter (EnKF) has been employed to assimilate the measurements, e.g., the gas pressure. As a Monte Carlo (MC) based method, the EnKF usually requires a large ensemble size, which poses a high computational cost for large scale problems. In this work, we propose a probabilistic collocation based iterative Kalman filter (PCIKF) to estimate permeability in a liquid-gas coupling model. This method employs polynomial chaos expansion (PCE) to represent and propagate the uncertainties of model parameters and states, and an iterative form of Kalman filter to assimilate the current gas pressure data. To further reduce the computation cost, the functional ANOVA (analysis of variance) decomposition is conducted, and only the first order ANOVA components are remained for PCE. Illustrated with numerical case studies, this proposed method shows significant superiority in computation efficiency compared with the traditional MC based iterative EnKF. The developed method has promising potential in reliable prediction and management of landfill gas production.

Probabilistic-driven oriented Speckle reducing anisotropic diffusion with application to cardiac ultrasonic images.

PubMed

Vegas-Sanchez-Ferrero, G; Aja-Fernandez, S; Martin-Fernandez, M; Frangi, A F; Palencia, C

2010-01-01

A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.

Threatened species and the potential loss of phylogenetic diversity: conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis.

PubMed

Faith, Daniel P

2008-12-01

New species conservation strategies, including the EDGE of Existence (EDGE) program, have expanded threatened species assessments by integrating information about species' phylogenetic distinctiveness. Distinctiveness has been measured through simple scores that assign shared credit among species for evolutionary heritage represented by the deeper phylogenetic branches. A species with a high score combined with a high extinction probability receives high priority for conservation efforts. Simple hypothetical scenarios for phylogenetic trees and extinction probabilities demonstrate how such scoring approaches can provide inefficient priorities for conservation. An existing probabilistic framework derived from the phylogenetic diversity measure (PD) properly captures the idea of shared responsibility for the persistence of evolutionary history. It avoids static scores, takes into account the status of close relatives through their extinction probabilities, and allows for the necessary updating of priorities in light of changes in species threat status. A hypothetical phylogenetic tree illustrates how changes in extinction probabilities of one or more species translate into changes in expected PD. The probabilistic PD framework provided a range of strategies that moved beyond expected PD to better consider worst-case PD losses. In another example, risk aversion gave higher priority to a conservation program that provided a smaller, but less risky, gain in expected PD. The EDGE program could continue to promote a list of top species conservation priorities through application of probabilistic PD and simple estimates of current extinction probability. The list might be a dynamic one, with all the priority scores updated as extinction probabilities change. Results of recent studies suggest that estimation of extinction probabilities derived from the red list criteria linked to changes in species range sizes may provide estimated probabilities for many different species. Probabilistic PD provides a framework for single-species assessment that is well-integrated with a broader measurement of impacts on PD owing to climate change and other factors.

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

NASA Astrophysics Data System (ADS)

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Vega, J.; Dormido-Canto, S.; Baruzzo, M.; Gelfusa, M.; Contributors, JET

2018-05-01

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

Using Tranformation Group Priors and Maximum Relative Entropy for Bayesian Glaciological Inversions

NASA Astrophysics Data System (ADS)

Arthern, R. J.; Hindmarsh, R. C. A.; Williams, C. R.

2014-12-01

One of the key advances that has allowed better simulations of the large ice sheets of Greenland and Antarctica has been the use of inverse methods. These have allowed poorly known parameters such as the basal drag coefficient and ice viscosity to be constrained using a wide variety of satellite observations. Inverse methods used by glaciologists have broadly followed one of two related approaches. The first is minimization of a cost function that describes the misfit to the observations, often accompanied by some kind of explicit or implicit regularization that promotes smallness or smoothness in the inverted parameters. The second approach is a probabilistic framework that makes use of Bayes' theorem to update prior assumptions about the probability of parameters, making use of data with known error estimates. Both approaches have much in common and questions of regularization often map onto implicit choices of prior probabilities that are made explicit in the Bayesian framework. In both approaches questions can arise that seem to demand subjective input. What should the functional form of the cost function be if there are alternatives? What kind of regularization should be applied, and how much? How should the prior probability distribution for a parameter such as basal slipperiness be specified when we know so little about the details of the subglacial environment? Here we consider some approaches that have been used to address these questions and discuss ways that probabilistic prior information used for regularizing glaciological inversions might be specified with greater objectivity.

Probabilistic Tsunami Hazard Analysis

NASA Astrophysics Data System (ADS)

Thio, H. K.; Ichinose, G. A.; Somerville, P. G.; Polet, J.

2006-12-01

The recent tsunami disaster caused by the 2004 Sumatra-Andaman earthquake has focused our attention to the hazard posed by large earthquakes that occur under water, in particular subduction zone earthquakes, and the tsunamis that they generate. Even though these kinds of events are rare, the very large loss of life and material destruction caused by this earthquake warrant a significant effort towards the mitigation of the tsunami hazard. For ground motion hazard, Probabilistic Seismic Hazard Analysis (PSHA) has become a standard practice in the evaluation and mitigation of seismic hazard to populations in particular with respect to structures, infrastructure and lifelines. Its ability to condense the complexities and variability of seismic activity into a manageable set of parameters greatly facilitates the design of effective seismic resistant buildings but also the planning of infrastructure projects. Probabilistic Tsunami Hazard Analysis (PTHA) achieves the same goal for hazards posed by tsunami. There are great advantages of implementing such a method to evaluate the total risk (seismic and tsunami) to coastal communities. The method that we have developed is based on the traditional PSHA and therefore completely consistent with standard seismic practice. Because of the strong dependence of tsunami wave heights on bathymetry, we use a full waveform tsunami waveform computation in lieu of attenuation relations that are common in PSHA. By pre-computing and storing the tsunami waveforms at points along the coast generated for sets of subfaults that comprise larger earthquake faults, we can efficiently synthesize tsunami waveforms for any slip distribution on those faults by summing the individual subfault tsunami waveforms (weighted by their slip). This efficiency make it feasible to use Green's function summation in lieu of attenuation relations to provide very accurate estimates of tsunami height for probabilistic calculations, where one typically computes thousands of earthquake scenarios. We have carried out preliminary tsunami hazard calculations for different return periods for western North America and Hawaii based on thousands of earthquake scenarios around the Pacific rim and along the coast of North America. We will present tsunami hazard maps for several return periods and also discuss how to use these results for probabilistic inundation and runup mapping. Our knowledge of certain types of tsunami sources is very limited (e.g. submarine landslides), but a probabilistic framework for tsunami hazard evaluation can include even such sources and their uncertainties and present the overall hazard in a meaningful and consistent way.

The direct and indirect cost of diabetes in Italy: a prevalence probabilistic approach.

PubMed

Marcellusi, A; Viti, R; Mecozzi, A; Mennini, F S

2016-03-01

Diabetes mellitus is a chronic degenerative disease associated with a high risk of chronic complications and comorbidities. However, very few data are available on the associated cost. The objective of this study is to identify the available information on the epidemiology of the disease and estimate the average annual cost incurred by the National Health Service and Society for the Treatment of Diabetes in Italy. A probabilistic prevalence cost of illness model was developed to calculate an aggregate measure of the economic burden associated with the disease, in terms of direct medical costs (drugs, hospitalizations, monitoring and adverse events) and indirect costs (absenteeism and early retirement). A systematic review of the literature was conducted to determine both the epidemiological and economic data. Furthermore, a one-way and probabilistic sensitivity analysis with 5,000 Monte Carlo simulations was performed to test the robustness of the results and define a 95% CI. The model estimated a prevalence of 2.6 million patients under drug therapies in Italy. The total economic burden of diabetic patients in Italy amounted to €20.3 billion/year (95% CI €18.61 to €22.29 billion), 54% of which are associated with indirect costs (95% CI €10.10 to €11.62 billion) and 46% with direct costs only (95% CI €8.11 to €11.06 billion). This is the first study conducted in Italy aimed at estimating the direct and indirect cost of diabetes with a probabilistic prevalence approach. As might be expected, the lack of information means that the real burden of diabetes is partly underestimated, especially with regard to indirect costs. However, this is a useful approach for policy makers to understand the economic implications of diabetes treatment in Italy.

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

On the skill of various ensemble spread estimators for probabilistic short range wind forecasting

NASA Astrophysics Data System (ADS)

Kann, A.

2012-05-01

A variety of applications ranging from civil protection associated with severe weather to economical interests are heavily dependent on meteorological information. For example, a precise planning of the energy supply with a high share of renewables requires detailed meteorological information on high temporal and spatial resolution. With respect to wind power, detailed analyses and forecasts of wind speed are of crucial interest for the energy management. Although the applicability and the current skill of state-of-the-art probabilistic short range forecasts has increased during the last years, ensemble systems still show systematic deficiencies which limit its practical use. This paper presents methods to improve the ensemble skill of 10-m wind speed forecasts by combining deterministic information from a nowcasting system on very high horizontal resolution with uncertainty estimates from a limited area ensemble system. It is shown for a one month validation period that a statistical post-processing procedure (a modified non-homogeneous Gaussian regression) adds further skill to the probabilistic forecasts, especially beyond the nowcasting range after +6 h.

Multi-Scale/Multi-Functional Probabilistic Composite Fatigue

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A multi-level (multi-scale/multi-functional) evaluation is demonstrated by applying it to three different sample problems. These problems include the probabilistic evaluation of a space shuttle main engine blade, an engine rotor and an aircraft wing. The results demonstrate that the blade will fail at the highest probability path, the engine two-stage rotor will fail by fracture at the rim and the aircraft wing will fail at 109 fatigue cycles with a probability of 0.9967.

Monte Carlo simulation for slip rate sensitivity analysis in Cimandiri fault area

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

Pratama, Cecep, E-mail: great.pratama@gmail.com; Meilano, Irwan; Nugraha, Andri Dian

Slip rate is used to estimate earthquake recurrence relationship which is the most influence for hazard level. We examine slip rate contribution of Peak Ground Acceleration (PGA), in probabilistic seismic hazard maps (10% probability of exceedance in 50 years or 500 years return period). Hazard curve of PGA have been investigated for Sukabumi using a PSHA (Probabilistic Seismic Hazard Analysis). We observe that the most influence in the hazard estimate is crustal fault. Monte Carlo approach has been developed to assess the sensitivity. Then, Monte Carlo simulations properties have been assessed. Uncertainty and coefficient of variation from slip rate formore » Cimandiri Fault area has been calculated. We observe that seismic hazard estimates is sensitive to fault slip rate with seismic hazard uncertainty result about 0.25 g. For specific site, we found seismic hazard estimate for Sukabumi is between 0.4904 – 0.8465 g with uncertainty between 0.0847 – 0.2389 g and COV between 17.7% – 29.8%.« less

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…

Probabilistic Learning by Rodent Grid Cells

PubMed Central

Cheung, Allen

2016-01-01

Mounting evidence shows mammalian brains are probabilistic computers, but the specific cells involved remain elusive. Parallel research suggests that grid cells of the mammalian hippocampal formation are fundamental to spatial cognition but their diverse response properties still defy explanation. No plausible model exists which explains stable grids in darkness for twenty minutes or longer, despite being one of the first results ever published on grid cells. Similarly, no current explanation can tie together grid fragmentation and grid rescaling, which show very different forms of flexibility in grid responses when the environment is varied. Other properties such as attractor dynamics and grid anisotropy seem to be at odds with one another unless additional properties are assumed such as a varying velocity gain. Modelling efforts have largely ignored the breadth of response patterns, while also failing to account for the disastrous effects of sensory noise during spatial learning and recall, especially in darkness. Here, published electrophysiological evidence from a range of experiments are reinterpreted using a novel probabilistic learning model, which shows that grid cell responses are accurately predicted by a probabilistic learning process. Diverse response properties of probabilistic grid cells are statistically indistinguishable from rat grid cells across key manipulations. A simple coherent set of probabilistic computations explains stable grid fields in darkness, partial grid rescaling in resized arenas, low-dimensional attractor grid cell dynamics, and grid fragmentation in hairpin mazes. The same computations also reconcile oscillatory dynamics at the single cell level with attractor dynamics at the cell ensemble level. Additionally, a clear functional role for boundary cells is proposed for spatial learning. These findings provide a parsimonious and unified explanation of grid cell function, and implicate grid cells as an accessible neuronal population readout of a set of probabilistic spatial computations. PMID:27792723

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.

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.

Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff

NASA Astrophysics Data System (ADS)

Moncoulon, D.; Labat, D.; Ardon, J.; Onfroy, T.; Leblois, E.; Poulard, C.; Aji, S.; Rémy, A.; Quantin, A.

2013-07-01

The analysis of flood exposure at a national scale for the French insurance market must combine the generation of a probabilistic event set of all possible but not yet occurred flood situations with hazard and damage modeling. In this study, hazard and damage models are calibrated on a 1995-2012 historical event set, both for hazard results (river flow, flooded areas) and loss estimations. Thus, uncertainties in the deterministic estimation of a single event loss are known before simulating a probabilistic event set. To take into account at least 90% of the insured flood losses, the probabilistic event set must combine the river overflow (small and large catchments) with the surface runoff due to heavy rainfall, on the slopes of the watershed. Indeed, internal studies of CCR claim database has shown that approximately 45% of the insured flood losses are located inside the floodplains and 45% outside. 10% other percent are due to seasurge floods and groundwater rise. In this approach, two independent probabilistic methods are combined to create a single flood loss distribution: generation of fictive river flows based on the historical records of the river gauge network and generation of fictive rain fields on small catchments, calibrated on the 1958-2010 Météo-France rain database SAFRAN. All the events in the probabilistic event sets are simulated with the deterministic model. This hazard and damage distribution is used to simulate the flood losses at the national scale for an insurance company (MACIF) and to generate flood areas associated with hazard return periods. The flood maps concern river overflow and surface water runoff. Validation of these maps is conducted by comparison with the address located claim data on a small catchment (downstream Argens).

Incorporating psychological influences in probabilistic cost analysis

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

Kujawski, Edouard; Alvaro, Mariana; Edwards, William

2004-01-08

Today's typical probabilistic cost analysis assumes an ''ideal'' project that is devoid of the human and organizational considerations that heavily influence the success and cost of real-world projects. In the real world ''Money Allocated Is Money Spent'' (MAIMS principle); cost underruns are rarely available to protect against cost overruns while task overruns are passed on to the total project cost. Realistic cost estimates therefore require a modified probabilistic cost analysis that simultaneously models the cost management strategy including budget allocation. Psychological influences such as overconfidence in assessing uncertainties and dependencies among cost elements and risks are other important considerations thatmore » are generally not addressed. It should then be no surprise that actual project costs often exceed the initial estimates and are delivered late and/or with a reduced scope. This paper presents a practical probabilistic cost analysis model that incorporates recent findings in human behavior and judgment under uncertainty, dependencies among cost elements, the MAIMS principle, and project management practices. Uncertain cost elements are elicited from experts using the direct fractile assessment method and fitted with three-parameter Weibull distributions. The full correlation matrix is specified in terms of two parameters that characterize correlations among cost elements in the same and in different subsystems. The analysis is readily implemented using standard Monte Carlo simulation tools such as {at}Risk and Crystal Ball{reg_sign}. The analysis of a representative design and engineering project substantiates that today's typical probabilistic cost analysis is likely to severely underestimate project cost for probability of success values of importance to contractors and procuring activities. The proposed approach provides a framework for developing a viable cost management strategy for allocating baseline budgets and contingencies. Given the scope and magnitude of the cost-overrun problem, the benefits are likely to be significant.« less

Ranking of sabotage/tampering avoidance technology alternatives

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

Andrews, W.B.; Tabatabai, A.S.; Powers, T.B.

1986-01-01

Pacific Northwest Laboratory conducted a study to evaluate alternatives to the design and operation of nuclear power plants, emphasizing a reduction of their vulnerability to sabotage. Estimates of core melt accident frequency during normal operations and from sabotage/tampering events were used to rank the alternatives. Core melt frequency for normal operations was estimated using sensitivity analysis of results of probabilistic risk assessments. Core melt frequency for sabotage/tampering was estimated by developing a model based on probabilistic risk analyses, historic data, engineering judgment, and safeguards analyses of plant locations where core melt events could be initiated. Results indicate the most effectivemore » alternatives focus on large areas of the plant, increase safety system redundancy, and reduce reliance on single locations for mitigation of transients. Less effective options focus on specific areas of the plant, reduce reliance on some plant areas for safe shutdown, and focus on less vulnerable targets.« less

Estimating uncertainties in complex joint inverse problems

NASA Astrophysics Data System (ADS)

Afonso, Juan Carlos

2016-04-01

Sources of uncertainty affecting geophysical inversions can be classified either as reflective (i.e. the practitioner is aware of her/his ignorance) or non-reflective (i.e. the practitioner does not know that she/he does not know!). Although we should be always conscious of the latter, the former are the ones that, in principle, can be estimated either empirically (by making measurements or collecting data) or subjectively (based on the experience of the researchers). For complex parameter estimation problems in geophysics, subjective estimation of uncertainty is the most common type. In this context, probabilistic (aka Bayesian) methods are commonly claimed to offer a natural and realistic platform from which to estimate model uncertainties. This is because in the Bayesian approach, errors (whatever their nature) can be naturally included as part of the global statistical model, the solution of which represents the actual solution to the inverse problem. However, although we agree that probabilistic inversion methods are the most powerful tool for uncertainty estimation, the common claim that they produce "realistic" or "representative" uncertainties is not always justified. Typically, ALL UNCERTAINTY ESTIMATES ARE MODEL DEPENDENT, and therefore, besides a thorough characterization of experimental uncertainties, particular care must be paid to the uncertainty arising from model errors and input uncertainties. We recall here two quotes by G. Box and M. Gunzburger, respectively, of special significance for inversion practitioners and for this session: "…all models are wrong, but some are useful" and "computational results are believed by no one, except the person who wrote the code". In this presentation I will discuss and present examples of some problems associated with the estimation and quantification of uncertainties in complex multi-observable probabilistic inversions, and how to address them. Although the emphasis will be on sources of uncertainty related to the forward and statistical models, I will also address other uncertainties associated with data and uncertainty propagation.

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.

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.

Simulation of probabilistic wind loads and building analysis

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Chamis, Christos C.

1991-01-01

Probabilistic wind loads likely to occur on a structure during its design life are predicted. Described here is a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and reliability against overturning were obtained using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were qualified in terms of their respective sensitivities on the structural response.

Assessment of undiscovered sandstone copper deposits of the Kodar-Udokan area, Russia: Chapter M in Global mineral resource assessment

USGS Publications Warehouse

Zientek, Michael L.; Chechetkin, Vladimir S.; Parks, Heather L.; Box, Stephen E.; Briggs, Deborah A.; Cossette, Pamela M.; Dolgopolova, Alla; Hayes, Timothy S.; Seltmann, Reimar; Syusyura, Boris; Taylor, Cliff D.; Wintzer, Niki E.

2014-01-01

This probabilistic assessment indicates that a significant amount of undiscovered copper is associated with sediment-hosted stratabound copper deposits in the Kodar-Udokan Trough. In the assessment, a mean of 21 undiscovered deposits is estimated to occur within the Kodar-Udokan area. There are two known deposits in the area that contain drill-identified resources of 19.6 million metric tons of copper. Using Monte Carlo simulation, probabilistic estimates of the numbers of undiscovered sandstone copper deposits for these tracts were combined with tonnage and grade distributions of sandstone copper deposits to forecast an arithmetic mean of 20.6 million metric tons of undiscovered copper. Significant value can be expected from associated metals, particularly silver.

Active marks structure optimization for optical-electronic systems of spatial position control of industrial objects

NASA Astrophysics Data System (ADS)

Sycheva, Elena A.; Vasilev, Aleksandr S.; Lashmanov, Oleg U.; Korotaev, Valery V.

2017-06-01

The article is devoted to the optimization of optoelectronic systems of the spatial position of objects. Probabilistic characteristics of the detection of an active structured mark on a random noisy background are investigated. The developed computer model and the results of the study allow us to estimate the probabilistic characteristics of detection of a complex structured mark on a random gradient background, and estimate the error of spatial coordinates. The results of the study make it possible to improve the accuracy of measuring the coordinates of the object. Based on the research recommendations are given on the choice of parameters of the optimal mark structure for use in opticalelectronic systems for monitoring the spatial position of large-sized structures.

The Importance of Calibration in Clinical Psychology.

PubMed

Lindhiem, Oliver; Petersen, Isaac T; Mentch, Lucas K; Youngstrom, Eric A

2018-02-01

Accuracy has several elements, not all of which have received equal attention in the field of clinical psychology. Calibration, the degree to which a probabilistic estimate of an event reflects the true underlying probability of the event, has largely been neglected in the field of clinical psychology in favor of other components of accuracy such as discrimination (e.g., sensitivity, specificity, area under the receiver operating characteristic curve). Although it is frequently overlooked, calibration is a critical component of accuracy with particular relevance for prognostic models and risk-assessment tools. With advances in personalized medicine and the increasing use of probabilistic (0% to 100%) estimates and predictions in mental health research, the need for careful attention to calibration has become increasingly important.

Probabilistic dose-response modeling: case study using dichloromethane PBPK model results.

PubMed

Marino, Dale J; Starr, Thomas B

2007-12-01

A revised assessment of dichloromethane (DCM) has recently been reported that examines the influence of human genetic polymorphisms on cancer risks using deterministic PBPK and dose-response modeling in the mouse combined with probabilistic PBPK modeling in humans. This assessment utilized Bayesian techniques to optimize kinetic variables in mice and humans with mean values from posterior distributions used in the deterministic modeling in the mouse. To supplement this research, a case study was undertaken to examine the potential impact of probabilistic rather than deterministic PBPK and dose-response modeling in mice on subsequent unit risk factor (URF) determinations. Four separate PBPK cases were examined based on the exposure regimen of the NTP DCM bioassay. These were (a) Same Mouse (single draw of all PBPK inputs for both treatment groups); (b) Correlated BW-Same Inputs (single draw of all PBPK inputs for both treatment groups except for bodyweights (BWs), which were entered as correlated variables); (c) Correlated BW-Different Inputs (separate draws of all PBPK inputs for both treatment groups except that BWs were entered as correlated variables); and (d) Different Mouse (separate draws of all PBPK inputs for both treatment groups). Monte Carlo PBPK inputs reflect posterior distributions from Bayesian calibration in the mouse that had been previously reported. A minimum of 12,500 PBPK iterations were undertaken, in which dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day for lung and liver were determined. For dose-response modeling, these metrics were combined with NTP tumor incidence data that were randomly selected from binomial distributions. Resultant potency factors (0.1/ED(10)) were coupled with probabilistic PBPK modeling in humans that incorporated genetic polymorphisms to derive URFs. Results show that there was relatively little difference, i.e., <10% in central tendency and upper percentile URFs, regardless of the case evaluated. Independent draws of PBPK inputs resulted in the slightly higher URFs. Results were also comparable to corresponding values from the previously reported deterministic mouse PBPK and dose-response modeling approach that used LED(10)s to derive potency factors. This finding indicated that the adjustment from ED(10) to LED(10) in the deterministic approach for DCM compensated for variability resulting from probabilistic PBPK and dose-response modeling in the mouse. Finally, results show a similar degree of variability in DCM risk estimates from a number of different sources including the current effort even though these estimates were developed using very different techniques. Given the variety of different approaches involved, 95th percentile-to-mean risk estimate ratios of 2.1-4.1 represent reasonable bounds on variability estimates regarding probabilistic assessments of DCM.

Lexical Frequency Profiles and Zipf's Law

ERIC Educational Resources Information Center

Edwards, Roderick; Collins, Laura

2011-01-01

Laufer and Nation (1995) proposed that the Lexical Frequency Profile (LFP) can estimate the size of a second-language writer's productive vocabulary. Meara (2005) questioned the sensitivity and the reliability of LFPs for estimating vocabulary sizes, based on the results obtained from probabilistic simulations of LFPs. However, the underlying…

Uncertainty squared: Choosing among multiple input probability distributions and interpreting multiple output probability distributions in Monte Carlo climate risk models

NASA Astrophysics Data System (ADS)

Baer, P.; Mastrandrea, M.

2006-12-01

Simple probabilistic models which attempt to estimate likely transient temperature change from specified CO2 emissions scenarios must make assumptions about at least six uncertain aspects of the causal chain between emissions and temperature: current radiative forcing (including but not limited to aerosols), current land use emissions, carbon sinks, future non-CO2 forcing, ocean heat uptake, and climate sensitivity. Of these, multiple PDFs (probability density functions) have been published for the climate sensitivity, a couple for current forcing and ocean heat uptake, one for future non-CO2 forcing, and none for current land use emissions or carbon cycle uncertainty (which are interdependent). Different assumptions about these parameters, as well as different model structures, will lead to different estimates of likely temperature increase from the same emissions pathway. Thus policymakers will be faced with a range of temperature probability distributions for the same emissions scenarios, each described by a central tendency and spread. Because our conventional understanding of uncertainty and probability requires that a probabilistically defined variable of interest have only a single mean (or median, or modal) value and a well-defined spread, this "multidimensional" uncertainty defies straightforward utilization in policymaking. We suggest that there are no simple solutions to the questions raised. Crucially, we must dispel the notion that there is a "true" probability probabilities of this type are necessarily subjective, and reasonable people may disagree. Indeed, we suggest that what is at stake is precisely the question, what is it reasonable to believe, and to act as if we believe? As a preliminary suggestion, we demonstrate how the output of a simple probabilistic climate model might be evaluated regarding the reasonableness of the outputs it calculates with different input PDFs. We suggest further that where there is insufficient evidence to clearly favor one range of probabilistic projections over another, that the choice of results on which to base policy must necessarily involve ethical considerations, as they have inevitable consequences for the distribution of risk In particular, the choice to use a more "optimistic" PDF for climate sensitivity (or other components of the causal chain) leads to the allowance of higher emissions consistent with any specified goal for risk reduction, and thus leads to higher climate impacts, in exchange for lower mitigation costs.

Traits Without Borders: Integrating Functional Diversity Across Scales.

PubMed

Carmona, Carlos P; de Bello, Francesco; Mason, Norman W H; Lepš, Jan

2016-05-01

Owing to the conceptual complexity of functional diversity (FD), a multitude of different methods are available for measuring it, with most being operational at only a small range of spatial scales. This causes uncertainty in ecological interpretations and limits the potential to generalize findings across studies or compare patterns across scales. We solve this problem by providing a unified framework expanding on and integrating existing approaches. The framework, based on trait probability density (TPD), is the first to fully implement the Hutchinsonian concept of the niche as a probabilistic hypervolume in estimating FD. This novel approach could revolutionize FD-based research by allowing quantification of the various FD components from organismal to macroecological scales, and allowing seamless transitions between scales. Copyright © 2016 Elsevier Ltd. All rights reserved.

Data Analysis Recipes: Using Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Hogg, David W.; Foreman-Mackey, Daniel

2018-05-01

Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used. .

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.

Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging.

PubMed

Anbeek, Petronella; Vincken, Koen L; Groenendaal, Floris; Koeman, Annemieke; van Osch, Matthias J P; van der Grond, Jeroen

2008-02-01

A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal fluid (CSF). The segmentation algorithm is based on information from T2-weighted (T2-w) and inversion recovery (IR) scans. The method uses a K nearest neighbor (KNN) classification technique with features derived from spatial information and voxel intensities. Probabilistic segmentations of each tissue type were generated. By applying thresholds on these probability maps, binary segmentations were obtained. These final segmentations were evaluated by comparison with a gold standard. The sensitivity, specificity, and Dice similarity index (SI) were calculated for quantitative validation of the results. High sensitivity and specificity with respect to the gold standard were reached: sensitivity >0.82 and specificity >0.9 for all tissue types. Tissue volumes were calculated from the binary and probabilistic segmentations. The probabilistic segmentation volumes of all tissue types accurately estimated the gold standard volumes. The KNN approach offers valuable ways for neonatal brain segmentation. The probabilistic outcomes provide a useful tool for accurate volume measurements. The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.

What is the correct cost functional for variational data assimilation?

NASA Astrophysics Data System (ADS)

Bröcker, Jochen

2018-03-01

Variational approaches to data assimilation, and weakly constrained four dimensional variation (WC-4DVar) in particular, are important in the geosciences but also in other communities (often under different names). The cost functions and the resulting optimal trajectories may have a probabilistic interpretation, for instance by linking data assimilation with maximum aposteriori (MAP) estimation. This is possible in particular if the unknown trajectory is modelled as the solution of a stochastic differential equation (SDE), as is increasingly the case in weather forecasting and climate modelling. In this situation, the MAP estimator (or "most probable path" of the SDE) is obtained by minimising the Onsager-Machlup functional. Although this fact is well known, there seems to be some confusion in the literature, with the energy (or "least squares") functional sometimes been claimed to yield the most probable path. The first aim of this paper is to address this confusion and show that the energy functional does not, in general, provide the most probable path. The second aim is to discuss the implications in practice. Although the mentioned results pertain to stochastic models in continuous time, they do have consequences in practice where SDE's are approximated by discrete time schemes. It turns out that using an approximation to the SDE and calculating its most probable path does not necessarily yield a good approximation to the most probable path of the SDE proper. This suggest that even in discrete time, a version of the Onsager-Machlup functional should be used, rather than the energy functional, at least if the solution is to be interpreted as a MAP estimator.

A probabilistic estimate of maximum acceleration in rock in the contiguous United States

USGS Publications Warehouse

Algermissen, Sylvester Theodore; Perkins, David M.

1976-01-01

This paper presents a probabilistic estimate of the maximum ground acceleration to be expected from earthquakes occurring in the contiguous United States. It is based primarily upon the historic seismic record which ranges from very incomplete before 1930 to moderately complete after 1960. Geologic data, primarily distribution of faults, have been employed only to a minor extent, because most such data have not been interpreted yet with earthquake hazard evaluation in mind.The map provides a preliminary estimate of the relative hazard in various parts of the country. The report provides a method for evaluating the relative importance of the many parameters and assumptions in hazard analysis. The map and methods of evaluation described reflect the current state of understanding and are intended to be useful for engineering purposes in reducing the effects of earthquakes on buildings and other structures.Studies are underway on improved methods for evaluating the relativ( earthquake hazard of different regions. Comments on this paper are invited to help guide future research and revisions of the accompanying map.The earthquake hazard in the United States has been estimated in a variety of ways since the initial effort by Ulrich (see Roberts and Ulrich, 1950). In general, the earlier maps provided an estimate of the severity of ground shaking or damage but the frequency of occurrence of the shaking or damage was not given. Ulrich's map showed the distribution of expected damage in terms of no damage (zone 0), minor damage (zone 1), moderate damage (zone 2), and major damage (zone 3). The zones were not defined further and the frequency of occurrence of damage was not suggested. Richter (1959) and Algermissen (1969) estimated the ground motion in terms of maximum Modified Mercalli intensity. Richter used the terms "occasional" and "frequent" to characterize intensity IX shaking and Algermissen included recurrence curves for various parts of the country in the paper accompanying his map.The first probabilistic hazard maps covering portions of the United States were by Milne and Davenport (1969a). Recently, Wiggins, Hirshberg and Bronowicki (1974) prepared a probabilistic map of maximum particle velocity and Modified Mercalli intensity for the entire United States. The maps are based on an analysis of the historical seismicity. In general, geological data were not incorporated into the development of the maps.

Application of experimental design in geothermal resources assessment of Ciwidey-Patuha, West Java, Indonesia

NASA Astrophysics Data System (ADS)

Ashat, Ali; Pratama, Heru Berian

2017-12-01

The successful Ciwidey-Patuha geothermal field size assessment required integration data analysis of all aspects to determined optimum capacity to be installed. Resources assessment involve significant uncertainty of subsurface information and multiple development scenarios from these field. Therefore, this paper applied the application of experimental design approach to the geothermal numerical simulation of Ciwidey-Patuha to generate probabilistic resource assessment result. This process assesses the impact of evaluated parameters affecting resources and interacting between these parameters. This methodology have been successfully estimated the maximum resources with polynomial function covering the entire range of possible values of important reservoir parameters.

Reliability, Risk and Cost Trade-Offs for Composite Designs

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Singhal, Surendra N.; Chamis, Christos C.

1996-01-01

Risk and cost trade-offs have been simulated using a probabilistic method. The probabilistic method accounts for all naturally-occurring uncertainties including those in constituent material properties, fabrication variables, structure geometry and loading conditions. The probability density function of first buckling load for a set of uncertain variables is computed. The probabilistic sensitivity factors of uncertain variables to the first buckling load is calculated. The reliability-based cost for a composite fuselage panel is defined and minimized with respect to requisite design parameters. The optimization is achieved by solving a system of nonlinear algebraic equations whose coefficients are functions of probabilistic sensitivity factors. With optimum design parameters such as the mean and coefficient of variation (representing range of scatter) of uncertain variables, the most efficient and economical manufacturing procedure can be selected. In this paper, optimum values of the requisite design parameters for a predetermined cost due to failure occurrence are computationally determined. The results for the fuselage panel analysis show that the higher the cost due to failure occurrence, the smaller the optimum coefficient of variation of fiber modulus (design parameter) in longitudinal direction.

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

A Meta-Analysis of Children's Object-to-Mouth Frequency Data for Estimating Non-Dietary Ingestion Exposure

EPA Science Inventory

To improve estimates of non-dietary ingestion in probabilistic exposure modeling, a meta-analysis of children's object-to-mouth frequency was conducted using data from seven available studies representing 438 participants and ~ 1500 h of behavior observation. The analysis repres...

Bayesian Monte Carlo and Maximum Likelihood Approach for Uncertainty Estimation and Risk Management: Application to Lake Oxygen Recovery Model

EPA Science Inventory

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...

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...

Monte Carlo Approach for Reliability Estimations in Generalizability Studies.

ERIC Educational Resources Information Center

Dimitrov, Dimiter M.

A Monte Carlo approach is proposed, using the Statistical Analysis System (SAS) programming language, for estimating reliability coefficients in generalizability theory studies. Test scores are generated by a probabilistic model that considers the probability for a person with a given ability score to answer an item with a given difficulty…

Specifying and Refining a Complex Measurement Model.

ERIC Educational Resources Information Center

Levy, Roy; Mislevy, Robert J.

This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines…

A Brain Network Processing the Age of Faces

PubMed Central

Homola, György A.; Jbabdi, Saad; Beckmann, Christian F.; Bartsch, Andreas J.

2012-01-01

Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke's understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships. PMID:23185334

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.

A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

PubMed Central

Long, Chengjiang; Hua, Gang; Kapoor, Ashish

2015-01-01

We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. PMID:26924892

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.

Probabilistic evaluation of on-line checks in fault-tolerant multiprocessor systems

NASA Technical Reports Server (NTRS)

Nair, V. S. S.; Hoskote, Yatin V.; Abraham, Jacob A.

1992-01-01

The analysis of fault-tolerant multiprocessor systems that use concurrent error detection (CED) schemes is much more difficult than the analysis of conventional fault-tolerant architectures. Various analytical techniques have been proposed to evaluate CED schemes deterministically. However, these approaches are based on worst-case assumptions related to the failure of system components. Often, the evaluation results do not reflect the actual fault tolerance capabilities of the system. A probabilistic approach to evaluate the fault detecting and locating capabilities of on-line checks in a system is developed. The various probabilities associated with the checking schemes are identified and used in the framework of the matrix-based model. Based on these probabilistic matrices, estimates for the fault tolerance capabilities of various systems are derived analytically.

Non-Gaussian probabilistic MEG source localisation based on kernel density estimation☆

PubMed Central

Mohseni, Hamid R.; Kringelbach, Morten L.; Woolrich, Mark W.; Baker, Adam; Aziz, Tipu Z.; Probert-Smith, Penny

2014-01-01

There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. PMID:24055702

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

NASA Astrophysics Data System (ADS)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

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.

Multisensory decisions provide support for probabilistic number representations.

PubMed

Kanitscheider, Ingmar; Brown, Amanda; Pouget, Alexandre; Churchland, Anne K

2015-06-01

A large body of evidence suggests that an approximate number sense allows humans to estimate numerosity in sensory scenes. This ability is widely observed in humans, including those without formal mathematical training. Despite this, many outstanding questions remain about the nature of the numerosity representation in the brain. Specifically, it is not known whether approximate numbers are represented as scalar estimates of numerosity or, alternatively, as probability distributions over numerosity. In the present study, we used a multisensory decision task to distinguish these possibilities. We trained human subjects to decide whether a test stimulus had a larger or smaller numerosity compared with a fixed reference. Depending on the trial, the numerosity was presented as either a sequence of visual flashes or a sequence of auditory tones, or both. To test for a probabilistic representation, we varied the reliability of the stimulus by adding noise to the visual stimuli. In accordance with a probabilistic representation, we observed a significant improvement in multisensory compared with unisensory trials. Furthermore, a trial-by-trial analysis revealed that although individual subjects showed strategic differences in how they leveraged auditory and visual information, all subjects exploited the reliability of unisensory cues. An alternative, nonprobabilistic model, in which subjects combined cues without regard for reliability, was not able to account for these trial-by-trial choices. These findings provide evidence that the brain relies on a probabilistic representation for numerosity decisions. Copyright © 2015 the American Physiological Society.

Web-GIS platform for forest fire danger prediction in Ukraine: prospects of RS technologies

NASA Astrophysics Data System (ADS)

Baranovskiy, N. V.; Zharikova, M. V.

2016-10-01

There are many different statistical and empirical methods of forest fire danger use at present time. All systems have not physical basis. Last decade deterministic-probabilistic method is rapidly developed in Tomsk Polytechnic University. Forest sites classification is one way to estimate forest fire danger. We used this method in present work. Forest fire danger estimation depends on forest vegetation condition, forest fire retrospective, precipitation and air temperature. In fact, we use modified Nesterov Criterion. Lightning activity is under consideration as a high temperature source in present work. We use Web-GIS platform for program realization of this method. The program realization of the fire danger assessment system is the Web-oriented geoinformation system developed by the Django platform in the programming language Python. The GeoDjango framework was used for realization of cartographic functions. We suggest using of Terra/Aqua MODIS products for hot spot monitoring. Typical territory for forest fire danger estimation is Proletarskoe forestry of Kherson region (Ukraine).

Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

NASA Astrophysics Data System (ADS)

Maiti, Saumen; Tiwari, Ram Krishna

2010-10-01

A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho-section of the KTB. The comparisons of maximum a posteriori geological sections constructed here, based on the maximum a posteriori probability distribution, with the available geological information and the existing geophysical findings suggest that the BNN results reveal some additional finer details in the KTB borehole data at certain depths, which appears to be of some geological significance. We also demonstrate that the proposed BNN approach is superior to the conventional artificial neural network in terms of both avoiding "over-fitting" and aiding uncertainty estimation, which are vital for meaningful interpretation of geophysical records. Our analyses demonstrate that the BNN-based approach renders a robust means for the classification of complex changes in the litho-facies successions and thus could provide a useful guide for understanding the crustal inhomogeneity and the structural discontinuity in many other tectonically complex regions.

Probabilistic Determination of Green Infrastructure Pollutant Removal Rates from the International Stormwater BMP Database

NASA Astrophysics Data System (ADS)

Gilliom, R.; Hogue, T. S.; McCray, J. E.

2017-12-01

There is a need for improved parameterization of stormwater best management practices (BMP) performance estimates to improve modeling of urban hydrology, planning and design of green infrastructure projects, and water quality crediting for stormwater management. Percent removal is commonly used to estimate BMP pollutant removal efficiency, but there is general agreement that this approach has significant uncertainties and is easily affected by site-specific factors. Additionally, some fraction of monitored BMPs have negative percent removal, so it is important to understand the probability that a BMP will provide the desired water quality function versus exacerbating water quality problems. The widely used k-C* equation has shown to provide a more adaptable and accurate method to model BMP contaminant attenuation, and previous work has begun to evaluate the strengths and weaknesses of the k-C* method. However, no systematic method exists for obtaining first-order removal rate constants needed to use the k-C* equation for stormwater BMPs; thus there is minimal application of the method. The current research analyzes existing water quality data in the International Stormwater BMP Database to provide screening-level parameterization of the k-C* equation for selected BMP types and analysis of factors that skew the distribution of efficiency estimates from the database. Results illustrate that while certain BMPs are more likely to provide desired contaminant removal than others, site- and design-specific factors strongly influence performance. For example, bioretention systems show both the highest and lowest removal rates of dissolved copper, total phosphorous, and total nitrogen. Exploration and discussion of this and other findings will inform the application of the probabilistic pollutant removal rate constants. Though data limitations exist, this research will facilitate improved accuracy of BMP modeling and ultimately aid decision-making for stormwater quality management in urban systems.

Estimates of Dietary Exposure to Bisphenol A (BPA) from Light Metal Packaging using Food Consumption and Packaging usage Data: A Refined Deterministic Approach and a Fully Probabilistic (FACET) Approach

PubMed Central

Oldring, P.K.T.; Castle, L.; O'Mahony, C.; Dixon, J.

2013-01-01

The FACET tool is a probabilistic model to estimate exposure to chemicals in foodstuffs, originating from flavours, additives and food contact materials. This paper demonstrates the use of the FACET tool to estimate exposure to BPA (bisphenol A) from light metal packaging. For exposure to migrants from food packaging, FACET uses industry-supplied data on the occurrence of substances in the packaging, their concentrations and construction of the packaging, which were combined with data from a market research organisation and food consumption data supplied by national database managers. To illustrate the principles, UK packaging data were used together with consumption data from the UK National Diet and Nutrition Survey (NDNS) dietary survey for 19–64 year olds for a refined deterministic verification. The UK data were chosen mainly because the consumption surveys are detailed, data for UK packaging at a detailed level were available and, arguably, the UK population is composed of high consumers of packaged foodstuffs. Exposures were run for each food category that could give rise to BPA from light metal packaging. Consumer loyalty to a particular type of packaging, commonly referred to as packaging loyalty, was set. The BPA extraction levels used for the 15 types of coating chemistries that could release BPA were in the range of 0.00005–0.012 mg dm−2. The estimates of exposure to BPA using FACET for the total diet were 0.0098 (mean) and 0.0466 (97.5th percentile) mg/person/day, corresponding to 0.00013 (mean) and 0.00059 (97.5th percentile) mg kg−1 body weight day−1 for consumers of foods packed in light metal packaging. This is well below the current EFSA (and other recognised bodies) TDI of 0.05 mg kg−1 body weight day. These probabilistic estimates were compared with estimates using a refined deterministic approach drawing on the same input data. The results from FACET for the mean, 95th and 97.5th percentile exposures to BPA lay between the lowest and the highest estimates from the refined deterministic calculations. Since this should be the case, for a fully probabilistic compared with a deterministic approach, it is concluded that the FACET tool has been verified in this example. A recent EFSA draft opinion on exposure to BPA from different sources showed that canned foods were a major contributor and compared results from various models, including those from FACET. The results from FACET were overall conservative. PMID:24405320

Optimization of Systems with Uncertainty: Initial Developments for Performance, Robustness and Reliability Based Designs

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.

Advanced probabilistic methods for quantifying the effects of various uncertainties in structural response

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.

1988-01-01

The effects of actual variations, also called uncertainties, in geometry and material properties on the structural response of a space shuttle main engine turbopump blade are evaluated. A normal distribution was assumed to represent the uncertainties statistically. Uncertainties were assumed to be totally random, partially correlated, and fully correlated. The magnitude of these uncertainties were represented in terms of mean and variance. Blade responses, recorded in terms of displacements, natural frequencies, and maximum stress, was evaluated and plotted in the form of probabilistic distributions under combined uncertainties. These distributions provide an estimate of the range of magnitudes of the response and probability of occurrence of a given response. Most importantly, these distributions provide the information needed to estimate quantitatively the risk in a structural design.

Wlan-Based Indoor Localization Using Neural Networks

NASA Astrophysics Data System (ADS)

Saleem, Fasiha; Wyne, Shurjeel

2016-07-01

Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

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

Base-Rate Neglect as a Function of Base Rates in Probabilistic Contingency Learning

ERIC Educational Resources Information Center

Kutzner, Florian; Freytag, Peter; Vogel, Tobias; Fiedler, Klaus

2008-01-01

When humans predict criterion events based on probabilistic predictors, they often lend excessive weight to the predictor and insufficient weight to the base rate of the criterion event. In an operant analysis, using a matching-to-sample paradigm, Goodie and Fantino (1996) showed that humans exhibit base-rate neglect when predictors are associated…

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 mapping of flood-induced backscatter changes in SAR time series

NASA Astrophysics Data System (ADS)

Schlaffer, Stefan; Chini, Marco; Giustarini, Laura; Matgen, Patrick

2017-04-01

The information content of flood extent maps can be increased considerably by including information on the uncertainty of the flood area delineation. This additional information can be of benefit in flood forecasting and monitoring. Furthermore, flood probability maps can be converted to binary maps showing flooded and non-flooded areas by applying a threshold probability value pF = 0.5. In this study, a probabilistic change detection approach for flood mapping based on synthetic aperture radar (SAR) time series is proposed. For this purpose, conditional probability density functions (PDFs) for land and open water surfaces were estimated from ENVISAT ASAR Wide Swath (WS) time series containing >600 images using a reference mask of permanent water bodies. A pixel-wise harmonic model was used to account for seasonality in backscatter from land areas caused by soil moisture and vegetation dynamics. The approach was evaluated for a large-scale flood event along the River Severn, United Kingdom. The retrieved flood probability maps were compared to a reference flood mask derived from high-resolution aerial imagery by means of reliability diagrams. The obtained performance measures indicate both high reliability and confidence although there was a slight under-estimation of the flood extent, which may in part be attributed to topographically induced radar shadows along the edges of the floodplain. Furthermore, the results highlight the importance of local incidence angle for the separability between flooded and non-flooded areas as specular reflection properties of open water surfaces increase with a more oblique viewing geometry.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Estimating rates of local species extinction, colonization and turnover in animal communities

USGS Publications Warehouse

Nichols, James D.; Boulinier, T.; Hines, J.E.; Pollock, K.H.; Sauer, J.R.

1998-01-01

Species richness has been identified as a useful state variable for conservation and management purposes. Changes in richness over time provide a basis for predicting and evaluating community responses to management, to natural disturbance, and to changes in factors such as community composition (e.g., the removal of a keystone species). Probabilistic capture-recapture models have been used recently to estimate species richness from species count and presence-absence data. These models do not require the common assumption that all species are detected in sampling efforts. We extend this approach to the development of estimators useful for studying the vital rates responsible for changes in animal communities over time; rates of local species extinction, turnover, and colonization. Our approach to estimation is based on capture-recapture models for closed animal populations that permit heterogeneity in detection probabilities among the different species in the sampled community. We have developed a computer program, COMDYN, to compute many of these estimators and associated bootstrap variances. Analyses using data from the North American Breeding Bird Survey (BBS) suggested that the estimators performed reasonably well. We recommend estimators based on probabilistic modeling for future work on community responses to management efforts as well as on basic questions about community dynamics.

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

Estimating the risk of Amazonian forest dieback.

PubMed

Rammig, Anja; Jupp, Tim; Thonicke, Kirsten; Tietjen, Britta; Heinke, Jens; Ostberg, Sebastian; Lucht, Wolfgang; Cramer, Wolfgang; Cox, Peter

2010-08-01

*Climate change will very likely affect most forests in Amazonia during the course of the 21st century, but the direction and intensity of the change are uncertain, in part because of differences in rainfall projections. In order to constrain this uncertainty, we estimate the probability for biomass change in Amazonia on the basis of rainfall projections that are weighted by climate model performance for current conditions. *We estimate the risk of forest dieback by using weighted rainfall projections from 24 general circulation models (GCMs) to create probability density functions (PDFs) for future forest biomass changes simulated by a dynamic vegetation model (LPJmL). *Our probabilistic assessment of biomass change suggests a likely shift towards increasing biomass compared with nonweighted results. Biomass estimates range between a gain of 6.2 and a loss of 2.7 kg carbon m(-2) for the Amazon region, depending on the strength of CO(2) fertilization. *The uncertainty associated with the long-term effect of CO(2) is much larger than that associated with precipitation change. This underlines the importance of reducing uncertainties in the direct effects of CO(2) on tropical ecosystems.

N-mix for fish: estimating riverine salmonid habitat selection via N-mixture models

USGS Publications Warehouse

Som, Nicholas A.; Perry, Russell W.; Jones, Edward C.; De Juilio, Kyle; Petros, Paul; Pinnix, William D.; Rupert, Derek L.

2018-01-01

Models that formulate mathematical linkages between fish use and habitat characteristics are applied for many purposes. For riverine fish, these linkages are often cast as resource selection functions with variables including depth and velocity of water and distance to nearest cover. Ecologists are now recognizing the role that detection plays in observing organisms, and failure to account for imperfect detection can lead to spurious inference. Herein, we present a flexible N-mixture model to associate habitat characteristics with the abundance of riverine salmonids that simultaneously estimates detection probability. Our formulation has the added benefits of accounting for demographics variation and can generate probabilistic statements regarding intensity of habitat use. In addition to the conceptual benefits, model application to data from the Trinity River, California, yields interesting results. Detection was estimated to vary among surveyors, but there was little spatial or temporal variation. Additionally, a weaker effect of water depth on resource selection is estimated than that reported by previous studies not accounting for detection probability. N-mixture models show great promise for applications to riverine resource selection.

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 assessment of uncertain adaptive hybrid composites

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Singhal, Surendra N.; Chamis, Christos C.

1994-01-01

Adaptive composite structures using actuation materials, such as piezoelectric fibers, were assessed probabilistically utilizing intraply hybrid composite mechanics in conjunction with probabilistic composite structural analysis. Uncertainties associated with the actuation material as well as the uncertainties in the regular (traditional) composite material properties were quantified and considered in the assessment. Static and buckling analyses were performed for rectangular panels with various boundary conditions and different control arrangements. The probability density functions of the structural behavior, such as maximum displacement and critical buckling load, were computationally simulated. The results of the assessment indicate that improved design and reliability can be achieved with actuation material.

CUFID-query: accurate network querying through random walk based network flow estimation.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2017-12-28

Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive performance evaluation based on biological networks with known functional modules, we show that CUFID-query outperforms the existing state-of-the-art algorithms in terms of prediction accuracy and biological significance of the predictions.

Probabilistic analysis of a materially nonlinear structure

NASA Technical Reports Server (NTRS)

Millwater, H. R.; Wu, Y.-T.; Fossum, A. F.

1990-01-01

A probabilistic finite element program is used to perform probabilistic analysis of a materially nonlinear structure. The program used in this study is NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), under development at Southwest Research Institute. The cumulative distribution function (CDF) of the radial stress of a thick-walled cylinder under internal pressure is computed and compared with the analytical solution. In addition, sensitivity factors showing the relative importance of the input random variables are calculated. Significant plasticity is present in this problem and has a pronounced effect on the probabilistic results. The random input variables are the material yield stress and internal pressure with Weibull and normal distributions, respectively. The results verify the ability of NESSUS to compute the CDF and sensitivity factors of a materially nonlinear structure. In addition, the ability of the Advanced Mean Value (AMV) procedure to assess the probabilistic behavior of structures which exhibit a highly nonlinear response is shown. Thus, the AMV procedure can be applied with confidence to other structures which exhibit nonlinear behavior.

Probabilistic and deterministic evaluation of uncertainty in a local scale multi-risk analysis

NASA Astrophysics Data System (ADS)

Lari, S.; Frattini, P.; Crosta, G. B.

2009-04-01

We performed a probabilistic multi-risk analysis (QPRA) at the local scale for a 420 km2 area surrounding the town of Brescia (Northern Italy). We calculated the expected annual loss in terms of economical damage and life loss, for a set of risk scenarios of flood, earthquake and industrial accident with different occurrence probabilities and different intensities. The territorial unit used for the study was the census parcel, of variable area, for which a large amount of data was available. Due to the lack of information related to the evaluation of the hazards, to the value of the exposed elements (e.g., residential and industrial area, population, lifelines, sensitive elements as schools, hospitals) and to the process-specific vulnerability, and to a lack of knowledge of the processes (floods, industrial accidents, earthquakes), we assigned an uncertainty to the input variables of the analysis. For some variables an homogeneous uncertainty was assigned on the whole study area, as for instance for the number of buildings of various typologies, and for the event occurrence probability. In other cases, as for phenomena intensity (e.g.,depth of water during flood) and probability of impact, the uncertainty was defined in relation to the census parcel area. In fact assuming some variables homogeneously diffused or averaged on the census parcels, we introduce a larger error for larger parcels. We propagated the uncertainty in the analysis using three different models, describing the reliability of the output (risk) as a function of the uncertainty of the inputs (scenarios and vulnerability functions). We developed a probabilistic approach based on Monte Carlo simulation, and two deterministic models, namely First Order Second Moment (FOSM) and Point Estimate (PE). In general, similar values of expected losses are obtained with the three models. The uncertainty of the final risk value is in the three cases around the 30% of the expected value. Each of the models, nevertheless, requires different assumptions and computational efforts, and provides results with different level of detail.

A comparison of the weights-of-evidence method and probabilistic neural networks

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1999-01-01

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

Estimating Independent Locally Shifted Random Utility Models for Ranking Data

ERIC Educational Resources Information Center

Lam, Kar Yin; Koning, Alex J.; Franses, Philip Hans

2011-01-01

We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any…

A chance constraint estimation approach to optimizing resource management under uncertainty

Treesearch

Michael Bevers

2007-01-01

Chance-constrained optimization is an important method for managing risk arising from random variations in natural resource systems, but the probabilistic formulations often pose mathematical programming problems that cannot be solved with exact methods. A heuristic estimation method for these problems is presented that combines a formulation for order statistic...

An In Vitro Assessment of Bioaccessibility of Arsenicals in Rice and the Use of this Estimate within a Probabilistic Exposure Model

EPA Science Inventory

In this study, an in vitro synthetic gastrointestinal extraction protocol was used to estimate bioaccessibility of different arsenicals present in seventeen rice samples of various grain types that were collected across the US. The across matrix average for total arsenic was 209...

Perceptual-motor skill learning in Gilles de la Tourette syndrome. Evidence for multiple procedural learning and memory systems.

PubMed

Marsh, Rachel; Alexander, Gerianne M; Packard, Mark G; Zhu, Hongtu; Peterson, Bradley S

2005-01-01

Procedural learning and memory systems likely comprise several skills that are differentially affected by various illnesses of the central nervous system, suggesting their relative functional independence and reliance on differing neural circuits. Gilles de la Tourette syndrome (GTS) is a movement disorder that involves disturbances in the structure and function of the striatum and related circuitry. Recent studies suggest that patients with GTS are impaired in performance of a probabilistic classification task that putatively involves the acquisition of stimulus-response (S-R)-based habits. Assessing the learning of perceptual-motor skills and probabilistic classification in the same samples of GTS and healthy control subjects may help to determine whether these various forms of procedural (habit) learning rely on the same or differing neuroanatomical substrates and whether those substrates are differentially affected in persons with GTS. Therefore, we assessed perceptual-motor skill learning using the pursuit-rotor and mirror tracing tasks in 50 patients with GTS and 55 control subjects who had previously been compared at learning a task of probabilistic classifications. The GTS subjects did not differ from the control subjects in performance of either the pursuit rotor or mirror-tracing tasks, although they were significantly impaired in the acquisition of a probabilistic classification task. In addition, learning on the perceptual-motor tasks was not correlated with habit learning on the classification task in either the GTS or healthy control subjects. These findings suggest that the differing forms of procedural learning are dissociable both functionally and neuroanatomically. The specific deficits in the probabilistic classification form of habit learning in persons with GTS are likely to be a consequence of disturbances in specific corticostriatal circuits, but not the same circuits that subserve the perceptual-motor form of habit learning.

Design of high temperature ceramic components against fast fracture and time-dependent failure using cares/life

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

Jadaan, O.M.; Powers, L.M.; Nemeth, N.N.

1995-08-01

A probabilistic design methodology which predicts the fast fracture and time-dependent failure behavior of thermomechanically loaded ceramic components is discussed using the CARES/LIFE integrated design computer program. Slow crack growth (SCG) is assumed to be the mechanism responsible for delayed failure behavior. Inert strength and dynamic fatigue data obtained from testing coupon specimens (O-ring and C-ring specimens) are initially used to calculate the fast fracture and SCG material parameters as a function of temperature using the parameter estimation techniques available with the CARES/LIFE code. Finite element analysis (FEA) is used to compute the stress distributions for the tube as amore » function of applied pressure. Knowing the stress and temperature distributions and the fast fracture and SCG material parameters, the life time for a given tube can be computed. A stress-failure probability-time to failure (SPT) diagram is subsequently constructed for these tubes. Such a diagram can be used by design engineers to estimate the time to failure at a given failure probability level for a component subjected to a given thermomechanical load.« less

Quantifying the uncertainty in site amplification modeling and its effects on site-specific seismic-hazard estimation in the upper Mississippi embayment and adjacent areas

USGS Publications Warehouse

Cramer, C.H.

2006-01-01

The Mississippi embayment, located in the central United States, and its thick deposits of sediments (over 1 km in places) have a large effect on earthquake ground motions. Several previous studies have addressed how these thick sediments might modify probabilistic seismic-hazard maps. The high seismic hazard associated with the New Madrid seismic zone makes it particularly important to quantify the uncertainty in modeling site amplification to better represent earthquake hazard in seismic-hazard maps. The methodology of the Memphis urban seismic-hazard-mapping project (Cramer et al., 2004) is combined with the reference profile approach of Toro and Silva (2001) to better estimate seismic hazard in the Mississippi embayment. Improvements over previous approaches include using the 2002 national seismic-hazard model, fully probabilistic hazard calculations, calibration of site amplification with improved nonlinear soil-response estimates, and estimates of uncertainty. Comparisons are made with the results of several previous studies, and estimates of uncertainty inherent in site-amplification modeling for the upper Mississippi embayment are developed. I present new seismic-hazard maps for the upper Mississippi embayment with the effects of site geology incorporating these uncertainties.

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.

Passage relevance models for genomics search.

PubMed

Urbain, Jay; Frieder, Ophir; Goharian, Nazli

2009-03-19

We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

The idiosyncratic nature of confidence

PubMed Central

Navajas, Joaquin; Hindocha, Chandni; Foda, Hebah; Keramati, Mehdi; Latham, Peter E; Bahrami, Bahador

2017-01-01

Confidence is the ‘feeling of knowing’ that accompanies decision making. Bayesian theory proposes that confidence is a function solely of the perceived probability of being correct. Empirical research has suggested, however, that different individuals may perform different computations to estimate confidence from uncertain evidence. To test this hypothesis, we collected confidence reports in a task where subjects made categorical decisions about the mean of a sequence. We found that for most individuals, confidence did indeed reflect the perceived probability of being correct. However, in approximately half of them, confidence also reflected a different probabilistic quantity: the perceived uncertainty in the estimated variable. We found that the contribution of both quantities was stable over weeks. We also observed that the influence of the perceived probability of being correct was stable across two tasks, one perceptual and one cognitive. Overall, our findings provide a computational interpretation of individual differences in human confidence. PMID:29152591

Validation of PV-RPM Code in the System Advisor Model.

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

Klise, Geoffrey Taylor; Lavrova, Olga; Freeman, Janine

2017-04-01

This paper describes efforts made by Sandia National Laboratories (SNL) and the National Renewable Energy Laboratory (NREL) to validate the SNL developed PV Reliability Performance Model (PV - RPM) algorithm as implemented in the NREL System Advisor Model (SAM). The PV - RPM model is a library of functions that estimates component failure and repair in a photovoltaic system over a desired simulation period. The failure and repair distributions in this paper are probabilistic representations of component failure and repair based on data collected by SNL for a PV power plant operating in Arizona. The validation effort focuses on whethermore » the failure and repair dist ributions used in the SAM implementation result in estimated failures that match the expected failures developed in the proof - of - concept implementation. Results indicate that the SAM implementation of PV - RPM provides the same results as the proof - of - concep t implementation, indicating the algorithms were reproduced successfully.« less

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.

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.

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

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.

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.

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

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.

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

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

PubMed

Prieto, Faustino; Gómez-Déniz, Emilio; Sarabia, José María

2014-10-01

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed. Copyright © 2014 Elsevier Ltd. All rights reserved.

3D Traffic Scene Understanding From Movable Platforms.

PubMed

Geiger, Andreas; Lauer, Martin; Wojek, Christian; Stiller, Christoph; Urtasun, Raquel

2014-05-01

In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry, and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar, or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow, and occupancy grids. For each of these cues, we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.

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

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

Optimization of the kernel functions in a probabilistic neural network analyzing the local pattern distribution.

PubMed

Galleske, I; Castellanos, J

2002-05-01

This article proposes a procedure for the automatic determination of the elements of the covariance matrix of the gaussian kernel function of probabilistic neural networks. Two matrices, a rotation matrix and a matrix of variances, can be calculated by analyzing the local environment of each training pattern. The combination of them will form the covariance matrix of each training pattern. This automation has two advantages: First, it will free the neural network designer from indicating the complete covariance matrix, and second, it will result in a network with better generalization ability than the original model. A variation of the famous two-spiral problem and real-world examples from the UCI Machine Learning Repository will show a classification rate not only better than the original probabilistic neural network but also that this model can outperform other well-known classification techniques.

B-value and slip rate sensitivity analysis for PGA value in Lembang fault and Cimandiri fault area

NASA Astrophysics Data System (ADS)

Pratama, Cecep; Ito, Takeo; Meilano, Irwan; Nugraha, Andri Dian

2017-07-01

We examine slip rate and b-value contribution of Peak Ground Acceleration (PGA), in probabilistic seismic hazard maps (10% probability of exceedence in 50 years or 500 years return period). Hazard curve of PGA have been investigated for Sukabumi and Bandung using a PSHA (Probabilistic Seismic Hazard Analysis). We observe that the most influence in the hazard estimate is crustal fault. Monte Carlo approach has been developed to assess the sensitivity. Uncertainty and coefficient of variation from slip rate and b-value in Lembang and Cimandiri Fault area have been calculated. We observe that seismic hazard estimates are sensitive to fault slip rate and b-value with uncertainty result are 0.25 g dan 0.1-0.2 g, respectively. For specific site, we found seismic hazard estimate are 0.49 + 0.13 g with COV 27% and 0.39 + 0.05 g with COV 13% for Sukabumi and Bandung, respectively.

Probabilistic Appraisal of Earthquake Hazard Parameters Deduced from a Bayesian Approach in the Northwest Frontier of the Himalayas

NASA Astrophysics Data System (ADS)

Yadav, R. B. S.; Tsapanos, T. M.; Bayrak, Yusuf; Koravos, G. Ch.

2013-03-01

A straightforward Bayesian statistic is applied in five broad seismogenic source zones of the northwest frontier of the Himalayas to estimate the earthquake hazard parameters (maximum regional magnitude M max, β value of G-R relationship and seismic activity rate or intensity λ). For this purpose, a reliable earthquake catalogue which is homogeneous for M W ≥ 5.0 and complete during the period 1900 to 2010 is compiled. The Hindukush-Pamir Himalaya zone has been further divided into two seismic zones of shallow ( h ≤ 70 km) and intermediate depth ( h > 70 km) according to the variation of seismicity with depth in the subduction zone. The estimated earthquake hazard parameters by Bayesian approach are more stable and reliable with low standard deviations than other approaches, but the technique is more time consuming. In this study, quantiles of functions of distributions of true and apparent magnitudes for future time intervals of 5, 10, 20, 50 and 100 years are calculated with confidence limits for probability levels of 50, 70 and 90 % in all seismogenic source zones. The zones of estimated M max greater than 8.0 are related to the Sulaiman-Kirthar ranges, Hindukush-Pamir Himalaya and Himalayan Frontal Thrusts belt; suggesting more seismically hazardous regions in the examined area. The lowest value of M max (6.44) has been calculated in Northern-Pakistan and Hazara syntaxis zone which have estimated lowest activity rate 0.0023 events/day as compared to other zones. The Himalayan Frontal Thrusts belt exhibits higher earthquake magnitude (8.01) in next 100-years with 90 % probability level as compared to other zones, which reveals that this zone is more vulnerable to occurrence of a great earthquake. The obtained results in this study are directly useful for the probabilistic seismic hazard assessment in the examined region of Himalaya.

Probabilistic solutions of nonlinear oscillators excited by combined colored and white noise excitations

NASA Astrophysics Data System (ADS)

Siu-Siu, Guo; Qingxuan, Shi

2017-03-01

In this paper, single-degree-of-freedom (SDOF) systems combined to Gaussian white noise and Gaussian/non-Gaussian colored noise excitations are investigated. By expressing colored noise excitation as a second-order filtered white noise process and introducing colored noise as an additional state variable, the equation of motion for SDOF system under colored noise is then transferred artificially to multi-degree-of-freedom (MDOF) system under white noise excitations with four-coupled first-order differential equations. As a consequence, corresponding Fokker-Planck-Kolmogorov (FPK) equation governing the joint probabilistic density function (PDF) of state variables increases to 4-dimension (4-D). Solution procedure and computer programme become much more sophisticated. The exponential-polynomial closure (EPC) method, widely applied for cases of SDOF systems under white noise excitations, is developed and improved for cases of systems under colored noise excitations and for solving the complex 4-D FPK equation. On the other hand, Monte Carlo simulation (MCS) method is performed to test the approximate EPC solutions. Two examples associated with Gaussian and non-Gaussian colored noise excitations are considered. Corresponding band-limited power spectral densities (PSDs) for colored noise excitations are separately given. Numerical studies show that the developed EPC method provides relatively accurate estimates of the stationary probabilistic solutions, especially the ones in the tail regions of the PDFs. Moreover, statistical parameter of mean-up crossing rate (MCR) is taken into account, which is important for reliability and failure analysis. Hopefully, our present work could provide insights into the investigation of structures under random loadings.

Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Wahl, Thomas; Plant, Nathaniel G.; Long, Joseph W.

2016-05-01

We assess erosion and flooding risk in the northern Gulf of Mexico by identifying interdependencies among oceanographic drivers and probabilistically modeling the resulting potential for coastal change. Wave and water level observations are used to determine relationships between six hydrodynamic parameters that influence total water level and therefore erosion and flooding, through consideration of a wide range of univariate distribution functions and multivariate elliptical copulas. Using these relationships, we explore how different our interpretation of the present-day erosion/flooding risk could be if we had seen more or fewer extreme realizations of individual and combinations of parameters in the past by simulating 10,000 physically and statistically consistent sea-storm time series. We find that seasonal total water levels associated with the 100 year return period could be up to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the observational records. Impact hours of collision and overwash—where total water levels exceed the dune toe or dune crest elevations—could be on average 70% (collision) and 100% (overwash) larger than inferred from the observations. Our model accounts for non-stationarity in a straightforward, non-parametric way that can be applied (with little adjustments) to many other coastlines. The probabilistic model presented here, which accounts for observational uncertainty, can be applied to other coastlines where short record lengths limit the ability to identify the full range of possible wave and water level conditions that coastal mangers and planners must consider to develop sustainable management strategies.

Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

USGS Publications Warehouse

Plant, Nathaniel G.; Wahl, Thomas; Long, Joseph W.

2016-01-01

We assess erosion and flooding risk in the northern Gulf of Mexico by identifying interdependencies among oceanographic drivers and probabilistically modeling the resulting potential for coastal change. Wave and water level observations are used to determine relationships between six hydrodynamic parameters that influence total water level and therefore erosion and flooding, through consideration of a wide range of univariate distribution functions and multivariate elliptical copulas. Using these relationships, we explore how different our interpretation of the present-day erosion/flooding risk could be if we had seen more or fewer extreme realizations of individual and combinations of parameters in the past by simulating 10,000 physically and statistically consistent sea-storm time series. We find that seasonal total water levels associated with the 100 year return period could be up to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the observational records. Impact hours of collision and overwash—where total water levels exceed the dune toe or dune crest elevations—could be on average 70% (collision) and 100% (overwash) larger than inferred from the observations. Our model accounts for non-stationarity in a straightforward, non-parametric way that can be applied (with little adjustments) to many other coastlines. The probabilistic model presented here, which accounts for observational uncertainty, can be applied to other coastlines where short record lengths limit the ability to identify the full range of possible wave and water level conditions that coastal mangers and planners must consider to develop sustainable management strategies.

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

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

Evaluation of Horizontal Seismic Hazard of Shahrekord, Iran

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

Amiri, G. Ghodrati; Dehkordi, M. Raeisi; Amrei, S. A. Razavian

2008-07-08

This paper presents probabilistic horizontal seismic hazard assessment of Shahrekord, Iran. It displays the probabilistic estimate of Peak Ground Horizontal Acceleration (PGHA) for the return period of 75, 225, 475 and 2475 years. The output of the probabilistic seismic hazard analysis is based on peak ground acceleration (PGA), which is the most common criterion in designing of buildings. A catalogue of seismic events that includes both historical and instrumental events was developed and covers the period from 840 to 2007. The seismic sources that affect the hazard in Shahrekord were identified within the radius of 150 km and the recurrencemore » relationships of these sources were generated. Finally four maps have been prepared to indicate the earthquake hazard of Shahrekord in the form of iso-acceleration contour lines for different hazard levels by using SEISRISK III software.« less

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.

Mixture Modeling for Background and Sources Separation in x-ray Astronomical Images

NASA Astrophysics Data System (ADS)

Guglielmetti, Fabrizia; Fischer, Rainer; Dose, Volker

2004-11-01

A probabilistic technique for the joint estimation of background and sources in high-energy astrophysics is described. Bayesian probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. The present analysis is applied to ROSAT PSPC data (0.1-2.4 keV) in Survey Mode. A background map is modelled using a Thin-Plate spline. Source probability maps are obtained for each pixel (45 arcsec) independently and for larger correlation lengths, revealing faint and extended sources. We will demonstrate that the described probabilistic method allows for detection improvement of faint extended celestial sources compared to the Standard Analysis Software System (SASS) used for the production of the ROSAT All-Sky Survey (RASS) catalogues.

Reconstructing cerebrovascular networks under local physiological constraints by integer programming

DOE PAGES

Rempfler, Markus; Schneider, Matthias; Ielacqua, Giovanna D.; ...

2015-04-23

We introduce a probabilistic approach to vessel network extraction that enforces physiological constraints on the vessel structure. The method accounts for both image evidence and geometric relationships between vessels by solving an integer program, which is shown to yield the maximum a posteriori (MAP) estimate to the probabilistic model. Starting from an over-connected network, it is pruning vessel stumps and spurious connections by evaluating the local geometry and the global connectivity of the graph. We utilize a high-resolution micro computed tomography (µCT) dataset of a cerebrovascular corrosion cast to obtain a reference network and learn the prior distributions of ourmore » probabilistic model. As a result, we perform experiments on micro magnetic resonance angiography (µMRA) images of mouse brains and discuss properties of the networks obtained under different tracking and pruning approaches.« less

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 Approach to Enable Extreme-Scale Simulations under Uncertainty and System Faults. Final Technical Report

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

Knio, Omar

2017-05-05

The current project develops a novel approach that uses a probabilistic description to capture the current state of knowledge about the computational solution. To effectively spread the computational effort over multiple nodes, the global computational domain is split into many subdomains. Computational uncertainty in the solution translates into uncertain boundary conditions for the equation system to be solved on those subdomains, and many independent, concurrent subdomain simulations are used to account for this bound- ary condition uncertainty. By relying on the fact that solutions on neighboring subdomains must agree with each other, a more accurate estimate for the global solutionmore » can be achieved. Statistical approaches in this update process make it possible to account for the effect of system faults in the probabilistic description of the computational solution, and the associated uncertainty is reduced through successive iterations. By combining all of these elements, the probabilistic reformulation allows splitting the computational work over very many independent tasks for good scalability, while being robust to system faults.« less

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

NASA Technical Reports Server (NTRS)

Lau, William K. M. (Technical Monitor); Bell, Thomas L.; Steiner, Matthias; Zhang, Yu; Wood, Eric F.

2002-01-01

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

Space shuttle solid rocket booster recovery system definition. Volume 2: SRB water impact Monte Carlo computer program, user's manual

NASA Technical Reports Server (NTRS)

1973-01-01

The HD 220 program was created as part of the space shuttle solid rocket booster recovery system definition. The model was generated to investigate the damage to SRB components under water impact loads. The random nature of environmental parameters, such as ocean waves and wind conditions, necessitates estimation of the relative frequency of occurrence for these parameters. The nondeterministic nature of component strengths also lends itself to probabilistic simulation. The Monte Carlo technique allows the simultaneous perturbation of multiple independent parameters and provides outputs describing the probability distribution functions of the dependent parameters. This allows the user to determine the required statistics for each output parameter.

Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff

NASA Astrophysics Data System (ADS)

Moncoulon, D.; Labat, D.; Ardon, J.; Leblois, E.; Onfroy, T.; Poulard, C.; Aji, S.; Rémy, A.; Quantin, A.

2014-09-01

The analysis of flood exposure at a national scale for the French insurance market must combine the generation of a probabilistic event set of all possible (but which have not yet occurred) flood situations with hazard and damage modeling. In this study, hazard and damage models are calibrated on a 1995-2010 historical event set, both for hazard results (river flow, flooded areas) and loss estimations. Thus, uncertainties in the deterministic estimation of a single event loss are known before simulating a probabilistic event set. To take into account at least 90 % of the insured flood losses, the probabilistic event set must combine the river overflow (small and large catchments) with the surface runoff, due to heavy rainfall, on the slopes of the watershed. Indeed, internal studies of the CCR (Caisse Centrale de Reassurance) claim database have shown that approximately 45 % of the insured flood losses are located inside the floodplains and 45 % outside. Another 10 % is due to sea surge floods and groundwater rise. In this approach, two independent probabilistic methods are combined to create a single flood loss distribution: a generation of fictive river flows based on the historical records of the river gauge network and a generation of fictive rain fields on small catchments, calibrated on the 1958-2010 Météo-France rain database SAFRAN. All the events in the probabilistic event sets are simulated with the deterministic model. This hazard and damage distribution is used to simulate the flood losses at the national scale for an insurance company (Macif) and to generate flood areas associated with hazard return periods. The flood maps concern river overflow and surface water runoff. Validation of these maps is conducted by comparison with the address located claim data on a small catchment (downstream Argens).

A review of estimation of distribution algorithms in bioinformatics

PubMed Central

Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro

2008-01-01

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112

Satellite-map position estimation for the Mars rover

NASA Technical Reports Server (NTRS)

Hayashi, Akira; Dean, Thomas

1989-01-01

A method for locating the Mars rover using an elevation map generated from satellite data is described. In exploring its environment, the rover is assumed to generate a local rover-centered elevation map that can be used to extract information about the relative position and orientation of landmarks corresponding to local maxima. These landmarks are integrated into a stochastic map which is then matched with the satellite map to obtain an estimate of the robot's current location. The landmarks are not explicitly represented in the satellite map. The results of the matching algorithm correspond to a probabilistic assessment of whether or not the robot is located within a given region of the satellite map. By assigning a probabilistic interpretation to the information stored in the satellite map, researchers are able to provide a precise characterization of the results computed by the matching algorithm.

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dietary Iron Bioavailability: Agreement between Estimation Methods and Association with Serum Ferritin Concentrations in Women of Childbearing Age

PubMed Central

Dias, Gisele Cristina; Morimoto, Juliana Massami; Marchioni, Dirce Maria Lobo; Colli, Célia

2018-01-01

Predictive iron bioavailability (FeBio) methods aimed at evaluating the association between diet and body iron have been proposed, but few studies explored their validity and practical usefulness in epidemiological studies. In this cross-sectional study involving 127 women (18–42 years) with presumably steady-state body iron balance, correlations were checked among various FeBio estimates (probabilistic approach and meal-based and diet-based algorithms) and serum ferritin (SF) concentrations. Iron deficiency was defined as SF < 15 µg/L. Pearson correlation, Friedman test, and linear regression were employed. Iron intake and prevalence of iron deficiency were 10.9 mg/day and 12.6%. Algorithm estimates were strongly correlated (0.69≤ r ≥0.85; p < 0.001), although diet-based models (8.5–8.9%) diverged from meal-based models (11.6–12.8%; p < 0.001). Still, all algorithms underestimated the probabilistic approach (17.2%). No significant association was found between SF and FeBio from Monsen (1978), Reddy (2000), and Armah (2013) algorithms. Nevertheless, there was a 30–37% difference in SF concentrations between women stratified at extreme tertiles of FeBio from Hallberg and Hulthén (2000) and Collings’ (2013) models. The results demonstrate discordance of FeBio from probabilistic approach and algorithm methods while suggesting two models with best performances to rank individuals according to their bioavailable iron intakes. PMID:29883384

A probabilistic framework to infer brain functional connectivity from anatomical connections.

PubMed

Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Robinson, Emma; Sharp, David J; Edwards, A David; Rueckert, Daniel

2011-01-01

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, i.e. the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices. Our model is based on constraining the conditional independence structure of functional activity by the anatomical connectivity. Subsequently, we learn a linear predictor of a stationary multivariate autoregressive model. This natural parameterization of functional connectivity also enforces the positive-definiteness of the predicted covariance and thus matches the structure of the output space. Our results show that functional connectivity can be explained by anatomical connectivity on a rigorous statistical basis, and that a proper model of functional connectivity is essential to assess this link.

Probabilistic assessment of landslide tsunami hazard for the northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Pampell-Manis, A.; Horrillo, J.; Shigihara, Y.; Parambath, L.

2016-01-01

The devastating consequences of recent tsunamis affecting Indonesia and Japan have prompted a scientific response to better assess unexpected tsunami hazards. Although much uncertainty exists regarding the recurrence of large-scale tsunami events in the Gulf of Mexico (GoM), geological evidence indicates that a tsunami is possible and would most likely come from a submarine landslide triggered by an earthquake. This study customizes for the GoM a first-order probabilistic landslide tsunami hazard assessment. Monte Carlo Simulation (MCS) is employed to determine landslide configurations based on distributions obtained from observational submarine mass failure (SMF) data. Our MCS approach incorporates a Cholesky decomposition method for correlated landslide size parameters to capture correlations seen in the data as well as uncertainty inherent in these events. Slope stability analyses are performed using landslide and sediment properties and regional seismic loading to determine landslide configurations which fail and produce a tsunami. The probability of each tsunamigenic failure is calculated based on the joint probability of slope failure and probability of the triggering earthquake. We are thus able to estimate sizes and return periods for probabilistic maximum credible landslide scenarios. We find that the Cholesky decomposition approach generates landslide parameter distributions that retain the trends seen in observational data, improving the statistical validity and relevancy of the MCS technique in the context of landslide tsunami hazard assessment. Estimated return periods suggest that probabilistic maximum credible SMF events in the north and northwest GoM have a recurrence of 5000-8000 years, in agreement with age dates of observed deposits.

Economic Analysis of a Multi-Site Prevention Program: Assessment of Program Costs and Characterizing Site-level Variability

PubMed Central

Corso, Phaedra S.; Ingels, Justin B.; Kogan, Steven M.; Foster, E. Michael; Chen, Yi-Fu; Brody, Gene H.

2013-01-01

Programmatic cost analyses of preventive interventions commonly have a number of methodological difficulties. To determine the mean total costs and properly characterize variability, one often has to deal with small sample sizes, skewed distributions, and especially missing data. Standard approaches for dealing with missing data such as multiple imputation may suffer from a small sample size, a lack of appropriate covariates, or too few details around the method used to handle the missing data. In this study, we estimate total programmatic costs for a prevention trial evaluating the Strong African American Families-Teen program. This intervention focuses on the prevention of substance abuse and risky sexual behavior. To account for missing data in the assessment of programmatic costs we compare multiple imputation to probabilistic sensitivity analysis. The latter approach uses collected cost data to create a distribution around each input parameter. We found that with the multiple imputation approach, the mean (95% confidence interval) incremental difference was $2149 ($397, $3901). With the probabilistic sensitivity analysis approach, the incremental difference was $2583 ($778, $4346). Although the true cost of the program is unknown, probabilistic sensitivity analysis may be a more viable alternative for capturing variability in estimates of programmatic costs when dealing with missing data, particularly with small sample sizes and the lack of strong predictor variables. Further, the larger standard errors produced by the probabilistic sensitivity analysis method may signal its ability to capture more of the variability in the data, thus better informing policymakers on the potentially true cost of the intervention. PMID:23299559

Economic analysis of a multi-site prevention program: assessment of program costs and characterizing site-level variability.

PubMed

Corso, Phaedra S; Ingels, Justin B; Kogan, Steven M; Foster, E Michael; Chen, Yi-Fu; Brody, Gene H

2013-10-01

Programmatic cost analyses of preventive interventions commonly have a number of methodological difficulties. To determine the mean total costs and properly characterize variability, one often has to deal with small sample sizes, skewed distributions, and especially missing data. Standard approaches for dealing with missing data such as multiple imputation may suffer from a small sample size, a lack of appropriate covariates, or too few details around the method used to handle the missing data. In this study, we estimate total programmatic costs for a prevention trial evaluating the Strong African American Families-Teen program. This intervention focuses on the prevention of substance abuse and risky sexual behavior. To account for missing data in the assessment of programmatic costs we compare multiple imputation to probabilistic sensitivity analysis. The latter approach uses collected cost data to create a distribution around each input parameter. We found that with the multiple imputation approach, the mean (95 % confidence interval) incremental difference was $2,149 ($397, $3,901). With the probabilistic sensitivity analysis approach, the incremental difference was $2,583 ($778, $4,346). Although the true cost of the program is unknown, probabilistic sensitivity analysis may be a more viable alternative for capturing variability in estimates of programmatic costs when dealing with missing data, particularly with small sample sizes and the lack of strong predictor variables. Further, the larger standard errors produced by the probabilistic sensitivity analysis method may signal its ability to capture more of the variability in the data, thus better informing policymakers on the potentially true cost of the intervention.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

2012-12-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

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.

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.

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.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

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

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

A Probabilistic Model of Visual Working Memory: Incorporating Higher Order Regularities into Working Memory Capacity Estimates

ERIC Educational Resources Information Center

Brady, Timothy F.; Tenenbaum, Joshua B.

2013-01-01

When remembering a real-world scene, people encode both detailed information about specific objects and higher order information like the overall gist of the scene. However, formal models of change detection, like those used to estimate visual working memory capacity, assume observers encode only a simple memory representation that includes no…

Considerations for interpreting probabilistic estimates of uncertainty of forest carbon

Treesearch

James E. Smith; Linda S. Heath

2000-01-01

Quantitative estimated of carbon inventories are needed as part of nationwide attempts to reduce net release of greenhouse gases and the associated climate forcing. Naturally, an appreciable amount of uncertainty is inherent in such large-scale assessments, especially since both science and policy issues are still evolving. Decision makers need an idea of the...

Probabilistic combination of static and dynamic gait features for verification

NASA Astrophysics Data System (ADS)

Bazin, Alex I.; Nixon, Mark S.

2005-03-01

This paper describes a novel probabilistic framework for biometric identification and data fusion. Based on intra and inter-class variation extracted from training data, posterior probabilities describing the similarity between two feature vectors may be directly calculated from the data using the logistic function and Bayes rule. Using a large publicly available database we show the two imbalanced gait modalities may be fused using this framework. All fusion methods tested provide an improvement over the best modality, with the weighted sum rule giving the best performance, hence showing that highly imbalanced classifiers may be fused in a probabilistic setting; improving not only the performance, but also generalized application capability.

Assessing the public health benefits of reduced ozone concentrations.

PubMed Central

Levy, J I; Carrothers, T J; Tuomisto, J T; Hammitt, J K; Evans, J S

2001-01-01

In this paper we examine scientific evidence and related uncertainties in two steps of benefit-cost analyses of ozone reduction: estimating the health improvements attributable to reductions in ozone and determining the appropriate monetary values of these improvements. Although substantial evidence exists on molecular and physiologic impacts, the evidence needed to establish concentration-response functions is somewhat limited. Furthermore, because exposure to ozone depends on factors such as air conditioning use, past epidemiologic studies may not be directly applicable in unstudied settings. To evaluate the evidence likely to contribute significantly to benefits, we focus on four health outcomes: premature mortality, chronic asthma, respiratory hospital admissions, and minor restricted activity days. We determine concentration-response functions for these health outcomes for a hypothetical case study in Houston, Texas, using probabilistic weighting reflecting our judgment of the strength of the evidence and the possibility of confounding. We make a similar presentation for valuation, where uncertainty is due primarily to the lack of willingness-to-pay data for the population affected by ozone. We estimate that the annual monetary value of health benefits from reducing ozone concentrations in Houston is approximately $10 per person per microgram per cubic meter (24-hr average) reduced (95% confidence interval, $0.70-$40). The central estimate exceeds past estimates by approximately a factor of five, driven by the inclusion of mortality. We discuss the implications of our findings for future analyses and determine areas of research that might help reduce the uncertainties in benefit estimation. PMID:11748028

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PubMed Central

Chen, Yasheng; Juttukonda, Meher; Su, Yi; Benzinger, Tammie; Rubin, Brian G.; Lee, Yueh Z.; Lin, Weili; Shen, Dinggang; Lalush, David

2015-01-01

Purpose To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. © RSNA, 2014 PMID:25521778

Risk of DDT residue in maize consumed by infants as complementary diet in southwest Ethiopia.

PubMed

Mekonen, Seblework; Lachat, Carl; Ambelu, Argaw; Steurbaut, Walter; Kolsteren, Patrick; Jacxsens, Liesbeth; Wondafrash, Mekitie; Houbraken, Michael; Spanoghe, Pieter

2015-04-01

Infants in Ethiopia are consuming food items such as maize as a complementary diet. However, this may expose infants to toxic contaminants like DDT. Maize samples were collected from the households visited during a consumption survey and from markets in Jimma zone, southwestern Ethiopia. The residues of total DDT and its metabolites were analyzed using the Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) method combined with dispersive solid phase extraction cleanup (d-SPE). Deterministic and probabilistic methods of analysis were applied to determine the consumer exposure of infants to total DDT. The results from the exposure assessment were compared with the health based guidance value in this case the provisional tolerable daily intake (PTDI). All maize samples (n=127) were contaminated by DDT, with a mean concentration of 1.770 mg/kg, which was far above the maximum residue limit (MRL). The mean and 97.5 percentile (P 97.5) estimated daily intake of total DDT for consumers were respectively 0.011 and 0.309 mg/kg bw/day for deterministic and 0.011 and 0.083 mg/kg bw/day for probabilistic exposure assessment. For total infant population (consumers and non-consumers), the 97.5 percentile estimated daily intake were 0.265 and 0.032 mg/kg bw/day from the deterministic and probabilistic exposure assessments, respectively. Health risk estimation revealed that, the mean and 97.5 percentile for consumers, and 97.5 percentile estimated daily intake of total DDT for total population were above the PTDI. Therefore, in Ethiopia, the use of maize as complementary food for infants may pose a health risk due to DDT residue. Copyright © 2014 Elsevier B.V. All rights reserved.

Discriminative confidence estimation for probabilistic multi-atlas label fusion.

PubMed

Benkarim, Oualid M; Piella, Gemma; González Ballester, Miguel Angel; Sanroma, Gerard

2017-12-01

Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image. Accurately estimating the contribution of each atlas labelmap to the final segmentation is a critical step for the success of multi-atlas segmentation. Common approaches to label fusion either rely on local patch similarity, probabilistic statistical frameworks or a combination of both. In this work, we propose a probabilistic label fusion framework based on atlas label confidences computed at each voxel of the structure of interest. Maximum likelihood atlas confidences are estimated using a supervised approach, explicitly modeling the relationship between local image appearances and segmentation errors produced by each of the atlases. We evaluate different spatial pooling strategies for modeling local segmentation errors. We also present a novel type of label-dependent appearance features based on atlas labelmaps that are used during confidence estimation to increase the accuracy of our label fusion. Our approach is evaluated on the segmentation of seven subcortical brain structures from the MICCAI 2013 SATA Challenge dataset and the hippocampi from the ADNI dataset. Overall, our results indicate that the proposed label fusion framework achieves superior performance to state-of-the-art approaches in the majority of the evaluated brain structures and shows more robustness to registration errors. Copyright © 2017 Elsevier B.V. All rights reserved.

A fast and robust method for moment tensor and depth determination of shallow seismic events in CTBT related studies.

NASA Astrophysics Data System (ADS)

Baker, Ben; Stachnik, Joshua; Rozhkov, Mikhail

2017-04-01

International Data Center is required to conduct expert technical analysis and special studies to improve event parameters and assist State Parties in identifying the source of specific event according to the protocol to the Protocol to the Comprehensive Nuclear Test Ban Treaty. Determination of seismic event source mechanism and its depth is closely related to these tasks. It is typically done through a strategic linearized inversion of the waveforms for a complete or subset of source parameters, or similarly defined grid search through precomputed Greens Functions created for particular source models. In this presentation we demonstrate preliminary results obtained with the latter approach from an improved software design. In this development we tried to be compliant with different modes of CTBT monitoring regime and cover wide range of source-receiver distances (regional to teleseismic), resolve shallow source depths, provide full moment tensor solution based on body and surface waves recordings, be fast to satisfy both on-demand studies and automatic processing and properly incorporate observed waveforms and any uncertainties a priori as well as accurately estimate posteriori uncertainties. Posterior distributions of moment tensor parameters show narrow peaks where a significant number of reliable surface wave observations are available. For earthquake examples, fault orientation (strike, dip, and rake) posterior distributions also provide results consistent with published catalogues. Inclusion of observations on horizontal components will provide further constraints. In addition, the calculation of teleseismic P wave Green's Functions are improved through prior analysis to determine an appropriate attenuation parameter for each source-receiver path. Implemented HDF5 based Green's Functions pre-packaging allows much greater flexibility in utilizing different software packages and methods for computation. Further additions will have the rapid use of Instaseis/AXISEM full waveform synthetics added to a pre-computed GF archive. Along with traditional post processing analysis of waveform misfits through several objective functions and variance reduction, we follow a probabilistic approach to assess the robustness of moment tensor solution. In a course of this project full moment tensor and depth estimates are determined for DPRK events and shallow earthquakes using a new implementation of teleseismic P waves waveform fitting. A full grid search over the entire moment tensor space is used to appropriately sample all possible solutions. A recent method by Tape & Tape (2012) to discretize the complete moment tensor space from a geometric perspective is used. Probabilistic uncertainty estimates on the moment tensor parameters provide robustness to solution.

Quantifying interindividual variability and asymmetry of face-selective regions: a probabilistic functional atlas.

PubMed

Zhen, Zonglei; Yang, Zetian; Huang, Lijie; Kong, Xiang-Zhen; Wang, Xu; Dang, Xiaobin; Huang, Yangyue; Song, Yiying; Liu, Jia

2015-06-01

Face-selective regions (FSRs) are among the most widely studied functional regions in the human brain. However, individual variability of the FSRs has not been well quantified. Here we use functional magnetic resonance imaging (fMRI) to localize the FSRs and quantify their spatial and functional variabilities in 202 healthy adults. The occipital face area (OFA), posterior and anterior fusiform face areas (pFFA and aFFA), posterior continuation of the superior temporal sulcus (pcSTS), and posterior and anterior STS (pSTS and aSTS) were delineated for each individual with a semi-automated procedure. A probabilistic atlas was constructed to characterize their interindividual variability, revealing that the FSRs were highly variable in location and extent across subjects. The variability of FSRs was further quantified on both functional (i.e., face selectivity) and spatial (i.e., volume, location of peak activation, and anatomical location) features. Considerable interindividual variability and rightward asymmetry were found in all FSRs on these features. Taken together, our work presents the first effort to characterize comprehensively the variability of FSRs in a large sample of healthy subjects, and invites future work on the origin of the variability and its relation to individual differences in behavioral performance. Moreover, the probabilistic functional atlas will provide an adequate spatial reference for mapping the face network. Copyright © 2015 Elsevier Inc. All rights reserved.

A probabilistic approach to aircraft design emphasizing stability and control uncertainties

NASA Astrophysics Data System (ADS)

Delaurentis, Daniel Andrew

In order to address identified deficiencies in current approaches to aerospace systems design, a new method has been developed. This new method for design is based on the premise that design is a decision making activity, and that deterministic analysis and synthesis can lead to poor, or misguided decision making. This is due to a lack of disciplinary knowledge of sufficient fidelity about the product, to the presence of uncertainty at multiple levels of the aircraft design hierarchy, and to a failure to focus on overall affordability metrics as measures of goodness. Design solutions are desired which are robust to uncertainty and are based on the maximum knowledge possible. The new method represents advances in the two following general areas. 1. Design models and uncertainty. The research performed completes a transition from a deterministic design representation to a probabilistic one through a modeling of design uncertainty at multiple levels of the aircraft design hierarchy, including: (1) Consistent, traceable uncertainty classification and representation; (2) Concise mathematical statement of the Probabilistic Robust Design problem; (3) Variants of the Cumulative Distribution Functions (CDFs) as decision functions for Robust Design; (4) Probabilistic Sensitivities which identify the most influential sources of variability. 2. Multidisciplinary analysis and design. Imbedded in the probabilistic methodology is a new approach for multidisciplinary design analysis and optimization (MDA/O), employing disciplinary analysis approximations formed through statistical experimentation and regression. These approximation models are a function of design variables common to the system level as well as other disciplines. For aircraft, it is proposed that synthesis/sizing is the proper avenue for integrating multiple disciplines. Research hypotheses are translated into a structured method, which is subsequently tested for validity. Specifically, the implementation involves the study of the relaxed static stability technology for a supersonic commercial transport aircraft. The probabilistic robust design method is exercised resulting in a series of robust design solutions based on different interpretations of "robustness". Insightful results are obtained and the ability of the method to expose trends in the design space are noted as a key advantage.

Design for cyclic loading endurance of composites

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Murthy, Pappu L. N.; Chamis, Christos C.; Liaw, Leslie D. G.

1993-01-01

The application of the computer code IPACS (Integrated Probabilistic Assessment of Composite Structures) to aircraft wing type structures is described. The code performs a complete probabilistic analysis for composites taking into account the uncertainties in geometry, boundary conditions, material properties, laminate lay-ups, and loads. Results of the analysis are presented in terms of cumulative distribution functions (CDF) and probability density function (PDF) of the fatigue life of a wing type composite structure under different hygrothermal environments subjected to the random pressure. The sensitivity of the fatigue life to a number of critical structural/material variables is also computed from the analysis.

Probabilistic analysis of bladed turbine disks and the effect of mistuning

NASA Technical Reports Server (NTRS)

Shah, A. R.; Nagpal, V. K.; Chamis, Christos C.

1990-01-01

Probabilistic assessment of the maximum blade response on a mistuned rotor disk is performed using the computer code NESSUS. The uncertainties in natural frequency, excitation frequency, amplitude of excitation and damping are included to obtain the cumulative distribution function (CDF) of blade responses. Advanced mean value first order analysis is used to compute CDF. The sensitivities of different random variables are identified. Effect of the number of blades on a rotor on mistuning is evaluated. It is shown that the uncertainties associated with the forcing function parameters have significant effect on the response distribution of the bladed rotor.

Probabilistic analysis of bladed turbine disks and the effect of mistuning

NASA Technical Reports Server (NTRS)

Shah, Ashwin; Nagpal, V. K.; Chamis, C. C.

1990-01-01

Probabilistic assessment of the maximum blade response on a mistuned rotor disk is performed using the computer code NESSUS. The uncertainties in natural frequency, excitation frequency, amplitude of excitation and damping have been included to obtain the cumulative distribution function (CDF) of blade responses. Advanced mean value first order analysis is used to compute CDF. The sensitivities of different random variables are identified. Effect of the number of blades on a rotor on mistuning is evaluated. It is shown that the uncertainties associated with the forcing function parameters have significant effect on the response distribution of the bladed rotor.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.

PubMed

Peressutti, Devis; Penney, Graeme P; Housden, R James; Kolbitsch, Christoph; Gomez, Alberto; Rijkhorst, Erik-Jan; Barratt, Dean C; Rhode, Kawal S; King, Andrew P

2013-05-01

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method. Copyright © 2013 Elsevier B.V. All rights reserved.

The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images.

PubMed

Loxley, P N

2017-10-01

The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.

The Answer Is Probably E, p or v2

ERIC Educational Resources Information Center

McCartney, Mark

2017-01-01

A number of probabilistic experiments are described to estimate e, p and v2, with results from computer simulations being used to investigate convergence. A number of possible classroom exercises and extensions are presented.

A probabilistic approach to radiative energy loss calculations for optically thick atmospheres - Hydrogen lines and continua

NASA Technical Reports Server (NTRS)

Canfield, R. C.; Ricchiazzi, P. J.

1980-01-01

An approximate probabilistic radiative transfer equation and the statistical equilibrium equations are simultaneously solved for a model hydrogen atom consisting of three bound levels and ionization continuum. The transfer equation for L-alpha, L-beta, H-alpha, and the Lyman continuum is explicitly solved assuming complete redistribution. The accuracy of this approach is tested by comparing source functions and radiative loss rates to values obtained with a method that solves the exact transfer equation. Two recent model solar-flare chromospheres are used for this test. It is shown that for the test atmospheres the probabilistic method gives values of the radiative loss rate that are characteristically good to a factor of 2. The advantage of this probabilistic approach is that it retains a description of the dominant physical processes of radiative transfer in the complete redistribution case, yet it achieves a major reduction in computational requirements.

Discounting of food, sex, and money.

PubMed

Holt, Daniel D; Newquist, Matthew H; Smits, Rochelle R; Tiry, Andrew M

2014-06-01

Discounting is a useful framework for understanding choice involving a range of delayed and probabilistic outcomes (e.g., money, food, drugs), but relatively few studies have examined how people discount other commodities (e.g., entertainment, sex). Using a novel discounting task, where the length of a line represented the value of an outcome and was adjusted using a staircase procedure, we replicated previous findings showing that individuals discount delayed and probabilistic outcomes in a manner well described by a hyperbola-like function. In addition, we found strong positive correlations between discounting rates of delayed, but not probabilistic, outcomes. This suggests that discounting of delayed outcomes may be relatively predictable across outcome types but that discounting of probabilistic outcomes may depend more on specific contexts. The generality of delay discounting and potential context dependence of probability discounting may provide important information regarding factors contributing to choice behavior.

Novel probabilistic and distributed algorithms for guidance, control, and nonlinear estimation of large-scale multi-agent systems

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Saptarshi

Multi-agent systems are widely used for constructing a desired formation shape, exploring an area, surveillance, coverage, and other cooperative tasks. This dissertation introduces novel algorithms in the three main areas of shape formation, distributed estimation, and attitude control of large-scale multi-agent systems. In the first part of this dissertation, we address the problem of shape formation for thousands to millions of agents. Here, we present two novel algorithms for guiding a large-scale swarm of robotic systems into a desired formation shape in a distributed and scalable manner. These probabilistic swarm guidance algorithms adopt an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled using tunable Markov chains. In the first algorithm - Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) - each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain that is constructed in real-time using feedback from the current swarm distribution. This PSG-IMC algorithm minimizes the expected cost of the transitions required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. In the second algorithm - Probabilistic Swarm Guidance using Optimal Transport (PSG-OT) - each agent determines its bin transition probabilities by solving an optimal transport problem, which is recast as a linear program. In the presence of perfect feedback of the current swarm distribution, this algorithm minimizes the given cost function, guarantees faster convergence, reduces the number of transitions for achieving the desired formation, and is robust to disturbances or damages to the formation. We demonstrate the effectiveness of these two proposed swarm guidance algorithms using results from numerical simulations and closed-loop hardware experiments on multiple quadrotors. In the second part of this dissertation, we present two novel discrete-time algorithms for distributed estimation, which track a single target using a network of heterogeneous sensing agents. The Distributed Bayesian Filtering (DBF) algorithm, the sensing agents combine their normalized likelihood functions using the logarithmic opinion pool and the discrete-time dynamic average consensus algorithm. Each agent's estimated likelihood function converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. Using a new proof technique, the convergence, stability, and robustness properties of the DBF algorithm are rigorously characterized. The explicit bounds on the time step of the robust DBF algorithm are shown to depend on the time-scale of the target dynamics. Furthermore, the DBF algorithm for linear-Gaussian models can be cast into a modified form of the Kalman information filter. In the Bayesian Consensus Filtering (BCF) algorithm, the agents combine their estimated posterior pdfs multiple times within each time step using the logarithmic opinion pool scheme. Thus, each agent's consensual pdf minimizes the sum of Kullback-Leibler divergences with the local posterior pdfs. The performance and robust properties of these algorithms are validated using numerical simulations. In the third part of this dissertation, we present an attitude control strategy and a new nonlinear tracking controller for a spacecraft carrying a large object, such as an asteroid or a boulder. If the captured object is larger or comparable in size to the spacecraft and has significant modeling uncertainties, conventional nonlinear control laws that use exact feed-forward cancellation are not suitable because they exhibit a large resultant disturbance torque. The proposed nonlinear tracking control law guarantees global exponential convergence of tracking errors with finite-gain Lp stability in the presence of modeling uncertainties and disturbances, and reduces the resultant disturbance torque. Further, this control law permits the use of any attitude representation and its integral control formulation eliminates any constant disturbance. Under small uncertainties, the best strategy for stabilizing the combined system is to track a fuel-optimal reference trajectory using this nonlinear control law, because it consumes the least amount of fuel. In the presence of large uncertainties, the most effective strategy is to track the derivative plus proportional-derivative based reference trajectory, because it reduces the resultant disturbance torque. The effectiveness of the proposed attitude control law is demonstrated by using results of numerical simulation based on an Asteroid Redirect Mission concept. The new algorithms proposed in this dissertation will facilitate the development of versatile autonomous multi-agent systems that are capable of performing a variety of complex tasks in a robust and scalable manner.

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.

Estimation for the Linear Model With Uncertain Covariance Matrices

NASA Astrophysics Data System (ADS)

Zachariah, Dave; Shariati, Nafiseh; Bengtsson, Mats; Jansson, Magnus; Chatterjee, Saikat

2014-03-01

We derive a maximum a posteriori estimator for the linear observation model, where the signal and noise covariance matrices are both uncertain. The uncertainties are treated probabilistically by modeling the covariance matrices with prior inverse-Wishart distributions. The nonconvex problem of jointly estimating the signal of interest and the covariance matrices is tackled by a computationally efficient fixed-point iteration as well as an approximate variational Bayes solution. The statistical performance of estimators is compared numerically to state-of-the-art estimators from the literature and shown to perform favorably.

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862

Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

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.

Probabilistic Assessment of Soil Moisture using C-band Quad-polarized Remote Sensing Data from RISAT1

NASA Astrophysics Data System (ADS)

Pal, Manali; Suman, Mayank; Das, Sarit Kumar; Maity, Rajib

2017-04-01

Information on spatio-temporal distribution of surface Soil Moisture Content (SMC) is essential in several hydrological, meteorological and agricultural applications. There has been increasing importance of microwave active remote sensing data for large-scale estimation of surface SMC because of its ability to monitor spatial and temporal variation of surface SMC at regional, continental and global scale at a reasonably fine spatial and temporal resolution. The use of Synthetic Aperture Radar (SAR) is highly potential for catchment-scale applications due to high spatial resolution (˜10-20 m) both for vegetated and bare soil surface as well as because of its all-weather and day and night characteristics. However, one prime disadvantage of SAR is that their signal is subjective to SMC along with Land Use Land Cover (LULC) and surface roughness conditions, making the retrieval of SMC from SAR data an "ill-posed" problem. Moreover, the quantification of uncertainty due to inappropriate surface roughness characterization, soil texture, inversion techniques etc. even in the latest established retrieval methods, is little explored. This paper reports a recently developed method to estimate the surface SMC with probabilistic assessment of uncertainty associated with the estimation (Pal et al., 2016). Quad-polarized SAR data from Radar Imaging Satellite1 (RISAT1), launched in 2012 by Indian Space Research Organization (ISRO) and information on LULC regarding bareland and vegetated land (<30 cm height) are used in estimation using the potential of multivariate probabilistic assessment through copulas. The salient features of the study are: 1) development of a combined index to understand the role of all the quad-polarized backscattering coefficients and soil texture information in SMC estimation; 2) applicability of the model for different incidence angles using normalized incidence angle theory proposed by Zibri et al. (2005); and 3) assessment of uncertainty range of the estimated SMC. Supervised Principal Component Analysis (SPCA) is used for development of combined index and Frank copula is found to be the best-fit copula. The developed model is validated with the field soil moisture values over 334 monitoring points within the study area and used for development of a soil moisture map. While the performance is promising, the model is applicable only for bare and vegetated land. References: Pal, M., Maity, R., Suman, M., Das, S.K., Patel, P., and Srivastava, H.S., (2016). "Satellite-Based Probabilistic Assessment of Soil Moisture Using C-Band Quad-Polarized RISAT1 Data." IEEE Transactions on Geoscience and Remote Sensing, In Press, doi:10.1109/TGRS.2016.2623378. Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., (2005)."New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion." Remote Sensing of Environment, vol. 96, nos. 3-4, pp. 485-496.

Estimates of dietary exposure to bisphenol A (BPA) from light metal packaging using food consumption and packaging usage data: a refined deterministic approach and a fully probabilistic (FACET) approach.

PubMed

Oldring, P K T; Castle, L; O'Mahony, C; Dixon, J

2014-01-01

The FACET tool is a probabilistic model to estimate exposure to chemicals in foodstuffs, originating from flavours, additives and food contact materials. This paper demonstrates the use of the FACET tool to estimate exposure to BPA (bisphenol A) from light metal packaging. For exposure to migrants from food packaging, FACET uses industry-supplied data on the occurrence of substances in the packaging, their concentrations and construction of the packaging, which were combined with data from a market research organisation and food consumption data supplied by national database managers. To illustrate the principles, UK packaging data were used together with consumption data from the UK National Diet and Nutrition Survey (NDNS) dietary survey for 19-64 year olds for a refined deterministic verification. The UK data were chosen mainly because the consumption surveys are detailed, data for UK packaging at a detailed level were available and, arguably, the UK population is composed of high consumers of packaged foodstuffs. Exposures were run for each food category that could give rise to BPA from light metal packaging. Consumer loyalty to a particular type of packaging, commonly referred to as packaging loyalty, was set. The BPA extraction levels used for the 15 types of coating chemistries that could release BPA were in the range of 0.00005-0.012 mg dm(-2). The estimates of exposure to BPA using FACET for the total diet were 0.0098 (mean) and 0.0466 (97.5th percentile) mg/person/day, corresponding to 0.00013 (mean) and 0.00059 (97.5th percentile) mg kg(-1) body weight day(-1) for consumers of foods packed in light metal packaging. This is well below the current EFSA (and other recognised bodies) TDI of 0.05 mg kg(-1) body weight day(-1). These probabilistic estimates were compared with estimates using a refined deterministic approach drawing on the same input data. The results from FACET for the mean, 95th and 97.5th percentile exposures to BPA lay between the lowest and the highest estimates from the refined deterministic calculations. Since this should be the case, for a fully probabilistic compared with a deterministic approach, it is concluded that the FACET tool has been verified in this example. A recent EFSA draft opinion on exposure to BPA from different sources showed that canned foods were a major contributor and compared results from various models, including those from FACET. The results from FACET were overall conservative.

Probabilistic models in human sensorimotor control

PubMed Central

Wolpert, Daniel M.

2009-01-01

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty. PMID:17628731

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Probability versus representativeness in infancy: can infants use naïve physics to adjust population base rates in probabilistic inference?

PubMed

Denison, Stephanie; Trikutam, Pallavi; Xu, Fei

2014-08-01

A rich tradition in developmental psychology explores physical reasoning in infancy. However, no research to date has investigated whether infants can reason about physical objects that behave probabilistically, rather than deterministically. Physical events are often quite variable, in that similar-looking objects can be placed in similar contexts with different outcomes. Can infants rapidly acquire probabilistic physical knowledge, such as some leaves fall and some glasses break by simply observing the statistical regularity with which objects behave and apply that knowledge in subsequent reasoning? We taught 11-month-old infants physical constraints on objects and asked them to reason about the probability of different outcomes when objects were drawn from a large distribution. Infants could have reasoned either by using the perceptual similarity between the samples and larger distributions or by applying physical rules to adjust base rates and estimate the probabilities. Infants learned the physical constraints quickly and used them to estimate probabilities, rather than relying on similarity, a version of the representativeness heuristic. These results indicate that infants can rapidly and flexibly acquire physical knowledge about objects following very brief exposure and apply it in subsequent reasoning. PsycINFO Database Record (c) 2014 APA, all rights reserved.

A Probabilistic Analysis of Surface Water Flood Risk in London.

PubMed

Jenkins, Katie; Hall, Jim; Glenis, Vassilis; Kilsby, Chris

2018-06-01

Flooding in urban areas during heavy rainfall, often characterized by short duration and high-intensity events, is known as "surface water flooding." Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively. © 2017 Society for Risk Analysis.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

A Probabilistic Tsunami Hazard Study of the Auckland Region, Part I: Propagation Modelling and Tsunami Hazard Assessment at the Shoreline

NASA Astrophysics Data System (ADS)

Power, William; Wang, Xiaoming; Lane, Emily; Gillibrand, Philip

2013-09-01

Regional source tsunamis represent a potentially devastating threat to coastal communities in New Zealand, yet are infrequent events for which little historical information is available. It is therefore essential to develop robust methods for quantitatively estimating the hazards posed, so that effective mitigation measures can be implemented. We develop a probabilistic model for the tsunami hazard posed to the Auckland region of New Zealand from the Kermadec Trench and the southern New Hebrides Trench subduction zones. An innovative feature of our model is the systematic analysis of uncertainty regarding the magnitude-frequency distribution of earthquakes in the source regions. The methodology is first used to estimate the tsunami hazard at the coastline, and then used to produce a set of scenarios that can be applied to produce probabilistic maps of tsunami inundation for the study region; the production of these maps is described in part II. We find that the 2,500 year return period regional source tsunami hazard for the densely populated east coast of Auckland is dominated by events originating in the Kermadec Trench, while the equivalent hazard to the sparsely populated west coast is approximately equally due to events on the Kermadec Trench and the southern New Hebrides Trench.

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

Measuring the effect of fuel treatments on forest carbon using landscape risk analysis

NASA Astrophysics Data System (ADS)

Ager, A. A.; Finney, M. A.; McMahan, A.; Cathcart, J.

2010-12-01

Wildfire simulation modelling was used to examine whether fuel reduction treatments can potentially reduce future wildfire emissions and provide carbon benefits. In contrast to previous reports, the current study modelled landscape scale effects of fuel treatments on fire spread and intensity, and used a probabilistic framework to quantify wildfire effects on carbon pools to account for stochastic wildfire occurrence. The study area was a 68 474 ha watershed located on the Fremont-Winema National Forest in southeastern Oregon, USA. Fuel reduction treatments were simulated on 10% of the watershed (19% of federal forestland). We simulated 30 000 wildfires with random ignition locations under both treated and untreated landscapes to estimate the change in burn probability by flame length class resulting from the treatments. Carbon loss functions were then calculated with the Forest Vegetation Simulator for each stand in the study area to quantify change in carbon as a function of flame length. We then calculated the expected change in carbon from a random ignition and wildfire as the sum of the product of the carbon loss and the burn probabilities by flame length class. The expected carbon difference between the non-treatment and treatment scenarios was then calculated to quantify the effect of fuel treatments. Overall, the results show that the carbon loss from implementing fuel reduction treatments exceeded the expected carbon benefit associated with lowered burn probabilities and reduced fire severity on the treated landscape. Thus, fuel management activities resulted in an expected net loss of carbon immediately after treatment. However, the findings represent a point in time estimate (wildfire immediately after treatments), and a temporal analysis with a probabilistic framework used here is needed to model carbon dynamics over the life cycle of the fuel treatments. Of particular importance is the long-term balance between emissions from the decay of dead trees killed by fire and carbon sequestration by forest regeneration following wildfire.

Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984

Developing a Malaysia flood model

NASA Astrophysics Data System (ADS)

Haseldine, Lucy; Baxter, Stephen; Wheeler, Phil; Thomson, Tina

2014-05-01

Faced with growing exposures in Malaysia, insurers have a need for models to help them assess their exposure to flood losses. The need for an improved management of flood risks has been further highlighted by the 2011 floods in Thailand and recent events in Malaysia. The increasing demand for loss accumulation tools in Malaysia has lead to the development of the first nationwide probabilistic Malaysia flood model, which we present here. The model is multi-peril, including river flooding for thousands of kilometres of river and rainfall-driven surface water flooding in major cities, which may cause losses equivalent to river flood in some high-density urban areas. The underlying hazard maps are based on a 30m digital surface model (DSM) and 1D/2D hydraulic modelling in JFlow and RFlow. Key mitigation schemes such as the SMART tunnel and drainage capacities are also considered in the model. The probabilistic element of the model is driven by a stochastic event set based on rainfall data, hence enabling per-event and annual figures to be calculated for a specific insurance portfolio and a range of return periods. Losses are estimated via depth-damage vulnerability functions which link the insured damage to water depths for different property types in Malaysia. The model provides a unique insight into Malaysian flood risk profiles and provides insurers with return period estimates of flood damage and loss to property portfolios through loss exceedance curve outputs. It has been successfully validated against historic flood events in Malaysia and is now being successfully used by insurance companies in the Malaysian market to obtain reinsurance cover.

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.

Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

PubMed

Nishino, Ko; Lombardi, Stephen

2011-01-01

We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

A simulation of probabilistic wildfire risk components for the continental United States

Treesearch

Mark A. Finney; Charles W. McHugh; Isaac C. Grenfell; Karin L. Riley; Karen C. Short

2011-01-01

This simulation research was conducted in order to develop a large-fire risk assessment system for the contiguous land area of the United States. The modeling system was applied to each of 134 Fire Planning Units (FPUs) to estimate burn probabilities and fire size distributions. To obtain stable estimates of these quantities, fire ignition and growth was simulated for...

Method and system for dynamic probabilistic risk assessment

NASA Technical Reports Server (NTRS)

Dugan, Joanne Bechta (Inventor); Xu, Hong (Inventor)

2013-01-01

The DEFT methodology, system and computer readable medium extends the applicability of the PRA (Probabilistic Risk Assessment) methodology to computer-based systems, by allowing DFT (Dynamic Fault Tree) nodes as pivot nodes in the Event Tree (ET) model. DEFT includes a mathematical model and solution algorithm, supports all common PRA analysis functions and cutsets. Additional capabilities enabled by the DFT include modularization, phased mission analysis, sequence dependencies, and imperfect coverage.

Constraining ozone-precursor responsiveness using ambient measurements

EPA Science Inventory

This study develops probabilistic estimates of ozone (O3) sensitivities to precursoremissions by incorporating uncertainties in photochemical modeling and evaluating modelperformance based on ground-level observations of O3 and oxides of nitrogen (NOx).Uncertainties in model form...

Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics

PubMed Central

Girshick, Ahna R.; Landy, Michael S.; Simoncelli, Eero P.

2011-01-01

Humans are remarkably good at performing visual tasks, but experimental measurements reveal substantial biases in the perception of basic visual attributes. An appealing hypothesis is that these biases arise through a process of statistical inference, in which information from noisy measurements is fused with a probabilistic model of the environment. But such inference is optimal only if the observer’s internal model matches the environment. Here, we provide evidence that this is the case. We measured performance in an orientation-estimation task, demonstrating the well-known fact that orientation judgements are more accurate at cardinal (horizontal and vertical) orientations, along with a new observation that judgements made under conditions of uncertainty are strongly biased toward cardinal orientations. We estimate observers’ internal models for orientation and find that they match the local orientation distribution measured in photographs. We also show how a neural population could embed probabilistic information responsible for such biases. PMID:21642976

Detecting Cyber Attacks On Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Campbell, Roy

This paper proposes an unconventional anomaly detection approach that provides digital instrumentation and control (I&C) systems in a nuclear power plant (NPP) with the capability to probabilistically discern between legitimate protocol frames and attack frames. The stochastic activity network (SAN) formalism is used to model the fusion of protocol activity in each digital I&C system and the operation of physical components of an NPP. SAN models are employed to analyze links between protocol frames as streams of bytes, their semantics in terms of NPP operations, control data as stored in the memory of I&C systems, the operations of I&C systems on NPP components, and NPP processes. Reward rates and impulse rewards are defined in the SAN models based on the activity-marking reward structure to estimate NPP operation profiles. These profiles are then used to probabilistically estimate the legitimacy of the semantics and payloads of protocol frames received by I&C systems.

Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models

PubMed Central

Kravanja, Jaka; Žganec, Mario; Žganec-Gros, Jerneja; Dobrišek, Simon; Štruc, Vitomir

2016-01-01

Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors. PMID:27775570

A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.

1989-05-01

A storm transposition approach is investigated as a possible tool of assessing the frequency of extreme precipitation depths, that is, depths of return period much greater than 100 years. This paper focuses on estimation of the annual exceedance probability of extreme average precipitation depths over a catchment. The probabilistic storm transposition methodology is presented, and the several conceptual and methodological difficulties arising in this approach are identified. The method is implemented and is partially evaluated by means of a semihypothetical example involving extreme midwestern storms and two hypothetical catchments (of 100 and 1000 mi2 (˜260 and 2600 km2)) located in central Iowa. The results point out the need for further research to fully explore the potential of this approach as a tool for assessing the probabilities of rare storms, and eventually floods, a necessary element of risk-based analysis and design of large hydraulic structures.

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

Herbert, J.H.

This brief note describes the probabilistic structure of the Arps/Roberts (A/R) model of petroleum discovery. A model similar to the A/R model is derived from probabilistic propositions demonstrated to be similar to the E. Barouch/G.M. Kaufman (B/K) model, and also demonstrated to be similar to the Drew, Schuenemeyer, and Root (D/S/R) model. This note attempts to elucidate and to simplify some fundamental ideas contained in an unpublished paper by Barouch and Kaufman. This note and its predecessor paper does not attempt to address a wide variety of statistical approaches for estimating petroleum resource availability. Rather, an attempt is made tomore » draw attention to characteristics of certain methods that are commonly used, both formally and informally, to estimate a petroleum resource base for a basin or a nation. Some of these characteristics are statistical, but many are not, except in the broadest sense of the term.« less

A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.

PubMed

Dinov, Ivo D; Boscardin, John W; Mega, Michael S; Sowell, Elizabeth L; Toga, Arthur W

2005-01-01

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.

Using probabilistic terrorism risk modeling for regulatory benefit-cost analysis: application to the Western hemisphere travel initiative in the land environment.

PubMed

Willis, Henry H; LaTourrette, Tom

2008-04-01

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI-L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk-reducing effectiveness of WHTI-L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI-L, and a range of casualty cost estimates based on the willingness-to-pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI-L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14-26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5-6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit-cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.

Probabilistic finite elements for fracture and fatigue analysis

NASA Technical Reports Server (NTRS)

Liu, W. K.; Belytschko, T.; Lawrence, M.; Besterfield, G. H.

1989-01-01

The fusion of the probabilistic finite element method (PFEM) and reliability analysis for probabilistic fracture mechanics (PFM) is presented. A comprehensive method for determining the probability of fatigue failure for curved crack growth was developed. The criterion for failure or performance function is stated as: the fatigue life of a component must exceed the service life of the component; otherwise failure will occur. An enriched element that has the near-crack-tip singular strain field embedded in the element is used to formulate the equilibrium equation and solve for the stress intensity factors at the crack-tip. Performance and accuracy of the method is demonstrated on a classical mode 1 fatigue problem.

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.

Probabilistic fiber tracking of the language and motor white matter pathways of the supplementary motor area (SMA) in patients with brain tumors.

PubMed

Jenabi, Mehrnaz; Peck, Kyung K; Young, Robert J; Brennan, Nicole; Holodny, Andrei I

2014-12-01

Accurate localization of anatomically and functionally separate SMA tracts is important to improve planning prior to neurosurgery. Using fMRI and probabilistic DTI techniques, we assessed the connectivity between the frontal language area (Broca's area) and the rostral pre-SMA (language SMA) and caudal SMA proper (motor SMA). Twenty brain tumor patients completed motor and language fMRI paradigms and DTI. Peaks of functional activity in the language SMA, motor SMA and Broca's area were used to define seed regions for probabilistic tractography. fMRI and probabilistic tractography identified separate and unique pathways connecting the SMA to Broca's area - the language SMA pathway and the motor SMA pathway. For all subjects, the language SMA pathway had a larger number of voxels (P<0.0001) and higher connectivity (P<0.0001) to Broca's area than did the motor SMA pathway. In each patient, the number of voxels was greater in the language and motor SMA pathways than in background pathways (P<0.0001). No differences were found between patients with ipsilateral and those with contralateral tumors for either the language SMA pathway (degree of connectivity: P<0.36; number of voxels: 0.35) or the motor SMA pathway (degree of connectivity, P<0.28; number of voxels, P<0.74). Probabilistic tractography can identify unique white matter tracts that connect language SMA and motor SMA to Broca's area. The language SMA is more significantly connected to Broca's area than is the motor subdivision of the SMA proper. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Consumer product chemical weight fractions from ingredient lists.

PubMed

Isaacs, Kristin K; Phillips, Katherine A; Biryol, Derya; Dionisio, Kathie L; Price, Paul S

2018-05-01

Assessing human exposures to chemicals in consumer products requires composition information. However, comprehensive composition data for products in commerce are not generally available. Many consumer products have reported ingredient lists that are constructed using specific guidelines. A probabilistic model was developed to estimate quantitative weight fraction (WF) values that are consistent with the rank of an ingredient in the list, the number of reported ingredients, and labeling rules. The model provides the mean, median, and 95% upper and lower confidence limit WFs for ingredients of any rank in lists of any length. WFs predicted by the model compared favorably with those reported on Material Safety Data Sheets. Predictions for chemicals known to provide specific functions in products were also found to reasonably agree with reported WFs. The model was applied to a selection of publicly available ingredient lists, thereby estimating WFs for 1293 unique ingredients in 1123 products in 81 product categories. Predicted WFs, although less precise than reported values, can be estimated for large numbers of product-chemical combinations and thus provide a useful source of data for high-throughput or screening-level exposure assessments.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

High-Resolution Underwater Mapping Using Side-Scan Sonar

PubMed Central

2016-01-01

The goal of this study is to generate high-resolution sea floor maps using a Side-Scan Sonar(SSS). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw sensor data is corrected by means of a physics-based SSS model. Second, the data is projected to the sea-floor. The errors involved in this projection are thoroughfully analysed. Third, a probabilistic SSS model is defined and used to estimate the probability of each sea-floor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution. Finally, a geometric map building method is presented and combined with the probabilistic approach. The resulting map is composed of two layers. The echo intensity layer holds the most likely echo intensities at each point in the sea-floor. The probabilistic layer contains information about how confident can the user or the higher control layers be about the echo intensity layer data. Experimental results have been conducted in a large subsea region. PMID:26821379

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.

ProbCD: enrichment analysis accounting for categorization uncertainty.

PubMed

Vêncio, Ricardo Z N; Shmulevich, Ilya

2007-10-12

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R-based software to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation.

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

Judgment under uncertainty; a probabilistic evaluation framework for decision-making about sanitation systems in low-income countries.

PubMed

Malekpour, Shirin; Langeveld, Jeroen; Letema, Sammy; Clemens, François; van Lier, Jules B

2013-03-30

This paper introduces the probabilistic evaluation framework, to enable transparent and objective decision-making in technology selection for sanitation solutions in low-income countries. The probabilistic framework recognizes the often poor quality of the available data for evaluations. Within this framework, the evaluations will be done based on the probabilities that the expected outcomes occur in practice, considering the uncertainties in evaluation parameters. Consequently, the outcome of evaluations will not be single point estimates; but there exists a range of possible outcomes. A first trial application of this framework for evaluation of sanitation options in the Nyalenda settlement in Kisumu, Kenya, showed how the range of values that an evaluation parameter may obtain in practice would influence the evaluation outcomes. In addition, as the probabilistic evaluation requires various site-specific data, sensitivity analysis was performed to determine the influence of each data set quality on the evaluation outcomes. Based on that, data collection activities could be (re)directed, in a trade-off between the required investments in those activities and the resolution of the decisions that are to be made. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures.

PubMed

Sjöberg, C; Ahnesjö, A

2013-06-01

Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Shear-wave velocity-based probabilistic and deterministic assessment of seismic soil liquefaction potential

USGS Publications Warehouse

Kayen, R.; Moss, R.E.S.; Thompson, E.M.; Seed, R.B.; Cetin, K.O.; Der Kiureghian, A.; Tanaka, Y.; Tokimatsu, K.

2013-01-01

Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand and the soil capacity were estimated and included in the analysis, resulting in greatly reduced overall model uncertainty relative to previous studies. The presented data set and probabilistic analysis also help resolve the ancillary issues of adjustment for soil fines content and magnitude scaling factors.

Real life cost and quality of life associated with continuous intraduodenal levodopa infusion compared with oral treatment in Parkinson patients.

PubMed

Lundqvist, Christofer; Beiske, Antonie Giæver; Reiertsen, Ola; Kristiansen, Ivar Sønbø

2014-12-01

Advanced-stage Parkinson's disease (PD) strongly affects quality of life (QoL). Continuous intraduodenal administration of levodopa (IDL) is efficacious, but entails high costs. This study aims to estimate these costs in routine care. 10 patients with advanced-PD who switched from oral medication to IDL were assessed at baseline, and subsequently at 3, 6, 9 and 12 months follow-up. We used the Unified PD Rating Scale (UPDRS) for function and 15D for Quality of Life (QoL). Costs were assessed using quarterly structured patient questionnaires and hospital registries. Costs per quality adjusted life year (QALY) were estimated for conventional treatment prior to switch and for 1-year treatment with IDL. Probabilistic sensitivity analysis was based on bootstrapping. IDL significantly improved functional scores and was safe to use. One-year conventional oral treatment entailed 0.63 QALY while IDL entailed 0.68 (p > 0.05). The estimated total 1-year treatment cost was NOK419,160 on conventional treatment and NOK890,920 on IDL, representing a cost of NOK9.2 million (€1.18 mill) per additional QALY. The incremental cost per unit UPDRS improvement was NOK25,000 (€3,250). Medication was the dominant cost during IDL (45% of total costs), it represented only 6.4% of the total for conventional treatment. IDL improves function but is not cost effective using recommended thresholds for cost/QALY in Norway.

Aggregate exposure modelling of zinc pyrithione in rinse-off personal cleansing products using a person-orientated approach with market share refinement.

PubMed

Tozer, Sarah A; Kelly, Seamus; O'Mahony, Cian; Daly, E J; Nash, J F

2015-09-01

Realistic estimates of chemical aggregate exposure are needed to ensure consumer safety. As exposure estimates are a critical part of the equation used to calculate acceptable "safe levels" and conduct quantitative risk assessments, methods are needed to produce realistic exposure estimations. To this end, a probabilistic aggregate exposure model was developed to estimate consumer exposure from several rinse off personal cleansing products containing the anti-dandruff preservative zinc pyrithione. The model incorporates large habits and practices surveys, containing data on frequency of use, amount applied, co-use along with market share, and combines these data at the level of the individual based on subject demographics to better estimate exposure. The daily-applied exposure (i.e., amount applied to the skin) was 3.79 mg/kg/day for the 95th percentile consumer. The estimated internal dose for the 95th percentile exposure ranged from 0.01-1.29 μg/kg/day after accounting for retention following rinsing and dermal penetration of ZnPt. This probabilistic aggregate exposure model can be used in the human safety assessment of ingredients in multiple rinse-off technologies (e.g., shampoo, bar soap, body wash, and liquid hand soap). In addition, this model may be used in other situations where refined exposure assessment is required to support a chemical risk assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assignment of functional activations to probabilistic cytoarchitectonic areas revisited.

PubMed

Eickhoff, Simon B; Paus, Tomas; Caspers, Svenja; Grosbras, Marie-Helene; Evans, Alan C; Zilles, Karl; Amunts, Katrin

2007-07-01

Probabilistic cytoarchitectonic maps in standard reference space provide a powerful tool for the analysis of structure-function relationships in the human brain. While these microstructurally defined maps have already been successfully used in the analysis of somatosensory, motor or language functions, several conceptual issues in the analysis of structure-function relationships still demand further clarification. In this paper, we demonstrate the principle approaches for anatomical localisation of functional activations based on probabilistic cytoarchitectonic maps by exemplary analysis of an anterior parietal activation evoked by visual presentation of hand gestures. After consideration of the conceptual basis and implementation of volume or local maxima labelling, we comment on some potential interpretational difficulties, limitations and caveats that could be encountered. Extending and supplementing these methods, we then propose a supplementary approach for quantification of structure-function correspondences based on distribution analysis. This approach relates the cytoarchitectonic probabilities observed at a particular functionally defined location to the areal specific null distribution of probabilities across the whole brain (i.e., the full probability map). Importantly, this method avoids the need for a unique classification of voxels to a single cortical area and may increase the comparability between results obtained for different areas. Moreover, as distribution-based labelling quantifies the "central tendency" of an activation with respect to anatomical areas, it will, in combination with the established methods, allow an advanced characterisation of the anatomical substrates of functional activations. Finally, the advantages and disadvantages of the various methods are discussed, focussing on the question of which approach is most appropriate for a particular situation.

PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

NASA Astrophysics Data System (ADS)

Tonini, Roberto; Sandri, Laura; Anne Thompson, Mary

2015-06-01

PyBetVH is a completely new, free, open-source and cross-platform software implementation of the Bayesian Event Tree for Volcanic Hazard (BET_VH), a tool for estimating the probability of any magmatic hazardous phenomenon occurring in a selected time frame, accounting for all the uncertainties. New capabilities of this implementation include the ability to calculate hazard curves which describe the distribution of the exceedance probability as a function of intensity (e.g., tephra load) on a grid of points covering the target area. The computed hazard curves are (i) absolute (accounting for the probability of eruption in a given time frame, and for all the possible vent locations and eruptive sizes) and (ii) Bayesian (computed at different percentiles, in order to quantify the epistemic uncertainty). Such curves allow representation of the full information contained in the probabilistic volcanic hazard assessment (PVHA) and are well suited to become a main input to quantitative risk analyses. PyBetVH allows for interactive visualization of both the computed hazard curves, and the corresponding Bayesian hazard/probability maps. PyBetVH is designed to minimize the efforts of end users, making PVHA results accessible to people who may be less experienced in probabilistic methodologies, e.g. decision makers. The broad compatibility of Python language has also allowed PyBetVH to be installed on the VHub cyber-infrastructure, where it can be run online or downloaded at no cost. PyBetVH can be used to assess any type of magmatic hazard from any volcano. Here we illustrate how to perform a PVHA through PyBetVH using the example of analyzing tephra fallout from the Okataina Volcanic Centre (OVC), New Zealand, and highlight the range of outputs that the tool can generate.

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

NASA Technical Reports Server (NTRS)

Chamis, Chrisos C. (Technical Monitor); Godines, Cody Ric

2003-01-01

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

Applying a probabilistic seismic-petrophysical inversion and two different rock-physics models for reservoir characterization in offshore Nile Delta

NASA Astrophysics Data System (ADS)

Aleardi, Mattia

2018-01-01

We apply a two-step probabilistic seismic-petrophysical inversion for the characterization of a clastic, gas-saturated, reservoir located in offshore Nile Delta. In particular, we discuss and compare the results obtained when two different rock-physics models (RPMs) are employed in the inversion. The first RPM is an empirical, linear model directly derived from the available well log data by means of an optimization procedure. The second RPM is a theoretical, non-linear model based on the Hertz-Mindlin contact theory. The first step of the inversion procedure is a Bayesian linearized amplitude versus angle (AVA) inversion in which the elastic properties, and the associated uncertainties, are inferred from pre-stack seismic data. The estimated elastic properties constitute the input to the second step that is a probabilistic petrophysical inversion in which we account for the noise contaminating the recorded seismic data and the uncertainties affecting both the derived rock-physics models and the estimated elastic parameters. In particular, a Gaussian mixture a-priori distribution is used to properly take into account the facies-dependent behavior of petrophysical properties, related to the different fluid and rock properties of the different litho-fluid classes. In the synthetic and in the field data tests, the very minor differences between the results obtained by employing the two RPMs, and the good match between the estimated properties and well log information, confirm the applicability of the inversion approach and the suitability of the two different RPMs for reservoir characterization in the investigated area.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

PubMed

Mengersen, Kerrie L; Drovandi, Christopher C; Robert, Christian P; Pyne, David B; Gore, Christopher J

2016-01-01

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Individuals With OCD Lack Unrealistic Optimism Bias in Threat Estimation.

PubMed

Zetsche, Ulrike; Rief, Winfried; Exner, Cornelia

2015-07-01

Overestimating the occurrence of threatening events has been highlighted as a central cognitive factor in the maintenance of obsessive-compulsive disorder (OCD). The present study examined the different facets of this cognitive bias, its underlying mechanisms, and its specificity to OCD. For this purpose, threat estimation, probabilistic classification learning (PCL) and psychopathological measures were assessed in 23 participants with OCD, 30 participants with social phobia, and 31 healthy controls. Whereas healthy participants showed an optimistic expectation bias regarding positive and negative future events, OCD participants lacked such a bias. This lack of an optimistic expectation bias was not specific to OCD. Compared to healthy controls, OCD participants overestimated their personal risk for experiencing negative events, but did not differ from controls in their risk estimation regarding other people. Finally, OCD participants' biases in the prediction of checking-related events were associated with their impairments in learning probabilistic cue-outcome associations in a disorder-relevant context. In sum, the present results add to a growing body of research demonstrating that cognitive biases in OCD are context-dependent. Copyright © 2015. Published by Elsevier Ltd.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions.

PubMed

Kaufman, Leyla V; Wright, Mark G

2017-07-07

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments.

Assessing Probabilistic Risk Assessment Approaches for Insect Biological Control Introductions

PubMed Central

Kaufman, Leyla V.; Wright, Mark G.

2017-01-01

The introduction of biological control agents to new environments requires host specificity tests to estimate potential non-target impacts of a prospective agent. Currently, the approach is conservative, and is based on physiological host ranges determined under captive rearing conditions, without consideration for ecological factors that may influence realized host range. We use historical data and current field data from introduced parasitoids that attack an endemic Lepidoptera species in Hawaii to validate a probabilistic risk assessment (PRA) procedure for non-target impacts. We use data on known host range and habitat use in the place of origin of the parasitoids to determine whether contemporary levels of non-target parasitism could have been predicted using PRA. Our results show that reasonable predictions of potential non-target impacts may be made if comprehensive data are available from places of origin of biological control agents, but scant data produce poor predictions. Using apparent mortality data rather than marginal attack rate estimates in PRA resulted in over-estimates of predicted non-target impact. Incorporating ecological data into PRA models improved the predictive power of the risk assessments. PMID:28686180

Probabilistic atlas and geometric variability estimation to drive tissue segmentation.

PubMed

Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie

2014-09-10

Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd.

Guaranteed convergence of the Hough transform

NASA Astrophysics Data System (ADS)

Soffer, Menashe; Kiryati, Nahum

1995-01-01

The straight-line Hough Transform using normal parameterization with a continuous voting kernel is considered. It transforms the colinearity detection problem to a problem of finding the global maximum of a two dimensional function above a domain in the parameter space. The principle is similar to robust regression using fixed scale M-estimation. Unlike standard M-estimation procedures the Hough Transform does not rely on a good initial estimate of the line parameters: The global optimization problem is approached by exhaustive search on a grid that is usually as fine as computationally feasible. The global maximum of a general function above a bounded domain cannot be found by a finite number of function evaluations. Only if sufficient a-priori knowledge about the smoothness of the objective function is available, convergence to the global maximum can be guaranteed. The extraction of a-priori information and its efficient use are the main challenges in real global optimization problems. The global optimization problem in the Hough Transform is essentially how fine should the parameter space quantization be in order not to miss the true maximum. More than thirty years after Hough patented the basic algorithm, the problem is still essentially open. In this paper an attempt is made to identify a-priori information on the smoothness of the objective (Hough) function and to introduce sufficient conditions for the convergence of the Hough Transform to the global maximum. An image model with several application dependent parameters is defined. Edge point location errors as well as background noise are accounted for. Minimal parameter space quantization intervals that guarantee convergence are obtained. Focusing policies for multi-resolution Hough algorithms are developed. Theoretical support for bottom- up processing is provided. Due to the randomness of errors and noise, convergence guarantees are probabilistic.

Probabilistic structural analysis of a truss typical for space station

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.

1990-01-01

A three-bay, space, cantilever truss is probabilistically evaluated using the computer code NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) to identify and quantify the uncertainties and respective sensitivities associated with corresponding uncertainties in the primitive variables (structural, material, and loads parameters) that defines the truss. The distribution of each of these primitive variables is described in terms of one of several available distributions such as the Weibull, exponential, normal, log-normal, etc. The cumulative distribution function (CDF's) for the response functions considered and sensitivities associated with the primitive variables for given response are investigated. These sensitivities help in determining the dominating primitive variables for that response.

Abstract probabilistic CNOT gate model based on double encoding: study of the errors and physical realizability

NASA Astrophysics Data System (ADS)

Gueddana, Amor; Attia, Moez; Chatta, Rihab

2015-03-01

In this work, we study the error sources standing behind the non-perfect linear optical quantum components composing a non-deterministic quantum CNOT gate model, which performs the CNOT function with a success probability of 4/27 and uses a double encoding technique to represent photonic qubits at the control and the target. We generalize this model to an abstract probabilistic CNOT version and determine the realizability limits depending on a realistic range of the errors. Finally, we discuss physical constraints allowing the implementation of the Asymmetric Partially Polarizing Beam Splitter (APPBS), which is at the heart of correctly realizing the CNOT function.

Hazard function analysis for flood planning under nonstationarity

NASA Astrophysics Data System (ADS)

Read, Laura K.; Vogel, Richard M.

2016-05-01

The field of hazard function analysis (HFA) involves a probabilistic assessment of the "time to failure" or "return period," T, of an event of interest. HFA is used in epidemiology, manufacturing, medicine, actuarial statistics, reliability engineering, economics, and elsewhere. For a stationary process, the probability distribution function (pdf) of the return period always follows an exponential distribution, the same is not true for nonstationary processes. When the process of interest, X, exhibits nonstationary behavior, HFA can provide a complementary approach to risk analysis with analytical tools particularly useful for hydrological applications. After a general introduction to HFA, we describe a new mathematical linkage between the magnitude of the flood event, X, and its return period, T, for nonstationary processes. We derive the probabilistic properties of T for a nonstationary one-parameter exponential model of X, and then use both Monte-Carlo simulation and HFA to generalize the behavior of T when X arises from a nonstationary two-parameter lognormal distribution. For this case, our findings suggest that a two-parameter Weibull distribution provides a reasonable approximation for the pdf of T. We document how HFA can provide an alternative approach to characterize the probabilistic properties of both nonstationary flood series and the resulting pdf of T.

THE PANCHROMATIC HUBBLE ANDROMEDA TREASURY. IV. A PROBABILISTIC APPROACH TO INFERRING THE HIGH-MASS STELLAR INITIAL MASS FUNCTION AND OTHER POWER-LAW FUNCTIONS

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

Weisz, Daniel R.; Fouesneau, Morgan; Dalcanton, Julianne J.

2013-01-10

We present a probabilistic approach for inferring the parameters of the present-day power-law stellar mass function (MF) of a resolved young star cluster. This technique (1) fully exploits the information content of a given data set; (2) can account for observational uncertainties in a straightforward way; (3) assigns meaningful uncertainties to the inferred parameters; (4) avoids the pitfalls associated with binning data; and (5) can be applied to virtually any resolved young cluster, laying the groundwork for a systematic study of the high-mass stellar MF (M {approx}> 1 M {sub Sun }). Using simulated clusters and Markov Chain Monte Carlomore » sampling of the probability distribution functions, we show that estimates of the MF slope, {alpha}, are unbiased and that the uncertainty, {Delta}{alpha}, depends primarily on the number of observed stars and on the range of stellar masses they span, assuming that the uncertainties on individual masses and the completeness are both well characterized. Using idealized mock data, we compute the theoretical precision, i.e., lower limits, on {alpha}, and provide an analytic approximation for {Delta}{alpha} as a function of the observed number of stars and mass range. Comparison with literature studies shows that {approx}3/4 of quoted uncertainties are smaller than the theoretical lower limit. By correcting these uncertainties to the theoretical lower limits, we find that the literature studies yield ({alpha}) = 2.46, with a 1{sigma} dispersion of 0.35 dex. We verify that it is impossible for a power-law MF to obtain meaningful constraints on the upper mass limit of the initial mass function, beyond the lower bound of the most massive star actually observed. We show that avoiding substantial biases in the MF slope requires (1) including the MF as a prior when deriving individual stellar mass estimates, (2) modeling the uncertainties in the individual stellar masses, and (3) fully characterizing and then explicitly modeling the completeness for stars of a given mass. The precision on MF slope recovery in this paper are lower limits, as we do not explicitly consider all possible sources of uncertainty, including dynamical effects (e.g., mass segregation), unresolved binaries, and non-coeval populations. We briefly discuss how each of these effects can be incorporated into extensions of the present framework. Finally, we emphasize that the technique and lessons learned are applicable to more general problems involving power-law fitting.« less

Noise-induced extinction for a ratio-dependent predator-prey model with strong Allee effect in prey

NASA Astrophysics Data System (ADS)

Mandal, Partha Sarathi

2018-04-01

In this paper, we study a stochastically forced ratio-dependent predator-prey model with strong Allee effect in prey population. In the deterministic case, we show that the model exhibits the stable interior equilibrium point or limit cycle corresponding to the co-existence of both species. We investigate a probabilistic mechanism of the noise-induced extinction in a zone of stable interior equilibrium point. Computational methods based on the stochastic sensitivity function technique are applied for the analysis of the dispersion of random states near stable interior equilibrium point. This method allows to construct a confidence domain and estimate the threshold value of the noise intensity for a transition from the coexistence to the extinction.

The DOZZ formula from the path integral

NASA Astrophysics Data System (ADS)

Kupiainen, Antti; Rhodes, Rémi; Vargas, Vincent

2018-05-01

We present a rigorous proof of the Dorn, Otto, Zamolodchikov, Zamolodchikov formula (the DOZZ formula) for the 3 point structure constants of Liouville Conformal Field Theory (LCFT) starting from a rigorous probabilistic construction of the functional integral defining LCFT given earlier by the authors and David. A crucial ingredient in our argument is a probabilistic derivation of the reflection relation in LCFT based on a refined tail analysis of Gaussian multiplicative chaos measures.

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.

Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

NASA Astrophysics Data System (ADS)

Chen, Xiao; Li, Yaan; Yu, Jing; Li, Yuxing

2018-01-01

For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.

Statistical physics of medical diagnostics: Study of a probabilistic model.

PubMed

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.

Probabilistic Assessment of High-Throughput Wireless Sensor Networks

PubMed Central

Kim, Robin E.; Mechitov, Kirill; Sim, Sung-Han; Spencer, Billie F.; Song, Junho

2016-01-01

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved. PMID:27258270

Statistical physics of medical diagnostics: Study of a probabilistic model

NASA Astrophysics Data System (ADS)

Mashaghi, Alireza; Ramezanpour, Abolfazl

2018-03-01

We study a diagnostic strategy which is based on the anticipation of the diagnostic process by simulation of the dynamical process starting from the initial findings. We show that such a strategy could result in more accurate diagnoses compared to a strategy that is solely based on the direct implications of the initial observations. We demonstrate this by employing the mean-field approximation of statistical physics to compute the posterior disease probabilities for a given subset of observed signs (symptoms) in a probabilistic model of signs and diseases. A Monte Carlo optimization algorithm is then used to maximize an objective function of the sequence of observations, which favors the more decisive observations resulting in more polarized disease probabilities. We see how the observed signs change the nature of the macroscopic (Gibbs) states of the sign and disease probability distributions. The structure of these macroscopic states in the configuration space of the variables affects the quality of any approximate inference algorithm (so the diagnostic performance) which tries to estimate the sign-disease marginal probabilities. In particular, we find that the simulation (or extrapolation) of the diagnostic process is helpful when the disease landscape is not trivial and the system undergoes a phase transition to an ordered phase.