Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

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

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

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

A Mediation Model to Explain the Role of Mathematics Skills and Probabilistic Reasoning on Statistics Achievement

ERIC Educational Resources Information Center

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

2016-01-01

Among the wide range of factors related to the acquisition of statistical knowledge, competence in basic mathematics, including basic probability, has received much attention. In this study, a mediation model was estimated to derive the total, direct, and indirect effects of mathematical competence on statistics achievement taking into account…

75 FR 18205 - Notice of Peer Review Meeting for the External Peer Review Drafts of Two Documents on Using...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-09

... Role of Risk Analysis in Decision-Making AGENCY: Environmental Protection Agency (EPA). ACTION: Notice... documents entitled, ``Using Probabilistic Methods to Enhance the Role of Risk Analysis in Decision- Making... Probabilistic Methods to Enhance the Role of Risk Analysis in Decision-Making, with Case Study Examples'' and...

Understanding Probabilistic Interpretations of Physical Systems: A Prerequisite to Learning Quantum Physics.

ERIC Educational Resources Information Center

Bao, Lei; Redish, Edward F.

2002-01-01

Explains the critical role of probability in making sense of quantum physics and addresses the difficulties science and engineering undergraduates experience in helping students build a model of how to think about probability in physical systems. (Contains 17 references.) (Author/YDS)

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

PubMed Central

2017-01-01

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

Probabilistic Fracture Mechanics of Reactor Pressure Vessels with Populations of Flaws

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

Spencer, Benjamin; Backman, Marie; Williams, Paul

This report documents recent progress in developing a tool that uses the Grizzly and RAVEN codes to perform probabilistic fracture mechanics analyses of reactor pressure vessels in light water reactor nuclear power plants. The Grizzly code is being developed with the goal of creating a general tool that can be applied to study a variety of degradation mechanisms in nuclear power plant components. Because of the central role of the reactor pressure vessel (RPV) in a nuclear power plant, particular emphasis is being placed on developing capabilities to model fracture in embrittled RPVs to aid in the process surrounding decisionmore » making relating to life extension of existing plants. A typical RPV contains a large population of pre-existing flaws introduced during the manufacturing process. The use of probabilistic techniques is necessary to assess the likelihood of crack initiation at one or more of these flaws during a transient event. This report documents development and initial testing of a capability to perform probabilistic fracture mechanics of large populations of flaws in RPVs using reduced order models to compute fracture parameters. The work documented here builds on prior efforts to perform probabilistic analyses of a single flaw with uncertain parameters, as well as earlier work to develop deterministic capabilities to model the thermo-mechanical response of the RPV under transient events, and compute fracture mechanics parameters at locations of pre-defined flaws. The capabilities developed as part of this work provide a foundation for future work, which will develop a platform that provides the flexibility needed to consider scenarios that cannot be addressed with the tools used in current practice.« less

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

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

Ho, Clifford Kuofei

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

A model-based test for treatment effects with probabilistic classifications.

PubMed

Cavagnaro, Daniel R; Davis-Stober, Clintin P

2018-05-21

Within modern psychology, computational and statistical models play an important role in describing a wide variety of human behavior. Model selection analyses are typically used to classify individuals according to the model(s) that best describe their behavior. These classifications are inherently probabilistic, which presents challenges for performing group-level analyses, such as quantifying the effect of an experimental manipulation. We answer this challenge by presenting a method for quantifying treatment effects in terms of distributional changes in model-based (i.e., probabilistic) classifications across treatment conditions. The method uses hierarchical Bayesian mixture modeling to incorporate classification uncertainty at the individual level into the test for a treatment effect at the group level. We illustrate the method with several worked examples, including a reanalysis of the data from Kellen, Mata, and Davis-Stober (2017), and analyze its performance more generally through simulation studies. Our simulations show that the method is both more powerful and less prone to type-1 errors than Fisher's exact test when classifications are uncertain. In the special case where classifications are deterministic, we find a near-perfect power-law relationship between the Bayes factor, derived from our method, and the p value obtained from Fisher's exact test. We provide code in an online supplement that allows researchers to apply the method to their own data. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Probabilistic topic modeling for the analysis and classification of genomic sequences

PubMed Central

2015-01-01

Background Studies on genomic sequences for classification and taxonomic identification have a leading role in the biomedical field and in the analysis of biodiversity. These studies are focusing on the so-called barcode genes, representing a well defined region of the whole genome. Recently, alignment-free techniques are gaining more importance because they are able to overcome the drawbacks of sequence alignment techniques. In this paper a new alignment-free method for DNA sequences clustering and classification is proposed. The method is based on k-mers representation and text mining techniques. Methods The presented method is based on Probabilistic Topic Modeling, a statistical technique originally proposed for text documents. Probabilistic topic models are able to find in a document corpus the topics (recurrent themes) characterizing classes of documents. This technique, applied on DNA sequences representing the documents, exploits the frequency of fixed-length k-mers and builds a generative model for a training group of sequences. This generative model, obtained through the Latent Dirichlet Allocation (LDA) algorithm, is then used to classify a large set of genomic sequences. Results and conclusions We performed classification of over 7000 16S DNA barcode sequences taken from Ribosomal Database Project (RDP) repository, training probabilistic topic models. The proposed method is compared to the RDP tool and Support Vector Machine (SVM) classification algorithm in a extensive set of trials using both complete sequences and short sequence snippets (from 400 bp to 25 bp). Our method reaches very similar results to RDP classifier and SVM for complete sequences. The most interesting results are obtained when short sequence snippets are considered. In these conditions the proposed method outperforms RDP and SVM with ultra short sequences and it exhibits a smooth decrease of performance, at every taxonomic level, when the sequence length is decreased. PMID:25916734

Linguistic Constraints on Statistical Word Segmentation: The Role of Consonants in Arabic and English

ERIC Educational Resources Information Center

Kastner, Itamar; Adriaans, Frans

2018-01-01

Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known…

Global/local methods for probabilistic structural analysis

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Global/local methods for probabilistic structural analysis

NASA Astrophysics Data System (ADS)

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

1993-04-01

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

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.

A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks

PubMed Central

Yin, Junming; Ho, Qirong; Xing, Eric P.

2014-01-01

We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487

Data Assimilation - Advances and Applications

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

Williams, Brian J.

2014-07-30

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batchmore » sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.« less

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

ERIC Educational Resources Information Center

Chen, Xin

2012-01-01

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

The Role of Probability in Developing Learners' Models of Simulation Approaches to Inference

ERIC Educational Resources Information Center

Lee, Hollylynne S.; Doerr, Helen M.; Tran, Dung; Lovett, Jennifer N.

2016-01-01

Repeated sampling approaches to inference that rely on simulations have recently gained prominence in statistics education, and probabilistic concepts are at the core of this approach. In this approach, learners need to develop a mapping among the problem situation, a physical enactment, computer representations, and the underlying randomization…

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.

Template-based protein structure modeling using the RaptorX web server.

PubMed

Källberg, Morten; Wang, Haipeng; Wang, Sheng; Peng, Jian; Wang, Zhiyong; Lu, Hui; Xu, Jinbo

2012-07-19

A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ~35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ~6,000 sequences submitted by ~1,600 users from around the world.

RaptorX server: a resource for template-based protein structure modeling.

PubMed

Källberg, Morten; Margaryan, Gohar; Wang, Sheng; Ma, Jianzhu; Xu, Jinbo

2014-01-01

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server ( http://raptorx.uchicago.edu ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling.Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min.

Template-based protein structure modeling using the RaptorX web server

PubMed Central

Källberg, Morten; Wang, Haipeng; Wang, Sheng; Peng, Jian; Wang, Zhiyong; Lu, Hui; Xu, Jinbo

2016-01-01

A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ~35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ~6,000 sequences submitted by ~1,600 users from around the world. PMID:22814390

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

PubMed Central

Franke, Michael; Degen, Judith

2016-01-01

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

A Probabilistic Approach to Model Update

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Voracek, David F.

2001-01-01

Finite element models are often developed for load validation, structural certification, response predictions, and to study alternate design concepts. In rare occasions, models developed with a nominal set of parameters agree with experimental data without the need to update parameter values. Today, model updating is generally heuristic and often performed by a skilled analyst with in-depth understanding of the model assumptions. Parameter uncertainties play a key role in understanding the model update problem and therefore probabilistic analysis tools, developed for reliability and risk analysis, may be used to incorporate uncertainty in the analysis. In this work, probability analysis (PA) tools are used to aid the parameter update task using experimental data and some basic knowledge of potential error sources. Discussed here is the first application of PA tools to update parameters of a finite element model for a composite wing structure. Static deflection data at six locations are used to update five parameters. It is shown that while prediction of individual response values may not be matched identically, the system response is significantly improved with moderate changes in parameter values.

Probabilistic models of eukaryotic evolution: time for integration

PubMed Central

Lartillot, Nicolas

2015-01-01

In spite of substantial work and recent progress, a global and fully resolved picture of the macroevolutionary history of eukaryotes is still under construction. This concerns not only the phylogenetic relations among major groups, but also the general characteristics of the underlying macroevolutionary processes, including the patterns of gene family evolution associated with endosymbioses, as well as their impact on the sequence evolutionary process. All these questions raise formidable methodological challenges, calling for a more powerful statistical paradigm. In this direction, model-based probabilistic approaches have played an increasingly important role. In particular, improved models of sequence evolution accounting for heterogeneities across sites and across lineages have led to significant, although insufficient, improvement in phylogenetic accuracy. More recently, one main trend has been to move away from simple parametric models and stepwise approaches, towards integrative models explicitly considering the intricate interplay between multiple levels of macroevolutionary processes. Such integrative models are in their infancy, and their application to the phylogeny of eukaryotes still requires substantial improvement of the underlying models, as well as additional computational developments. PMID:26323768

A Probabilistic Approach for Real-Time Volcano Surveillance

NASA Astrophysics Data System (ADS)

Cannavo, F.; Cannata, A.; Cassisi, C.; Di Grazia, G.; Maronno, P.; Montalto, P.; Prestifilippo, M.; Privitera, E.; Gambino, S.; Coltelli, M.

2016-12-01

Continuous evaluation of the state of potentially dangerous volcanos plays a key role for civil protection purposes. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the coupling of highly non-linear and complex volcanic dynamic processes leads to measurable effects that can show a large variety of different behaviors. Moreover, due to intrinsic uncertainties and possible failures in some recorded data, the volcano state needs to be expressed in probabilistic terms, thus making the fast volcano state assessment sometimes impracticable for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, we present a probabilistic graphical model to estimate automatically the ongoing volcano state from all the available different kind of measurements. The model consists of a Bayesian network able to represent a set of variables and their conditional dependencies via a directed acyclic graph. The model variables are both the measurements and the possible states of the volcano through the time. The model output is an estimation of the probability distribution of the feasible volcano states. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision making purposes.

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.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

Mørk, Søren; Holmes, Ian

2012-03-01

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

Integration of Advanced Probabilistic Analysis Techniques with Multi-Physics Models

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

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

2014-07-30

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

Probabilistic structural analysis methods of hot engine structures

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

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

NASA Astrophysics Data System (ADS)

Gromek, Katherine Emily

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

Application of Probabilistic Analysis to Aircraft Impact Dynamics

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

NASA Astrophysics Data System (ADS)

Rabatel, Matthias; Rampal, Pierre; Carrassi, Alberto; Bertino, Laurent; Jones, Christopher K. R. T.

2018-03-01

We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the free-drift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

From Cyclone Tracks to the Costs of European Winter Storms: A Probabilistic Loss Assessment Model

NASA Astrophysics Data System (ADS)

Orwig, K.; Renggli, D.; Corti, T.; Reese, S.; Wueest, M.; Viktor, E.; Zimmerli, P.

2014-12-01

European winter storms cause billions of dollars of insured losses every year. Therefore, it is essential to understand potential impacts of future events, and the role reinsurance can play to mitigate the losses. The authors will present an overview on natural catastrophe risk assessment modeling in the reinsurance industry, and the development of a new innovative approach for modeling the risk associated with European winter storms.The new innovative approach includes the development of physically meaningful probabilistic (i.e. simulated) events for European winter storm loss assessment. The meteorological hazard component of the new model is based on cyclone and windstorm tracks identified in the 20thCentury Reanalysis data. The knowledge of the evolution of winter storms both in time and space allows the physically meaningful perturbation of historical event properties (e.g. track, intensity, etc.). The perturbation includes a random element but also takes the local climatology and the evolution of the historical event into account.The low-resolution wind footprints taken from the 20thCentury Reanalysis are processed by a statistical-dynamical downscaling to generate high-resolution footprints for both the simulated and historical events. Downscaling transfer functions are generated using ENSEMBLES regional climate model data. The result is a set of reliable probabilistic events representing thousands of years. The event set is then combined with country and site-specific vulnerability functions and detailed market- or client-specific information to compute annual expected losses.

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.

Process for computing geometric perturbations for probabilistic analysis

DOEpatents

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

2012-04-10

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

Probabilistic modeling of discourse-aware sentence processing.

PubMed

Dubey, Amit; Keller, Frank; Sturt, Patrick

2013-07-01

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

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.

Improved water allocation utilizing probabilistic climate forecasts: Short-term water contracts in a risk management framework

NASA Astrophysics Data System (ADS)

Sankarasubramanian, A.; Lall, Upmanu; Souza Filho, Francisco Assis; Sharma, Ashish

2009-11-01

Probabilistic, seasonal to interannual streamflow forecasts are becoming increasingly available as the ability to model climate teleconnections is improving. However, water managers and practitioners have been slow to adopt such products, citing concerns with forecast skill. Essentially, a management risk is perceived in "gambling" with operations using a probabilistic forecast, while a system failure upon following existing operating policies is "protected" by the official rules or guidebook. In the presence of a prescribed system of prior allocation of releases under different storage or water availability conditions, the manager has little incentive to change. Innovation in allocation and operation is hence key to improved risk management using such forecasts. A participatory water allocation process that can effectively use probabilistic forecasts as part of an adaptive management strategy is introduced here. Users can express their demand for water through statements that cover the quantity needed at a particular reliability, the temporal distribution of the "allocation," the associated willingness to pay, and compensation in the event of contract nonperformance. The water manager then assesses feasible allocations using the probabilistic forecast that try to meet these criteria across all users. An iterative process between users and water manager could be used to formalize a set of short-term contracts that represent the resulting prioritized water allocation strategy over the operating period for which the forecast was issued. These contracts can be used to allocate water each year/season beyond long-term contracts that may have precedence. Thus, integrated supply and demand management can be achieved. In this paper, a single period multiuser optimization model that can support such an allocation process is presented. The application of this conceptual model is explored using data for the Jaguaribe Metropolitan Hydro System in Ceara, Brazil. The performance relative to the current allocation process is assessed in the context of whether such a model could support the proposed short-term contract based participatory process. A synthetic forecasting example is also used to explore the relative roles of forecast skill and reservoir storage in this framework.

A New Scheme for Probabilistic Teleportation and Its Potential Applications

NASA Astrophysics Data System (ADS)

Wei, Jia-Hua; Dai, Hong-Yi; Zhang, Ming

2013-12-01

We propose a novel scheme to probabilistically teleport an unknown two-level quantum state when the information of the partially entangled state is only available for the sender. This is in contrast with the fact that the receiver must know the non-maximally entangled state in previous typical schemes for the teleportation. Additionally, we illustrate two potential applications of the novel scheme for probabilistic teleportation from a sender to a receiver with the help of an assistant, who plays distinct roles under different communication conditions, and our results show that the novel proposal could enlarge the applied range of probabilistic teleportation.

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

PubMed

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

2012-02-01

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

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

PubMed Central

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

2012-01-01

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

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

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

Fuhr, N.; Roelleke, T.

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

Probabilistic Structural Analysis Theory Development

NASA Technical Reports Server (NTRS)

Burnside, O. H.

1985-01-01

The objective of the Probabilistic Structural Analysis Methods (PSAM) project is to develop analysis techniques and computer programs for predicting the probabilistic response of critical structural components for current and future space propulsion systems. This technology will play a central role in establishing system performance and durability. The first year's technical activity is concentrating on probabilistic finite element formulation strategy and code development. Work is also in progress to survey critical materials and space shuttle mian engine components. The probabilistic finite element computer program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) is being developed. The final probabilistic code will have, in the general case, the capability of performing nonlinear dynamic of stochastic structures. It is the goal of the approximate methods effort to increase problem solving efficiency relative to finite element methods by using energy methods to generate trial solutions which satisfy the structural boundary conditions. These approximate methods will be less computer intensive relative to the finite element approach.

Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories.

PubMed

Chikalov, Igor; Yao, Peggy; Moshkov, Mikhail; Latombe, Jean-Claude

2011-02-15

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. This paper describes inductive learning methods to train protein-independent probabilistic models of H-bond stability from molecular dynamics (MD) simulation trajectories of various proteins. The training data contains 32 input attributes (predictors) that describe an H-bond and its local environment in a conformation c and the output attribute is the probability that the H-bond will be present in an arbitrary conformation of this protein achievable from c within a time duration Δ. We model dependence of the output variable on the predictors by a regression tree. Several models are built using 6 MD simulation trajectories containing over 4000 distinct H-bonds (millions of occurrences). Experimental results demonstrate that such models can predict H-bond stability quite well. They perform roughly 20% better than models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a conformation. In most tests, about 80% of the 10% H-bonds predicted as the least stable are actually among the 10% truly least stable. The important attributes identified during the tree construction are consistent with previous findings. We use inductive learning methods to build protein-independent probabilistic models to study H-bond stability, and demonstrate that the models perform better than H-bond energy alone.

A probabilistic and continuous model of protein conformational space for template-free modeling.

PubMed

Zhao, Feng; Peng, Jian; Debartolo, Joe; Freed, Karl F; Sosnick, Tobin R; Xu, Jinbo

2010-06-01

One of the major challenges with protein template-free modeling is an efficient sampling algorithm that can explore a huge conformation space quickly. The popular fragment assembly method constructs a conformation by stringing together short fragments extracted from the Protein Data Base (PDB). The discrete nature of this method may limit generated conformations to a subspace in which the native fold does not belong. Another worry is that a protein with really new fold may contain some fragments not in the PDB. This article presents a probabilistic model of protein conformational space to overcome the above two limitations. This probabilistic model employs directional statistics to model the distribution of backbone angles and 2(nd)-order Conditional Random Fields (CRFs) to describe sequence-angle relationship. Using this probabilistic model, we can sample protein conformations in a continuous space, as opposed to the widely used fragment assembly and lattice model methods that work in a discrete space. We show that when coupled with a simple energy function, this probabilistic method compares favorably with the fragment assembly method in the blind CASP8 evaluation, especially on alpha or small beta proteins. To our knowledge, this is the first probabilistic method that can search conformations in a continuous space and achieves favorable performance. Our method also generated three-dimensional (3D) models better than template-based methods for a couple of CASP8 hard targets. The method described in this article can also be applied to protein loop modeling, model refinement, and even RNA tertiary structure prediction.

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

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

NASA Technical Reports Server (NTRS)

Dezfuli, Homayoon; Kelly, Dana; Smith, Curtis; Vedros, Kurt; Galyean, William

2009-01-01

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

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

NASA Astrophysics Data System (ADS)

Chhaibi, Reda

2013-02-01

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

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

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Analysing uncertainties of supply and demand in the future use of hydrogen as an energy vector

NASA Astrophysics Data System (ADS)

Lenel, U. R.; Davies, D. G. S.; Moore, M. A.

An analytical technique (Analysis with Uncertain Qualities), developed at Fulmer, is being used to examine the sensitivity of the outcome to uncertainties in input quantities in order to highlight which input quantities critically affect the potential role of hydrogen. The work presented here includes an outline of the model and the analysis technique, along with basic considerations of the input quantities to the model (demand, supply and constraints). Some examples are given of probabilistic estimates of input quantities.

Probabilistic models of cognition: conceptual foundations.

PubMed

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

2006-07-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

PubMed

Martin, Sébastien; Troccaz, Jocelyne; Daanenc, Vincent

2010-04-01

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

Probabilistic load simulation: Code development status

NASA Astrophysics Data System (ADS)

Newell, J. F.; Ho, H.

1991-05-01

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

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.

Vagueness as Probabilistic Linguistic Knowledge

NASA Astrophysics Data System (ADS)

Lassiter, Daniel

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

Probalistic Models for Solar Particle Events

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

EFFECTS OF CORRELATED PROBABILISTIC EXPOSURE MODEL INPUTS ON SIMULATED RESULTS

EPA Science Inventory

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

Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra.

PubMed

Claxton, Karl; Sculpher, Mark; McCabe, Chris; Briggs, Andrew; Akehurst, Ron; Buxton, Martin; Brazier, John; O'Hagan, Tony

2005-04-01

Recently the National Institute for Clinical Excellence (NICE) updated its methods guidance for technology assessment. One aspect of the new guidance is to require the use of probabilistic sensitivity analysis with all cost-effectiveness models submitted to the Institute. The purpose of this paper is to place the NICE guidance on dealing with uncertainty into a broader context of the requirements for decision making; to explain the general approach that was taken in its development; and to address each of the issues which have been raised in the debate about the role of probabilistic sensitivity analysis in general. The most appropriate starting point for developing guidance is to establish what is required for decision making. On the basis of these requirements, the methods and framework of analysis which can best meet these needs can then be identified. It will be argued that the guidance on dealing with uncertainty and, in particular, the requirement for probabilistic sensitivity analysis, is justified by the requirements of the type of decisions that NICE is asked to make. Given this foundation, the main issues and criticisms raised during and after the consultation process are reviewed. Finally, some of the methodological challenges posed by the need fully to characterise decision uncertainty and to inform the research agenda will be identified and discussed. Copyright (c) 2005 John Wiley & Sons, Ltd.

Probabilistic approach for earthquake scenarios in the Marmara region from dynamic rupture simulations

NASA Astrophysics Data System (ADS)

Aochi, Hideo

2014-05-01

The Marmara region (Turkey) along the North Anatolian fault is known as a high potential of large earthquakes in the next decades. For the purpose of seismic hazard/risk evaluation, kinematic and dynamic source models have been proposed (e.g. Oglesby and Mai, GJI, 2012). In general, the simulated earthquake scenarios depend on the hypothesis and cannot be verified before the expected earthquake. We then introduce a probabilistic insight to give the initial/boundary conditions to statistically analyze the simulated scenarios. We prepare different fault geometry models, tectonic loading and hypocenter locations. We keep the same framework of the simulation procedure as the dynamic rupture process of the adjacent 1999 Izmit earthquake (Aochi and Madariaga, BSSA, 2003), as the previous models were able to reproduce the seismological/geodetic aspects of the event. Irregularities in fault geometry play a significant role to control the rupture progress, and a relatively large change in geometry may work as barriers. The variety of the simulate earthquake scenarios should be useful for estimating the variety of the expected ground motion.

Using Markov chains of nucleotide sequences as a possible precursor to predict functional roles of human genome: a case study on inactive chromatin regions.

PubMed

Lee, K-E; Lee, E-J; Park, H-S

2016-08-30

Recent advances in computational epigenetics have provided new opportunities to evaluate n-gram probabilistic language models. In this paper, we describe a systematic genome-wide approach for predicting functional roles in inactive chromatin regions by using a sequence-based Markovian chromatin map of the human genome. We demonstrate that Markov chains of sequences can be used as a precursor to predict functional roles in heterochromatin regions and provide an example comparing two publicly available chromatin annotations of large-scale epigenomics projects: ENCODE project consortium and Roadmap Epigenomics consortium.

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

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

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

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

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

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

DOT National Transportation Integrated Search

2009-10-13

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

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

NASA Astrophysics Data System (ADS)

Fei, Cheng-Wei; Bai, Guang-Chen

2014-12-01

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

Superposition-Based Analysis of First-Order Probabilistic Timed Automata

NASA Astrophysics Data System (ADS)

Fietzke, Arnaud; Hermanns, Holger; Weidenbach, Christoph

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

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

PubMed

Campbell, Kieran R; Yau, Christopher

2017-03-15

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

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

PubMed

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

2017-12-01

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

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

USGS Publications Warehouse

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

2009-01-01

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

Distinct Roles of Dopamine and Subthalamic Nucleus in Learning and Probabilistic Decision Making

ERIC Educational Resources Information Center

Coulthard, Elizabeth J.; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K.; Murphy, Gillian; Keeley, Sophie; Whone, Alan L.

2012-01-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making…

Error Discounting in Probabilistic Category Learning

PubMed Central

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

2011-01-01

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

Probabilistic interpretation of Peelle's pertinent puzzle and its resolution

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

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

2004-01-01

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

Probabilistic Interpretation of Peelle's Pertinent Puzzle and its Resolution

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

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

2005-05-24

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

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

PubMed

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

1999-01-01

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

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

ERIC Educational Resources Information Center

Bodoff, David

1999-01-01

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

A PROBABILISTIC POPULATION EXPOSURE MODEL FOR PM10 AND PM 2.5

EPA Science Inventory

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

The Role of Probabilistic Design Analysis Methods in Safety and Affordability

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.

2016-01-01

For the last several years, NASA and its contractors have been working together to build space launch systems to commercialize space. Developing commercial affordable and safe launch systems becomes very important and requires a paradigm shift. This paradigm shift enforces the need for an integrated systems engineering environment where cost, safety, reliability, and performance need to be considered to optimize the launch system design. In such an environment, rule based and deterministic engineering design practices alone may not be sufficient to optimize margins and fault tolerance to reduce cost. As a result, introduction of Probabilistic Design Analysis (PDA) methods to support the current deterministic engineering design practices becomes a necessity to reduce cost without compromising reliability and safety. This paper discusses the importance of PDA methods in NASA's new commercial environment, their applications, and the key role they can play in designing reliable, safe, and affordable launch systems. More specifically, this paper discusses: 1) The involvement of NASA in PDA 2) Why PDA is needed 3) A PDA model structure 4) A PDA example application 5) PDA link to safety and affordability.

The probabilistic nature of preferential choice.

PubMed

Rieskamp, Jörg

2008-11-01

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

A Probabilistic Palimpsest Model of Visual Short-term Memory

PubMed Central

Matthey, Loic; Bays, Paul M.; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ. PMID:25611204

A probabilistic palimpsest model of visual short-term memory.

PubMed

Matthey, Loic; Bays, Paul M; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ.

Probabilistic Prediction of Lifetimes of Ceramic Parts

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

Probabilistic structural analysis of aerospace components using NESSUS

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Nagpal, Vinod K.; Chamis, Christos C.

1988-01-01

Probabilistic structural analysis of a Space Shuttle main engine turbopump blade is conducted using the computer code NESSUS (numerical evaluation of stochastic structures under stress). The goal of the analysis is to derive probabilistic characteristics of blade response given probabilistic descriptions of uncertainties in blade geometry, material properties, and temperature and pressure distributions. Probability densities are derived for critical blade responses. Risk assessment and failure life analysis is conducted assuming different failure models.

Cognitive Development Effects of Teaching Probabilistic Decision Making to Middle School Students

ERIC Educational Resources Information Center

Mjelde, James W.; Litzenberg, Kerry K.; Lindner, James R.

2011-01-01

This study investigated the comprehension and effectiveness of teaching formal, probabilistic decision-making skills to middle school students. Two specific objectives were to determine (1) if middle school students can comprehend a probabilistic decision-making approach, and (2) if exposure to the modeling approaches improves middle school…

The Terrestrial Investigation Model: A probabilistic risk assessment model for birds exposed to pesticides

EPA Science Inventory

One of the major recommendations of the National Academy of Science to the USEPA, NMFS and USFWS was to utilize probabilistic methods when assessing the risks of pesticides to federally listed endangered and threatened species. The Terrestrial Investigation Model (TIM, version 3....

Exploring Term Dependences in Probabilistic Information Retrieval Model.

ERIC Educational Resources Information Center

Cho, Bong-Hyun; Lee, Changki; Lee, Gary Geunbae

2003-01-01

Describes a theoretic process to apply Bahadur-Lazarsfeld expansion (BLE) to general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on two standard document collections, one in Korean and one in English, it is demonstrated that incorporation of term dependences using BLE significantly contributes to performance…

A Probabilistic Model of Phonological Relationships from Contrast to Allophony

ERIC Educational Resources Information Center

Hall, Kathleen Currie

2009-01-01

This dissertation proposes a model of phonological relationships, the Probabilistic Phonological Relationship Model (PPRM), that quantifies how predictably distributed two sounds in a relationship are. It builds on a core premise of traditional phonological analysis, that the ability to define phonological relationships such as contrast and…

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi

PubMed Central

Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni

2016-01-01

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a “specialized” domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the “community structure” of the ToH and their difficulties in executing so-called “counterintuitive” movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand—a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits. PMID:27074140

Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi.

PubMed

Donnarumma, Francesco; Maisto, Domenico; Pezzulo, Giovanni

2016-04-01

How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.

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

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.

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

NASA Astrophysics Data System (ADS)

Jin, Li; Zhang, Guoan; Zhu, Xiaojun

2017-07-01

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

A 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.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

PubMed

Pecevski, Dejan; Maass, Wolfgang

2016-01-01

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

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

PubMed Central

Pecevski, Dejan

2016-01-01

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

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

PubMed Central

Murabito, Ettore; Verma, Malkhey; Bekker, Martijn; Bellomo, Domenico; Westerhoff, Hans V.; Teusink, Bas; Steuer, Ralf

2014-01-01

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models. PMID:25268481

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…

The Stag Hunt Game: An Example of an Excel-Based Probabilistic Game

ERIC Educational Resources Information Center

Bridge, Dave

2016-01-01

With so many role-playing simulations already in the political science education literature, the recent repeated calls for new games is both timely and appropriate. This article answers and extends those calls by advocating the creation of probabilistic games using Microsoft Excel. I introduce the example of the Stag Hunt Game--a short, effective,…

A PROBABILISTIC MODELING FRAMEWORK FOR PREDICTING POPULATION EXPOSURES TO BENZENE

EPA Science Inventory

The US Environmental Protection Agency (EPA) is modifying their probabilistic Stochastic Human Exposure Dose Simulation (SHEDS) model to assess aggregate exposures to air toxics. Air toxics include urban Hazardous Air Pollutants (HAPS) such as benzene from mobile sources, part...

A new discriminative kernel from probabilistic models.

PubMed

Tsuda, Koji; Kawanabe, Motoaki; Rätsch, Gunnar; Sonnenburg, Sören; Müller, Klaus-Robert

2002-10-01

Recently, Jaakkola and Haussler (1999) proposed a method for constructing kernel functions from probabilistic models. Their so-called Fisher kernel has been combined with discriminative classifiers such as support vector machines and applied successfully in, for example, DNA and protein analysis. Whereas the Fisher kernel is calculated from the marginal log-likelihood, we propose the TOP kernel derived; from tangent vectors of posterior log-odds. Furthermore, we develop a theoretical framework on feature extractors from probabilistic models and use it for analyzing the TOP kernel. In experiments, our new discriminative TOP kernel compares favorably to the Fisher kernel.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Probabilistic Usage of the Multi-Factor Interaction Model

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A Multi-Factor Interaction Model (MFIM) is used to predict the insulating foam mass expulsion during the ascending of a space vehicle. The exponents in the MFIM are evaluated by an available approach which consists of least squares and an optimization algorithm. These results were subsequently used to probabilistically evaluate the effects of the uncertainties in each participating factor in the mass expulsion. The probabilistic results show that the surface temperature dominates at high probabilities and the pressure which causes the mass expulsion at low probabil

Integration of RAMS in LCC analysis for linear transport infrastructures. A case study for railways.

NASA Astrophysics Data System (ADS)

Calle-Cordón, Álvaro; Jiménez-Redondo, Noemi; Morales-Gámiz, F. J.; García-Villena, F. A.; Garmabaki, Amir H. S.; Odelius, Johan

2017-09-01

Life-cycle cost (LCC) analysis is an economic technique used to assess the total costs associated with the lifetime of a system in order to support decision making in long term strategic planning. For complex systems, such as railway and road infrastructures, the cost of maintenance plays an important role in the LCC analysis. Costs associated with maintenance interventions can be more reliably estimated by integrating the probabilistic nature of the failures associated to these interventions in the LCC models. Reliability, Maintainability, Availability and Safety (RAMS) parameters describe the maintenance needs of an asset in a quantitative way by using probabilistic information extracted from registered maintenance activities. Therefore, the integration of RAMS in the LCC analysis allows obtaining reliable predictions of system maintenance costs and the dependencies of these costs with specific cost drivers through sensitivity analyses. This paper presents an innovative approach for a combined RAMS & LCC methodology for railway and road transport infrastructures being developed under the on-going H2020 project INFRALERT. Such RAMS & LCC analysis provides relevant probabilistic information to be used for condition and risk-based planning of maintenance activities as well as for decision support in long term strategic investment planning.

The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms.

PubMed

Biehler, J; Wall, W A

2018-02-01

If computational models are ever to be used in high-stakes decision making in clinical practice, the use of personalized models and predictive simulation techniques is a must. This entails rigorous quantification of uncertainties as well as harnessing available patient-specific data to the greatest extent possible. Although researchers are beginning to realize that taking uncertainty in model input parameters into account is a necessity, the predominantly used probabilistic description for these uncertain parameters is based on elementary random variable models. In this work, we set out for a comparison of different probabilistic models for uncertain input parameters using the example of an uncertain wall thickness in finite element models of abdominal aortic aneurysms. We provide the first comparison between a random variable and a random field model for the aortic wall and investigate the impact on the probability distribution of the computed peak wall stress. Moreover, we show that the uncertainty about the prevailing peak wall stress can be reduced if noninvasively available, patient-specific data are harnessed for the construction of the probabilistic wall thickness model. Copyright © 2017 John Wiley & Sons, Ltd.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

PubMed

Herzallah, Randa

2015-03-01

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

ERIC Educational Resources Information Center

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

QUANTIFYING AGGREGATE CHLORPYRIFOS EXPOSURE AND DOSE TO CHILDREN USING A PHYSICALLY-BASED TWO-STAGE MONTE CARLO PROBABILISTIC MODEL

EPA Science Inventory

To help address the Food Quality Protection Act of 1996, a physically-based, two-stage Monte Carlo probabilistic model has been developed to quantify and analyze aggregate exposure and dose to pesticides via multiple routes and pathways. To illustrate model capabilities and ide...

Probabilistic Analysis Techniques Applied to Complex Spacecraft Power System Modeling

NASA Technical Reports Server (NTRS)

Hojnicki, Jeffrey S.; Rusick, Jeffrey J.

2005-01-01

Electric power system performance predictions are critical to spacecraft, such as the International Space Station (ISS), to ensure that sufficient power is available to support all the spacecraft s power needs. In the case of the ISS power system, analyses to date have been deterministic, meaning that each analysis produces a single-valued result for power capability because of the complexity and large size of the model. As a result, the deterministic ISS analyses did not account for the sensitivity of the power capability to uncertainties in model input variables. Over the last 10 years, the NASA Glenn Research Center has developed advanced, computationally fast, probabilistic analysis techniques and successfully applied them to large (thousands of nodes) complex structural analysis models. These same techniques were recently applied to large, complex ISS power system models. This new application enables probabilistic power analyses that account for input uncertainties and produce results that include variations caused by these uncertainties. Specifically, N&R Engineering, under contract to NASA, integrated these advanced probabilistic techniques with Glenn s internationally recognized ISS power system model, System Power Analysis for Capability Evaluation (SPACE).

An approximate methods approach to probabilistic structural analysis

NASA Technical Reports Server (NTRS)

Mcclung, R. C.; Millwater, H. R.; Wu, Y.-T.; Thacker, B. H.; Burnside, O. H.

1989-01-01

A probabilistic structural analysis method (PSAM) is described which makes an approximate calculation of the structural response of a system, including the associated probabilistic distributions, with minimal computation time and cost, based on a simplified representation of the geometry, loads, and material. The method employs the fast probability integration (FPI) algorithm of Wu and Wirsching. Typical solution strategies are illustrated by formulations for a representative critical component chosen from the Space Shuttle Main Engine (SSME) as part of a major NASA-sponsored program on PSAM. Typical results are presented to demonstrate the role of the methodology in engineering design and analysis.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

PubMed

Nagarajan, Mahesh B; Raman, Steven S; Lo, Pechin; Lin, Wei-Chan; Khoshnoodi, Pooria; Sayre, James W; Ramakrishna, Bharath; Ahuja, Preeti; Huang, Jiaoti; Margolis, Daniel J A; Lu, David S K; Reiter, Robert E; Goldin, Jonathan G; Brown, Matthew S; Enzmann, Dieter R

2018-02-19

We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Effects of sample survey design on the accuracy of classification tree models in species distribution models

Treesearch

Thomas C. Edwards; D. Richard Cutler; Niklaus E. Zimmermann; Linda Geiser; Gretchen G. Moisen

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by...

Organism-level models: When mechanisms and statistics fail us

NASA Astrophysics Data System (ADS)

Phillips, M. H.; Meyer, J.; Smith, W. P.; Rockhill, J. K.

2014-03-01

Purpose: To describe the unique characteristics of models that represent the entire course of radiation therapy at the organism level and to highlight the uses to which such models can be put. Methods: At the level of an organism, traditional model-building runs into severe difficulties. We do not have sufficient knowledge to devise a complete biochemistry-based model. Statistical model-building fails due to the vast number of variables and the inability to control many of them in any meaningful way. Finally, building surrogate models, such as animal-based models, can result in excluding some of the most critical variables. Bayesian probabilistic models (Bayesian networks) provide a useful alternative that have the advantages of being mathematically rigorous, incorporating the knowledge that we do have, and being practical. Results: Bayesian networks representing radiation therapy pathways for prostate cancer and head & neck cancer were used to highlight the important aspects of such models and some techniques of model-building. A more specific model representing the treatment of occult lymph nodes in head & neck cancer were provided as an example of how such a model can inform clinical decisions. A model of the possible role of PET imaging in brain cancer was used to illustrate the means by which clinical trials can be modelled in order to come up with a trial design that will have meaningful outcomes. Conclusions: Probabilistic models are currently the most useful approach to representing the entire therapy outcome process.

Modelling default and likelihood reasoning as probabilistic reasoning

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. Likely and by default are in fact treated as duals in the same sense as possibility and necessity. To model these four forms probabilistically, a qualitative default probabilistic (QDP) logic and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequent results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

Probabilistic estimation of residential air exchange rates for population-based human exposure modeling

EPA Science Inventory

Residential air exchange rates (AERs) are a key determinant in the infiltration of ambient air pollution indoors. Population-based human exposure models using probabilistic approaches to estimate personal exposure to air pollutants have relied on input distributions from AER meas...

From information processing to decisions: Formalizing and comparing psychologically plausible choice models.

PubMed

Heck, Daniel W; Hilbig, Benjamin E; Moshagen, Morten

2017-08-01

Decision strategies explain how people integrate multiple sources of information to make probabilistic inferences. In the past decade, increasingly sophisticated methods have been developed to determine which strategy explains decision behavior best. We extend these efforts to test psychologically more plausible models (i.e., strategies), including a new, probabilistic version of the take-the-best (TTB) heuristic that implements a rank order of error probabilities based on sequential processing. Within a coherent statistical framework, deterministic and probabilistic versions of TTB and other strategies can directly be compared using model selection by minimum description length or the Bayes factor. In an experiment with inferences from given information, only three of 104 participants were best described by the psychologically plausible, probabilistic version of TTB. Similar as in previous studies, most participants were classified as users of weighted-additive, a strategy that integrates all available information and approximates rational decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Are quantum-mechanical-like models possible, or necessary, outside quantum physics?

NASA Astrophysics Data System (ADS)

Plotnitsky, Arkady

2014-12-01

This article examines some experimental conditions that invite and possibly require recourse to quantum-mechanical-like mathematical models (QMLMs), models based on the key mathematical features of quantum mechanics, in scientific fields outside physics, such as biology, cognitive psychology, or economics. In particular, I consider whether the following two correlative features of quantum phenomena that were decisive for establishing the mathematical formalism of quantum mechanics play similarly important roles in QMLMs elsewhere. The first is the individuality and discreteness of quantum phenomena, and the second is the irreducibly probabilistic nature of our predictions concerning them, coupled to the particular character of the probabilities involved, as different from the character of probabilities found in classical physics. I also argue that these features could be interpreted in terms of a particular form of epistemology that suspends and even precludes a causal and, in the first place, realist description of quantum objects and processes. This epistemology limits the descriptive capacity of quantum theory to the description, classical in nature, of the observed quantum phenomena manifested in measuring instruments. Quantum mechanics itself only provides descriptions, probabilistic in nature, concerning numerical data pertaining to such phenomena, without offering a physical description of quantum objects and processes. While QMLMs share their use of the quantum-mechanical or analogous mathematical formalism, they may differ by the roles, if any, the two features in question play in them and by different ways of interpreting the phenomena they considered and this formalism itself. This article will address those differences as well.

Comparing Psychology Undergraduates' Performance in Probabilistic Reasoning under Verbal-Numerical and Graphical-Pictorial Problem Presentation Format: What Is the Role of Individual and Contextual Dimensions?

ERIC Educational Resources Information Center

Agus, Mirian; Peró-Cebollero, Maribel; Penna, Maria Pietronilla; Guàrdia-Olmos, Joan

2015-01-01

This study aims to investigate about the existence of a graphical facilitation effect on probabilistic reasoning. Measures of undergraduates' performances on problems presented in both verbal-numerical and graphical-pictorial formats have been related to visuo-spatial and numerical prerequisites, to statistical anxiety, to attitudes towards…

Probability misjudgment, cognitive ability, and belief in the paranormal.

PubMed

Musch, Jochen; Ehrenberg, Katja

2002-05-01

According to the probability misjudgment account of paranormal belief (Blackmore & Troscianko, 1985), believers in the paranormal tend to wrongly attribute remarkable coincidences to paranormal causes rather than chance. Previous studies have shown that belief in the paranormal is indeed positively related to error rates in probabilistic reasoning. General cognitive ability could account for a relationship between these two variables without assuming a causal role of probabilistic reasoning in the forming of paranormal beliefs, however. To test this alternative explanation, a belief in the paranormal scale (BPS) and a battery of probabilistic reasoning tasks were administered to 123 university students. Confirming previous findings, a significant correlation between BPS scores and error rates in probabilistic reasoning was observed. This relationship disappeared, however, when cognitive ability as measured by final examination grades was controlled for. Lower cognitive ability correlated substantially with belief in the paranormal. This finding suggests that differences in general cognitive performance rather than specific probabilistic reasoning skills provide the basis for paranormal beliefs.

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.

A generative, probabilistic model of local protein structure.

PubMed

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

2008-07-01

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

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...

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.

Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

PubMed Central

Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

2016-01-01

Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision. PMID:27303323

Model fitting data from syllogistic reasoning experiments.

PubMed

Hattori, Masasi

2016-12-01

The data presented in this article are related to the research article entitled "Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics" (M. Hattori, 2016) [1]. This article presents predicted data by three signature probabilistic models of syllogistic reasoning and model fitting results for each of a total of 12 experiments ( N =404) in the literature. Models are implemented in R, and their source code is also provided.

The Probability Heuristics Model of Syllogistic Reasoning.

ERIC Educational Resources Information Center

Chater, Nick; Oaksford, Mike

1999-01-01

Proposes a probability heuristic model for syllogistic reasoning and confirms the rationality of this heuristic by an analysis of the probabilistic validity of syllogistic reasoning that treats logical inference as a limiting case of probabilistic inference. Meta-analysis and two experiments involving 40 adult participants and using generalized…

Modeling Array Stations in SIG-VISA

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

NASA Astrophysics Data System (ADS)

Caglar, Mehmet Umut; Pal, Ranadip

2011-03-01

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

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

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

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses.

PubMed

Fuller, Robert William; Wong, Tony E; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.

Composite load spectra for select space propulsion structural components

NASA Technical Reports Server (NTRS)

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

1991-01-01

The work performed to develop composite load spectra (CLS) for the Space Shuttle Main Engine (SSME) using probabilistic methods. The three methods were implemented to be the engine system influence model. RASCAL was chosen to be the principal method as most component load models were implemented with the method. Validation of RASCAL was performed. High accuracy comparable to the Monte Carlo method can be obtained if a large enough bin size is used. Generic probabilistic models were developed and implemented for load calculations using the probabilistic methods discussed above. Each engine mission, either a real fighter or a test, has three mission phases: the engine start transient phase, the steady state phase, and the engine cut off transient phase. Power level and engine operating inlet conditions change during a mission. The load calculation module provides the steady-state and quasi-steady state calculation procedures with duty-cycle-data option. The quasi-steady state procedure is for engine transient phase calculations. In addition, a few generic probabilistic load models were also developed for specific conditions. These include the fixed transient spike model, the poison arrival transient spike model, and the rare event model. These generic probabilistic load models provide sufficient latitude for simulating loads with specific conditions. For SSME components, turbine blades, transfer ducts, LOX post, and the high pressure oxidizer turbopump (HPOTP) discharge duct were selected for application of the CLS program. They include static pressure loads and dynamic pressure loads for all four components, centrifugal force for the turbine blade, temperatures of thermal loads for all four components, and structural vibration loads for the ducts and LOX posts.

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.

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

A Probabilistic Model for Sediment Entrainment: the Role of Bed Irregularity

NASA Astrophysics Data System (ADS)

Thanos Papanicolaou, A. N.

2017-04-01

A generalized probabilistic model is developed in this study to predict sediment entrainment under the incipient motion, rolling, and pickup modes. A novelty of the proposed model is that it incorporates in its formulation the probability density function of the bed shear stress, instead of the near-bed velocity fluctuations, to account for the effects of both flow turbulence and bed surface irregularity on sediment entrainment. The proposed model incorporates in its formulation the collective effects of three parameters describing bed surface irregularity, namely the relative roughness, the volumetric fraction and relative position of sediment particles within the active layer. Another key feature of the model is that it provides a criterion for estimating the lift and drag coefficients jointly based on the recognition that lift and drag forces acting on sediment particles are interdependent and vary with particle protrusion and packing density. The model was validated using laboratory data of both fine and coarse sediment and was compared with previously published models. The study results show that for the fine sediment data, where the sediment particles have more uniform gradation and relative roughness is not a factor, all the examined models perform adequately. The proposed model was particularly suited for the coarse sediment data, where the increased bed irregularity was captured by the new parameters introduced in the model formulations. As a result, the proposed model yielded smaller prediction errors and physically acceptable values for the lift coefficient compared to the other models in case of the coarse sediment data.

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose-glucose metabolism.

PubMed

Asano, Masanari; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

2016-05-28

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies andEscherichia colilactose-glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing withn-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. © 2016 The Author(s).

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit.

PubMed

Frommholz, Ingo; Roelleke, Thomas

2016-01-01

Probabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs. Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing . The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.

Three-body system metaphor for the two-slit experiment and Escherichia coli lactose–glucose metabolism

PubMed Central

Asano, Masanari; Ohya, Masanori; Yamato, Ichiro

2016-01-01

We compare the contextual probabilistic structures of the seminal two-slit experiment (quantum interference experiment), the system of three interacting bodies and Escherichia coli lactose–glucose metabolism. We show that they have the same non-Kolmogorov probabilistic structure resulting from multi-contextuality. There are plenty of statistical data with non-Kolmogorov features; in particular, the probabilistic behaviour of neither quantum nor biological systems can be described classically. Biological systems (even cells and proteins) are macroscopic systems and one may try to present a more detailed model of interactions in such systems that lead to quantum-like probabilistic behaviour. The system of interactions between three bodies is one of the simplest metaphoric examples for such interactions. By proceeding further in this way (by playing with n-body systems) we shall be able to find metaphoric mechanical models for complex bio-interactions, e.g. signalling between cells, leading to non-Kolmogorov probabilistic data. PMID:27091163

Trait-Dependent Biogeography: (Re)Integrating Biology into Probabilistic Historical Biogeographical Models.

PubMed

Sukumaran, Jeet; Knowles, L Lacey

2018-06-01

The development of process-based probabilistic models for historical biogeography has transformed the field by grounding it in modern statistical hypothesis testing. However, most of these models abstract away biological differences, reducing species to interchangeable lineages. We present here the case for reintegration of biology into probabilistic historical biogeographical models, allowing a broader range of questions about biogeographical processes beyond ancestral range estimation or simple correlation between a trait and a distribution pattern, as well as allowing us to assess how inferences about ancestral ranges themselves might be impacted by differential biological traits. We show how new approaches to inference might cope with the computational challenges resulting from the increased complexity of these trait-based historical biogeographical models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Comparison of Four Probabilistic Models (CARES, Calendex, ConsEspo, SHEDS) to Estimate Aggregate Residential Exposures to Pesticides

EPA Science Inventory

Two deterministic models (US EPA’s Office of Pesticide Programs Residential Standard Operating Procedures (OPP Residential SOPs) and Draft Protocol for Measuring Children’s Non-Occupational Exposure to Pesticides by all Relevant Pathways (Draft Protocol)) and four probabilistic mo...

EXPERIENCES WITH USING PROBABILISTIC EXPOSURE ANALYSIS METHODS IN THE U.S. EPA

EPA Science Inventory

Over the past decade various Offices and Programs within the U.S. EPA have either initiated or increased the development and application of probabilistic exposure analysis models. These models have been applied to a broad range of research or regulatory problems in EPA, such as e...

Structural reliability assessment capability in NESSUS

NASA Technical Reports Server (NTRS)

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

1992-01-01

The principal capabilities of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), an advanced computer code developed for probabilistic structural response analysis, are reviewed, and its structural reliability assessed. The code combines flexible structural modeling tools with advanced probabilistic algorithms in order to compute probabilistic structural response and resistance, component reliability and risk, and system reliability and risk. An illustrative numerical example is presented.

Structural reliability assessment capability in NESSUS

NASA Astrophysics Data System (ADS)

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

1992-07-01

The principal capabilities of NESSUS (Numerical Evaluation of Stochastic Structures Under Stress), an advanced computer code developed for probabilistic structural response analysis, are reviewed, and its structural reliability assessed. The code combines flexible structural modeling tools with advanced probabilistic algorithms in order to compute probabilistic structural response and resistance, component reliability and risk, and system reliability and risk. An illustrative numerical example is presented.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

NASA Astrophysics Data System (ADS)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

A novel probabilistic framework for event-based speech recognition

NASA Astrophysics Data System (ADS)

Juneja, Amit; Espy-Wilson, Carol

2003-10-01

One of the reasons for unsatisfactory performance of the state-of-the-art automatic speech recognition (ASR) systems is the inferior acoustic modeling of low-level acoustic-phonetic information in the speech signal. An acoustic-phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal, but such a system for continuous speech recognition (CSR) is not known to exist. A probabilistic and statistical framework for CSR based on the idea of the representation of speech sounds by bundles of binary valued articulatory phonetic features is proposed. Multiple probabilistic sequences of linguistically motivated landmarks are obtained using binary classifiers of manner phonetic features-syllabic, sonorant and continuant-and the knowledge-based acoustic parameters (APs) that are acoustic correlates of those features. The landmarks are then used for the extraction of knowledge-based APs for source and place phonetic features and their binary classification. Probabilistic landmark sequences are constrained using manner class language models for isolated or connected word recognition. The proposed method could overcome the disadvantages encountered by the early acoustic-phonetic knowledge-based systems that led the ASR community to switch to systems highly dependent on statistical pattern analysis methods and probabilistic language or grammar models.

Probabilistic Seismic Risk Model for Western Balkans

NASA Astrophysics Data System (ADS)

Stejskal, Vladimir; Lorenzo, Francisco; Pousse, Guillaume; Radovanovic, Slavica; Pekevski, Lazo; Dojcinovski, Dragi; Lokin, Petar; Petronijevic, Mira; Sipka, Vesna

2010-05-01

A probabilistic seismic risk model for insurance and reinsurance purposes is presented for an area of Western Balkans, covering former Yugoslavia and Albania. This territory experienced many severe earthquakes during past centuries producing significant damage to many population centres in the region. The highest hazard is related to external Dinarides, namely to the collision zone of the Adriatic plate. The model is based on a unified catalogue for the region and a seismic source model consisting of more than 30 zones covering all the three main structural units - Southern Alps, Dinarides and the south-western margin of the Pannonian Basin. A probabilistic methodology using Monte Carlo simulation was applied to generate the hazard component of the model. Unique set of damage functions based on both loss experience and engineering assessments is used to convert the modelled ground motion severity into the monetary loss.

Probabilistic risk models for multiple disturbances: an example of forest insects and wildfires

Treesearch

Haiganoush K. Preisler; Alan A. Ager; Jane L. Hayes

2010-01-01

Building probabilistic risk models for highly random forest disturbances like wildfire and forest insect outbreaks is a challenging. Modeling the interactions among natural disturbances is even more difficult. In the case of wildfire and forest insects, we looked at the probability of a large fire given an insect outbreak and also the incidence of insect outbreaks...

Probabilistic Structural Analysis Methods (PSAM) for Select Space Propulsion System Components

NASA Technical Reports Server (NTRS)

1999-01-01

Probabilistic Structural Analysis Methods (PSAM) are described for the probabilistic structural analysis of engine components for current and future space propulsion systems. Components for these systems are subjected to stochastic thermomechanical launch loads. Uncertainties or randomness also occurs in material properties, structural geometry, and boundary conditions. Material property stochasticity, such as in modulus of elasticity or yield strength, exists in every structure and is a consequence of variations in material composition and manufacturing processes. Procedures are outlined for computing the probabilistic structural response or reliability of the structural components. The response variables include static or dynamic deflections, strains, and stresses at one or several locations, natural frequencies, fatigue or creep life, etc. Sample cases illustrates how the PSAM methods and codes simulate input uncertainties and compute probabilistic response or reliability using a finite element model with probabilistic methods.

Simulation investigation of multipactor in metal components for space application with an improved secondary emission model

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

Li, Yun, E-mail: genliyun@126.com, E-mail: cuiwanzhao@126.com; Cui, Wan-Zhao, E-mail: genliyun@126.com, E-mail: cuiwanzhao@126.com; Wang, Hong-Guang

2015-05-15

Effects of the secondary electron emission (SEE) phenomenon of metal surface on the multipactor analysis of microwave components are investigated numerically and experimentally in this paper. Both the secondary electron yield (SEY) and the emitted energy spectrum measurements are performed on silver plated samples for accurate description of the SEE phenomenon. A phenomenological probabilistic model based on SEE physics is utilized and fitted accurately to the measured SEY and emitted energy spectrum of the conditioned surface material of microwave components. Specially, the phenomenological probabilistic model is extended to the low primary energy end lower than 20 eV mathematically, since no accuratemore » measurement data can be obtained. Embedding the phenomenological probabilistic model into the Electromagnetic Particle-In-Cell (EM-PIC) method, the electronic resonant multipacting in microwave components can be tracked and hence the multipactor threshold can be predicted. The threshold prediction error of the transformer and the coaxial filter is 0.12 dB and 1.5 dB, respectively. Simulation results demonstrate that the discharge threshold is strongly dependent on the SEYs and its energy spectrum in the low energy end (lower than 50 eV). Multipacting simulation results agree quite well with experiments in practical components, while the phenomenological probabilistic model fit both the SEY and the emission energy spectrum better than the traditionally used model and distribution. The EM-PIC simulation method with the phenomenological probabilistic model for the surface collision simulation has been demonstrated for predicting the multipactor threshold in metal components for space application.« less

Probabilistic choice models in health-state valuation research: background, theories, assumptions and applications.

PubMed

Arons, Alexander M M; Krabbe, Paul F M

2013-02-01

Interest is rising in measuring subjective health outcomes, such as treatment outcomes that are not directly quantifiable (functional disability, symptoms, complaints, side effects and health-related quality of life). Health economists in particular have applied probabilistic choice models in the area of health evaluation. They increasingly use discrete choice models based on random utility theory to derive values for healthcare goods or services. Recent attempts have been made to use discrete choice models as an alternative method to derive values for health states. In this article, various probabilistic choice models are described according to their underlying theory. A historical overview traces their development and applications in diverse fields. The discussion highlights some theoretical and technical aspects of the choice models and their similarity and dissimilarity. The objective of the article is to elucidate the position of each model and their applications for health-state valuation.

Inherent limitations of probabilistic models for protein-DNA binding specificity

PubMed Central

Ruan, Shuxiang

2017-01-01

The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible. PMID:28686588

Cancer Evolution: Mathematical Models and Computational Inference

PubMed Central

Beerenwinkel, Niko; Schwarz, Roland F.; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. PMID:25293804

A Probabilistic Model for Diagnosing Misconceptions by a Pattern Classification Approach.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi K.

A probabilistic approach is introduced to classify and diagnose erroneous rules of operation resulting from a variety of misconceptions ("bugs") in a procedural domain of arithmetic. The model is contrasted with the deterministic approach which has commonly been used in the field of artificial intelligence, and the advantage of treating the…

Nonlinear probabilistic finite element models of laminated composite shells

NASA Technical Reports Server (NTRS)

Engelstad, S. P.; Reddy, J. N.

1993-01-01

A probabilistic finite element analysis procedure for laminated composite shells has been developed. A total Lagrangian finite element formulation, employing a degenerated 3-D laminated composite shell with the full Green-Lagrange strains and first-order shear deformable kinematics, forms the modeling foundation. The first-order second-moment technique for probabilistic finite element analysis of random fields is employed and results are presented in the form of mean and variance of the structural response. The effects of material nonlinearity are included through the use of a rate-independent anisotropic plasticity formulation with the macroscopic point of view. Both ply-level and micromechanics-level random variables can be selected, the latter by means of the Aboudi micromechanics model. A number of sample problems are solved to verify the accuracy of the procedures developed and to quantify the variability of certain material type/structure combinations. Experimental data is compared in many cases, and the Monte Carlo simulation method is used to check the probabilistic results. In general, the procedure is quite effective in modeling the mean and variance response of the linear and nonlinear behavior of laminated composite shells.

A unified probabilistic approach to improve spelling in an event-related potential-based brain-computer interface.

PubMed

Kindermans, Pieter-Jan; Verschore, Hannes; Schrauwen, Benjamin

2013-10-01

In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

PubMed

Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-05-01

Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

PubMed Central

Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-01-01

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. PMID:27655861

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

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

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.

Probabilistic inversion of expert assessments to inform projections about Antarctic ice sheet responses

PubMed Central

Wong, Tony E.; Keller, Klaus

2017-01-01

The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections. PMID:29287095

Role of Central Serotonin in Anticipation of Rewarding and Punishing Outcomes: Effects of Selective Amygdala or Orbitofrontal 5-HT Depletion

PubMed Central

Rygula, Rafal; Clarke, Hannah F.; Cardinal, Rudolf N.; Cockcroft, Gemma J.; Xia, Jing; Dalley, Jeff W.; Robbins, Trevor W.; Roberts, Angela C.

2015-01-01

Understanding the role of serotonin (or 5-hydroxytryptamine, 5-HT) in aversive processing has been hampered by the contradictory findings, across studies, of increased sensitivity to punishment in terms of subsequent response choice but decreased sensitivity to punishment-induced response suppression following gross depletion of central 5-HT. To address this apparent discrepancy, the present study determined whether both effects could be found in the same animals by performing localized 5-HT depletions in the amygdala or orbitofrontal cortex (OFC) of a New World monkey, the common marmoset. 5-HT depletion in the amygdala impaired response choice on a probabilistic visual discrimination task by increasing the effectiveness of misleading, or false, punishment and reward, and decreased response suppression in a variable interval test of punishment sensitivity that employed the same reward and punisher. 5-HT depletion in the OFC also disrupted probabilistic discrimination learning and decreased response suppression. Computational modeling of behavior on the discrimination task showed that the lesions reduced reinforcement sensitivity. A novel, unitary account of the findings in terms of the causal role of 5-HT in the anticipation of both negative and positive motivational outcomes is proposed and discussed in relation to current theories of 5-HT function and our understanding of mood and anxiety disorders. PMID:24879752

Application of the probabilistic approximate analysis method to a turbopump blade analysis. [for Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Thacker, B. H.; Mcclung, R. C.; Millwater, H. R.

1990-01-01

An eigenvalue analysis of a typical space propulsion system turbopump blade is presented using an approximate probabilistic analysis methodology. The methodology was developed originally to investigate the feasibility of computing probabilistic structural response using closed-form approximate models. This paper extends the methodology to structures for which simple closed-form solutions do not exist. The finite element method will be used for this demonstration, but the concepts apply to any numerical method. The results agree with detailed analysis results and indicate the usefulness of using a probabilistic approximate analysis in determining efficient solution strategies.

Probabilistic Models for Solar Particle Events

NASA Technical Reports Server (NTRS)

Adams, James H., Jr.; Dietrich, W. F.; Xapsos, M. A.; Welton, A. M.

2009-01-01

Probabilistic Models of Solar Particle Events (SPEs) are used in space mission design studies to provide a description of the worst-case radiation environment that the mission must be designed to tolerate.The models determine the worst-case environment using a description of the mission and a user-specified confidence level that the provided environment will not be exceeded. This poster will focus on completing the existing suite of models by developing models for peak flux and event-integrated fluence elemental spectra for the Z>2 elements. It will also discuss methods to take into account uncertainties in the data base and the uncertainties resulting from the limited number of solar particle events in the database. These new probabilistic models are based on an extensive survey of SPE measurements of peak and event-integrated elemental differential energy spectra. Attempts are made to fit the measured spectra with eight different published models. The model giving the best fit to each spectrum is chosen and used to represent that spectrum for any energy in the energy range covered by the measurements. The set of all such spectral representations for each element is then used to determine the worst case spectrum as a function of confidence level. The spectral representation that best fits these worst case spectra is found and its dependence on confidence level is parameterized. This procedure creates probabilistic models for the peak and event-integrated spectra.

Predictive coarse-graining

NASA Astrophysics Data System (ADS)

Schöberl, Markus; Zabaras, Nicholas; Koutsourelakis, Phaedon-Stelios

2017-03-01

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method [1] and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo - Expectation-Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

Predictive coarse-graining

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

Schöberl, Markus, E-mail: m.schoeberl@tum.de; Zabaras, Nicholas; Department of Aerospace and Mechanical Engineering, University of Notre Dame, 365 Fitzpatrick Hall, Notre Dame, IN 46556

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extendedmore » to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.« less

Assessing State Nuclear Weapons Proliferation: Using Bayesian Network Analysis of Social Factors

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

Coles, Garill A.; Brothers, Alan J.; Olson, Jarrod

A Bayesian network (BN) model of social factors can support proliferation assessments by estimating the likelihood that a state will pursue a nuclear weapon. Social factors including political, economic, nuclear capability, security, and national identity and psychology factors may play as important a role in whether a State pursues nuclear weapons as more physical factors. This paper will show how using Bayesian reasoning on a generic case of a would-be proliferator State can be used to combine evidence that supports proliferation assessment. Theories and analysis by political scientists can be leveraged in a quantitative and transparent way to indicate proliferationmore » risk. BN models facilitate diagnosis and inference in a probabilistic environment by using a network of nodes and acyclic directed arcs between the nodes whose connections, or absence of, indicate probabilistic relevance, or independence. We propose a BN model that would use information from both traditional safeguards and the strengthened safeguards associated with the Additional Protocol to indicate countries with a high risk of proliferating nuclear weapons. This model could be used in a variety of applications such a prioritization tool and as a component of state safeguards evaluations. This paper will discuss the benefits of BN reasoning, the development of Pacific Northwest National Laboratory’s (PNNL) BN state proliferation model and how it could be employed as an analytical tool.« less

Probabilistic evaluation of uncertainties and risks in aerospace components

NASA Technical Reports Server (NTRS)

Shah, A. R.; Shiao, M. C.; Nagpal, V. K.; Chamis, C. C.

1992-01-01

A methodology is presented for the computational simulation of primitive variable uncertainties, and attention is given to the simulation of specific aerospace components. Specific examples treated encompass a probabilistic material behavior model, as well as static, dynamic, and fatigue/damage analyses of a turbine blade in a mistuned bladed rotor in the SSME turbopumps. An account is given of the use of the NESSES probabilistic FEM analysis CFD code.

Probabilistic Design and Analysis Framework

NASA Technical Reports Server (NTRS)

Strack, William C.; Nagpal, Vinod K.

2010-01-01

PRODAF is a software package designed to aid analysts and designers in conducting probabilistic analysis of components and systems. PRODAF can integrate multiple analysis programs to ease the tedious process of conducting a complex analysis process that requires the use of multiple software packages. The work uses a commercial finite element analysis (FEA) program with modules from NESSUS to conduct a probabilistic analysis of a hypothetical turbine blade, disk, and shaft model. PRODAF applies the response surface method, at the component level, and extrapolates the component-level responses to the system level. Hypothetical components of a gas turbine engine are first deterministically modeled using FEA. Variations in selected geometrical dimensions and loading conditions are analyzed to determine the effects of the stress state within each component. Geometric variations include the cord length and height for the blade, inner radius, outer radius, and thickness, which are varied for the disk. Probabilistic analysis is carried out using developing software packages like System Uncertainty Analysis (SUA) and PRODAF. PRODAF was used with a commercial deterministic FEA program in conjunction with modules from the probabilistic analysis program, NESTEM, to perturb loads and geometries to provide a reliability and sensitivity analysis. PRODAF simplified the handling of data among the various programs involved, and will work with many commercial and opensource deterministic programs, probabilistic programs, or modules.

Psychological Plausibility of the Theory of Probabilistic Mental Models and the Fast and Frugal Heuristics

ERIC Educational Resources Information Center

Dougherty, Michael R.; Franco-Watkins, Ana M.; Thomas, Rick

2008-01-01

The theory of probabilistic mental models (PMM; G. Gigerenzer, U. Hoffrage, & H. Kleinbolting, 1991) has had a major influence on the field of judgment and decision making, with the most recent important modifications to PMM theory being the identification of several fast and frugal heuristics (G. Gigerenzer & D. G. Goldstein, 1996). These…

Developing probabilistic models to predict amphibian site occupancy in a patchy landscape

Treesearch

R. A. Knapp; K.R. Matthews; H. K. Preisler; R. Jellison

2003-01-01

Abstract. Human-caused fragmentation of habitats is threatening an increasing number of animal and plant species, making an understanding of the factors influencing patch occupancy ever more important. The overall goal of the current study was to develop probabilistic models of patch occupancy for the mountain yellow-legged frog (Rana muscosa). This once-common species...

Probabilistic migration modelling focused on functional barrier efficiency and low migration concepts in support of risk assessment.

PubMed

Brandsch, Rainer

2017-10-01

Migration modelling provides reliable migration estimates from food-contact materials (FCM) to food or food simulants based on mass-transfer parameters like diffusion and partition coefficients related to individual materials. In most cases, mass-transfer parameters are not readily available from the literature and for this reason are estimated with a given uncertainty. Historically, uncertainty was accounted for by introducing upper limit concepts first, turning out to be of limited applicability due to highly overestimated migration results. Probabilistic migration modelling gives the possibility to consider uncertainty of the mass-transfer parameters as well as other model inputs. With respect to a functional barrier, the most important parameters among others are the diffusion properties of the functional barrier and its thickness. A software tool that accepts distribution as inputs and is capable of applying Monte Carlo methods, i.e., random sampling from the input distributions of the relevant parameters (i.e., diffusion coefficient and layer thickness), predicts migration results with related uncertainty and confidence intervals. The capabilities of probabilistic migration modelling are presented in the view of three case studies (1) sensitivity analysis, (2) functional barrier efficiency and (3) validation by experimental testing. Based on the predicted migration by probabilistic migration modelling and related exposure estimates, safety evaluation of new materials in the context of existing or new packaging concepts is possible. Identifying associated migration risk and potential safety concerns in the early stage of packaging development is possible. Furthermore, dedicated material selection exhibiting required functional barrier efficiency under application conditions becomes feasible. Validation of the migration risk assessment by probabilistic migration modelling through a minimum of dedicated experimental testing is strongly recommended.

Single, Complete, Probability Spaces Consistent With EPR-Bohm-Bell Experimental Data

NASA Astrophysics Data System (ADS)

Avis, David; Fischer, Paul; Hilbert, Astrid; Khrennikov, Andrei

2009-03-01

We show that paradoxical consequences of violations of Bell's inequality are induced by the use of an unsuitable probabilistic description for the EPR-Bohm-Bell experiment. The conventional description (due to Bell) is based on a combination of statistical data collected for different settings of polarization beam splitters (PBSs). In fact, such data consists of some conditional probabilities which only partially define a probability space. Ignoring this conditioning leads to apparent contradictions in the classical probabilistic model (due to Kolmogorov). We show how to make a completely consistent probabilistic model by taking into account the probabilities of selecting the settings of the PBSs. Our model matches both the experimental data and is consistent with classical probability theory.

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

NASA Astrophysics Data System (ADS)

Schön, Thomas B.; Svensson, Andreas; Murray, Lawrence; Lindsten, Fredrik

2018-05-01

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods-the particle Metropolis-Hastings algorithm-which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods-including particle Metropolis-Hastings-to a large group of users without requiring them to know all the underlying mathematical details.

Probabilistic forecasts based on radar rainfall uncertainty

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

An analytical probabilistic model of the quality efficiency of a sewer tank

NASA Astrophysics Data System (ADS)

Balistrocchi, Matteo; Grossi, Giovanna; Bacchi, Baldassare

2009-12-01

The assessment of the efficiency of a storm water storage facility devoted to the sewer overflow control in urban areas strictly depends on the ability to model the main features of the rainfall-runoff routing process and the related wet weather pollution delivery. In this paper the possibility of applying the analytical probabilistic approach for developing a tank design method, whose potentials are similar to the continuous simulations, is proved. In the model derivation the quality issues of such devices were implemented. The formulation is based on a Weibull probabilistic model of the main characteristics of the rainfall process and on a power law describing the relationship between the dimensionless storm water cumulative runoff volume and the dimensionless cumulative pollutograph. Following this approach, efficiency indexes were established. The proposed model was verified by comparing its results to those obtained by continuous simulations; satisfactory agreement is shown for the proposed efficiency indexes.

Extended Range Prediction of Indian Summer Monsoon: Current status

NASA Astrophysics Data System (ADS)

Sahai, A. K.; Abhilash, S.; Borah, N.; Joseph, S.; Chattopadhyay, R.; S, S.; Rajeevan, M.; Mandal, R.; Dey, A.

2014-12-01

The main focus of this study is to develop forecast consensus in the extended range prediction (ERP) of monsoon Intraseasonal oscillations using a suit of different variants of Climate Forecast system (CFS) model. In this CFS based Grand MME prediction system (CGMME), the ensemble members are generated by perturbing the initial condition and using different configurations of CFSv2. This is to address the role of different physical mechanisms known to have control on the error growth in the ERP in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 and 11 high resolution CFST382. Thus, we develop the multi-model consensus forecast for the ERP of Indian summer monsoon (ISM) using a suite of different variants of CFS model. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. Skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. Main improvement is attained in probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. CGMME further added value to both deterministic and probability forecast compared to raw SME's and this better skill is probably flows from large spread and improved spread-error relationship. CGMME system is currently capable of generating ER prediction in real time and successfully delivering its experimental operational ER forecast of ISM for the last few years.

PROBABILISTIC MODELING FOR ADVANCED HUMAN EXPOSURE ASSESSMENT

EPA Science Inventory

Human exposures to environmental pollutants widely vary depending on the emission patterns that result in microenvironmental pollutant concentrations, as well as behavioral factors that determine the extent of an individual's contact with these pollutants. Probabilistic human exp...

Probabilistic boundary element method

NASA Technical Reports Server (NTRS)

Cruse, T. A.; Raveendra, S. T.

1989-01-01

The purpose of the Probabilistic Structural Analysis Method (PSAM) project is to develop structural analysis capabilities for the design analysis of advanced space propulsion system hardware. The boundary element method (BEM) is used as the basis of the Probabilistic Advanced Analysis Methods (PADAM) which is discussed. The probabilistic BEM code (PBEM) is used to obtain the structural response and sensitivity results to a set of random variables. As such, PBEM performs analogous to other structural analysis codes such as finite elements in the PSAM system. For linear problems, unlike the finite element method (FEM), the BEM governing equations are written at the boundary of the body only, thus, the method eliminates the need to model the volume of the body. However, for general body force problems, a direct condensation of the governing equations to the boundary of the body is not possible and therefore volume modeling is generally required.

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.

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

Time Alignment as a Necessary Step in the Analysis of Sleep Probabilistic Curves

NASA Astrophysics Data System (ADS)

Rošt'áková, Zuzana; Rosipal, Roman

2018-02-01

Sleep can be characterised as a dynamic process that has a finite set of sleep stages during the night. The standard Rechtschaffen and Kales sleep model produces discrete representation of sleep and does not take into account its dynamic structure. In contrast, the continuous sleep representation provided by the probabilistic sleep model accounts for the dynamics of the sleep process. However, analysis of the sleep probabilistic curves is problematic when time misalignment is present. In this study, we highlight the necessity of curve synchronisation before further analysis. Original and in time aligned sleep probabilistic curves were transformed into a finite dimensional vector space, and their ability to predict subjects' age or daily measures is evaluated. We conclude that curve alignment significantly improves the prediction of the daily measures, especially in the case of the S2-related sleep states or slow wave sleep.

Leveraging Past and Current Measurements to Probabilistically Nowcast Low Visibility Procedures at an Airport

NASA Astrophysics Data System (ADS)

Mayr, G. J.; Kneringer, P.; Dietz, S. J.; Zeileis, A.

2016-12-01

Low visibility or low cloud ceiling reduce the capacity of airports by requiring special low visibility procedures (LVP) for incoming/departing aircraft. Probabilistic forecasts when such procedures will become necessary help to mitigate delays and economic losses.We compare the performance of probabilistic nowcasts with two statistical methods: ordered logistic regression, and trees and random forests. These models harness historic and current meteorological measurements in the vicinity of the airport and LVP states, and incorporate diurnal and seasonal climatological information via generalized additive models (GAM). The methods are applied at Vienna International Airport (Austria). The performance is benchmarked against climatology, persistence and human forecasters.

Composite Load Spectra for Select Space Propulsion Structural Components

NASA Technical Reports Server (NTRS)

Ho, Hing W.; Newell, James F.

1994-01-01

Generic load models are described with multiple levels of progressive sophistication to simulate the composite (combined) load spectra (CLS) that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades and liquid oxygen (LOX) posts. These generic (coupled) models combine the deterministic models for composite load dynamic, acoustic, high-pressure and high rotational speed, etc., load simulation using statistically varying coefficients. These coefficients are then determined using advanced probabilistic simulation methods with and without strategically selected experimental data. The entire simulation process is included in a CLS computer code. Applications of the computer code to various components in conjunction with the PSAM (Probabilistic Structural Analysis Method) to perform probabilistic load evaluation and life prediction evaluations are also described to illustrate the effectiveness of the coupled model approach.

Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.

A three-way approach for protein function classification

PubMed Central

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy. PMID:28234929

A three-way approach for protein function classification.

PubMed

Ur Rehman, Hafeez; Azam, Nouman; Yao, JingTao; Benso, Alfredo

2017-01-01

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

A strategy for understanding noise-induced annoyance

NASA Astrophysics Data System (ADS)

Fidell, S.; Green, D. M.; Schultz, T. J.; Pearsons, K. S.

1988-08-01

This report provides a rationale for development of a systematic approach to understanding noise-induced annoyance. Two quantitative models are developed to explain: (1) the prevalence of annoyance due to residential exposure to community noise sources; and (2) the intrusiveness of individual noise events. Both models deal explicitly with the probabilistic nature of annoyance, and assign clear roles to acoustic and nonacoustic determinants of annoyance. The former model provides a theoretical foundation for empirical dosage-effect relationships between noise exposure and community response, while the latter model differentiates between the direct and immediate annoyance of noise intrusions and response bias factors that influence the reporting of annoyance. The assumptions of both models are identified, and the nature of the experimentation necessary to test hypotheses derived from the models is described.

Probabilistic modeling of the evolution of gene synteny within reconciled phylogenies

PubMed Central

2015-01-01

Background Most models of genome evolution concern either genetic sequences, gene content or gene order. They sometimes integrate two of the three levels, but rarely the three of them. Probabilistic models of gene order evolution usually have to assume constant gene content or adopt a presence/absence coding of gene neighborhoods which is blind to complex events modifying gene content. Results We propose a probabilistic evolutionary model for gene neighborhoods, allowing genes to be inserted, duplicated or lost. It uses reconciled phylogenies, which integrate sequence and gene content evolution. We are then able to optimize parameters such as phylogeny branch lengths, or probabilistic laws depicting the diversity of susceptibility of syntenic regions to rearrangements. We reconstruct a structure for ancestral genomes by optimizing a likelihood, keeping track of all evolutionary events at the level of gene content and gene synteny. Ancestral syntenies are associated with a probability of presence. We implemented the model with the restriction that at most one gene duplication separates two gene speciations in reconciled gene trees. We reconstruct ancestral syntenies on a set of 12 drosophila genomes, and compare the evolutionary rates along the branches and along the sites. We compare with a parsimony method and find a significant number of results not supported by the posterior probability. The model is implemented in the Bio++ library. It thus benefits from and enriches the classical models and methods for molecular evolution. PMID:26452018

A PROBABILISTIC EXPOSURE ASSESSMENT FOR CHILDREN WHO CONTACT CCA-TREATED PLAYSETS AND DECKS USING THE STOCHASTIC HUMAN EXPOSURE AND DOSE SIMULATION (SHEDS) MODEL FOR THE WOOD PRESERVATIVE EXPOSURE SCENARIO

EPA Science Inventory

The U.S. Environmental Protection Agency has conducted a probabilistic exposure and dose assessment on the arsenic (As) and chromium (Cr) components of Chromated Copper Arsenate (CCA) using the Stochastic Human Exposure and Dose Simulation model for wood preservatives (SHEDS-Wood...

Probabilistic Based Modeling and Simulation Assessment

DTIC Science & Technology

2010-06-01

different crash and blast scenarios. With the integration of the high fidelity neck and head model, a methodology to calculate the probability of injury...variability, correlation, and multiple (often competing) failure metrics. Important scenarios include vehicular collisions, blast /fragment impact, and...first area of focus is to develop a methodology to integrate probabilistic analysis into finite element analysis of vehicle collisions and blast . The

A Probabilistic Model for Students' Errors and Misconceptions on the Structure of Matter in Relation to Three Cognitive Variables

ERIC Educational Resources Information Center

Tsitsipis, Georgios; Stamovlasis, Dimitrios; Papageorgiou, George

2012-01-01

In this study, the effect of 3 cognitive variables such as logical thinking, field dependence/field independence, and convergent/divergent thinking on some specific students' answers related to the particulate nature of matter was investigated by means of probabilistic models. Besides recording and tabulating the students' responses, a combination…

Probabilistic commodity-flow-based focusing of monitoring activities to facilitate early detection of Phytophthora ramorum outbreaks

Treesearch

Steven C. McKelvey; William D. Smith; Frank Koch

2012-01-01

This project summary describes a probabilistic model developed with funding support from the Forest Health Monitoring Program of the Forest Service, U.S. Department of Agriculture (BaseEM Project SO-R-08-01). The model has been implemented in SODBuster, a standalone software package developed using the Java software development kit from Sun Microsystems.

Economic Dispatch for Microgrid Containing Electric Vehicles via Probabilistic Modeling: Preprint

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

Yao, Yin; Gao, Wenzhong; Momoh, James

In this paper, an economic dispatch model with probabilistic modeling is developed for a microgrid. The electric power supply in a microgrid consists of conventional power plants and renewable energy power plants, such as wind and solar power plants. Because of the fluctuation in the output of solar and wind power plants, an empirical probabilistic model is developed to predict their hourly output. According to different characteristics of wind and solar power plants, the parameters for probabilistic distribution are further adjusted individually for both. On the other hand, with the growing trend in plug-in electric vehicles (PHEVs), an integrated microgridmore » system must also consider the impact of PHEVs. The charging loads from PHEVs as well as the discharging output via the vehicle-to-grid (V2G) method can greatly affect the economic dispatch for all of the micro energy sources in a microgrid. This paper presents an optimization method for economic dispatch in a microgrid considering conventional power plants, renewable power plants, and PHEVs. The simulation results reveal that PHEVs with V2G capability can be an indispensable supplement in a modern microgrid.« less

Probabilistic population projections with migration uncertainty

PubMed Central

Azose, Jonathan J.; Ševčíková, Hana; Raftery, Adrian E.

2016-01-01

We produce probabilistic projections of population for all countries based on probabilistic projections of fertility, mortality, and migration. We compare our projections to those from the United Nations’ Probabilistic Population Projections, which uses similar methods for fertility and mortality but deterministic migration projections. We find that uncertainty in migration projection is a substantial contributor to uncertainty in population projections for many countries. Prediction intervals for the populations of Northern America and Europe are over 70% wider, whereas prediction intervals for the populations of Africa, Asia, and the world as a whole are nearly unchanged. Out-of-sample validation shows that the model is reasonably well calibrated. PMID:27217571

Probabilistic brains: knowns and unknowns

PubMed Central

Pouget, Alexandre; Beck, Jeffrey M; Ma, Wei Ji; Latham, Peter E

2015-01-01

There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference. PMID:23955561

A probabilistic maintenance model for diesel engines

NASA Astrophysics Data System (ADS)

Pathirana, Shan; Abeygunawardane, Saranga Kumudu

2018-02-01

In this paper, a probabilistic maintenance model is developed for inspection based preventive maintenance of diesel engines based on the practical model concepts discussed in the literature. Developed model is solved using real data obtained from inspection and maintenance histories of diesel engines and experts' views. Reliability indices and costs were calculated for the present maintenance policy of diesel engines. A sensitivity analysis is conducted to observe the effect of inspection based preventive maintenance on the life cycle cost of diesel engines.

Bayesian inference of radiation belt loss timescales.

NASA Astrophysics Data System (ADS)

Camporeale, E.; Chandorkar, M.

2017-12-01

Electron fluxes in the Earth's radiation belts are routinely studied using the classical quasi-linear radial diffusion model. Although this simplified linear equation has proven to be an indispensable tool in understanding the dynamics of the radiation belt, it requires specification of quantities such as the diffusion coefficient and electron loss timescales that are never directly measured. Researchers have so far assumed a-priori parameterisations for radiation belt quantities and derived the best fit using satellite data. The state of the art in this domain lacks a coherent formulation of this problem in a probabilistic framework. We present some recent progress that we have made in performing Bayesian inference of radial diffusion parameters. We achieve this by making extensive use of the theory connecting Gaussian Processes and linear partial differential equations, and performing Markov Chain Monte Carlo sampling of radial diffusion parameters. These results are important for understanding the role and the propagation of uncertainties in radiation belt simulations and, eventually, for providing a probabilistic forecast of energetic electron fluxes in a Space Weather context.

The role of shape complexity in the detection of closed contours.

PubMed

Wilder, John; Feldman, Jacob; Singh, Manish

2016-09-01

The detection of contours in noise has been extensively studied, but the detection of closed contours, such as the boundaries of whole objects, has received relatively little attention. Closed contours pose substantial challenges not present in the simple (open) case, because they form the outlines of whole shapes and thus take on a range of potentially important configural properties. In this paper we consider the detection of closed contours in noise as a probabilistic decision problem. Previous work on open contours suggests that contour complexity, quantified as the negative log probability (Description Length, DL) of the contour under a suitably chosen statistical model, impairs contour detectability; more complex (statistically surprising) contours are harder to detect. In this study we extended this result to closed contours, developing a suitable probabilistic model of whole shapes that gives rise to several distinct though interrelated measures of shape complexity. We asked subjects to detect either natural shapes (Exp. 1) or experimentally manipulated shapes (Exp. 2) embedded in noise fields. We found systematic effects of global shape complexity on detection performance, demonstrating how aspects of global shape and form influence the basic process of object detection. Copyright © 2015 Elsevier Ltd. All rights reserved.

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 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.

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation.

PubMed

Zhang, Lei; Zeng, Zhi; Ji, Qiang

2011-09-01

Chain graph (CG) is a hybrid probabilistic graphical model (PGM) capable of modeling heterogeneous relationships among random variables. So far, however, its application in image and video analysis is very limited due to lack of principled learning and inference methods for a CG of general topology. To overcome this limitation, we introduce methods to extend the conventional chain-like CG model to CG model with more general topology and the associated methods for learning and inference in such a general CG model. Specifically, we propose techniques to systematically construct a generally structured CG, to parameterize this model, to derive its joint probability distribution, to perform joint parameter learning, and to perform probabilistic inference in this model. To demonstrate the utility of such an extended CG, we apply it to two challenging image and video analysis problems: human activity recognition and image segmentation. The experimental results show improved performance of the extended CG model over the conventional directed or undirected PGMs. This study demonstrates the promise of the extended CG for effective modeling and inference of complex real-world problems.

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.

The method of belief scales as a means for dealing with uncertainty in tough regulatory decisions.

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

Pilch, Martin M.

Modeling and simulation is playing an increasing role in supporting tough regulatory decisions, which are typically characterized by variabilities and uncertainties in the scenarios, input conditions, failure criteria, model parameters, and even model form. Variability exists when there is a statistically significant database that is fully relevant to the application. Uncertainty, on the other hand, is characterized by some degree of ignorance. A simple algebraic problem was used to illustrate how various risk methodologies address variability and uncertainty in a regulatory context. These traditional risk methodologies include probabilistic methods (including frequensic and Bayesian perspectives) and second-order methods where variabilities andmore » uncertainties are treated separately. Representing uncertainties with (subjective) probability distributions and using probabilistic methods to propagate subjective distributions can lead to results that are not logically consistent with available knowledge and that may not be conservative. The Method of Belief Scales (MBS) is developed as a means to logically aggregate uncertain input information and to propagate that information through the model to a set of results that are scrutable, easily interpretable by the nonexpert, and logically consistent with the available input information. The MBS, particularly in conjunction with sensitivity analyses, has the potential to be more computationally efficient than other risk methodologies. The regulatory language must be tailored to the specific risk methodology if ambiguity and conflict are to be avoided.« less

Probabilistic Modeling of Settlement Risk at Land Disposal Facilities - 12304

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

Foye, Kevin C.; Soong, Te-Yang

2012-07-01

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

Probabilistic Structural Analysis Program

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.; Chamis, Christos C.; Murthy, Pappu L. N.; Stefko, George L.; Riha, David S.; Thacker, Ben H.; Nagpal, Vinod K.; Mital, Subodh K.

2010-01-01

NASA/NESSUS 6.2c is a general-purpose, probabilistic analysis program that computes probability of failure and probabilistic sensitivity measures of engineered systems. Because NASA/NESSUS uses highly computationally efficient and accurate analysis techniques, probabilistic solutions can be obtained even for extremely large and complex models. Once the probabilistic response is quantified, the results can be used to support risk-informed decisions regarding reliability for safety-critical and one-of-a-kind systems, as well as for maintaining a level of quality while reducing manufacturing costs for larger-quantity products. NASA/NESSUS has been successfully applied to a diverse range of problems in aerospace, gas turbine engines, biomechanics, pipelines, defense, weaponry, and infrastructure. This program combines state-of-the-art probabilistic algorithms with general-purpose structural analysis and lifting methods to compute the probabilistic response and reliability of engineered structures. Uncertainties in load, material properties, geometry, boundary conditions, and initial conditions can be simulated. The structural analysis methods include non-linear finite-element methods, heat-transfer analysis, polymer/ceramic matrix composite analysis, monolithic (conventional metallic) materials life-prediction methodologies, boundary element methods, and user-written subroutines. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. NASA/NESSUS 6.2c is structured in a modular format with 15 elements.

Value and role of intensive care unit outcome prediction models in end-of-life decision making.

PubMed

Barnato, Amber E; Angus, Derek C

2004-07-01

In the United States, intensive care unit (ICU) admission at the end of life is commonplace. What is the value and role of ICU mortality prediction models for informing the utility of ICU care?In this article, we review the history, statistical underpinnings,and current deployment of these models in clinical care. We conclude that the use of outcome prediction models to ration care that is unlikely to provide an expected benefit is hampered by imperfect performance, the lack of real-time availability, failure to consider functional outcomes beyond survival, and physician resistance to the use of probabilistic information when death is guaranteed by the decision it informs. Among these barriers, the most important technical deficiency is the lack of automated information systems to provide outcome predictions to decision makers, and the most important research and policy agenda is to understand and address our national ambivalence toward rationing care based on any criterion.

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

NASA Astrophysics Data System (ADS)

Wilby, R. L.

2008-12-01

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

Probabilistic seismic hazard estimates incorporating site effects - An example from Indiana, U.S.A

USGS Publications Warehouse

Hasse, J.S.; Park, C.H.; Nowack, R.L.; Hill, J.R.

2010-01-01

The U.S. Geological Survey (USGS) has published probabilistic earthquake hazard maps for the United States based on current knowledge of past earthquake activity and geological constraints on earthquake potential. These maps for the central and eastern United States assume standard site conditions with Swave velocities of 760 m/s in the top 30 m. For urban and infrastructure planning and long-term budgeting, the public is interested in similar probabilistic seismic hazard maps that take into account near-surface geological materials. We have implemented a probabilistic method for incorporating site effects into the USGS seismic hazard analysis that takes into account the first-order effects of the surface geologic conditions. The thicknesses of sediments, which play a large role in amplification, were derived from a P-wave refraction database with over 13, 000 profiles, and a preliminary geology-based velocity model was constructed from available information on S-wave velocities. An interesting feature of the preliminary hazard maps incorporating site effects is the approximate factor of two increases in the 1-Hz spectral acceleration with 2 percent probability of exceedance in 50 years for parts of the greater Indianapolis metropolitan region and surrounding parts of central Indiana. This effect is primarily due to the relatively thick sequence of sediments infilling ancient bedrock topography that has been deposited since the Pleistocene Epoch. As expected, the Late Pleistocene and Holocene depositional systems of the Wabash and Ohio Rivers produce additional amplification in the southwestern part of Indiana. Ground motions decrease, as would be expected, toward the bedrock units in south-central Indiana, where motions are significantly lower than the values on the USGS maps.

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.

Environmental probabilistic quantitative assessment methodologies

USGS Publications Warehouse

Crovelli, R.A.

1995-01-01

In this paper, four petroleum resource assessment methodologies are presented as possible pollution assessment methodologies, even though petroleum as a resource is desirable, whereas pollution is undesirable. A methodology is defined in this paper to consist of a probability model and a probabilistic method, where the method is used to solve the model. The following four basic types of probability models are considered: 1) direct assessment, 2) accumulation size, 3) volumetric yield, and 4) reservoir engineering. Three of the four petroleum resource assessment methodologies were written as microcomputer systems, viz. TRIAGG for direct assessment, APRAS for accumulation size, and FASPU for reservoir engineering. A fourth microcomputer system termed PROBDIST supports the three assessment systems. The three assessment systems have different probability models but the same type of probabilistic method. The type of advantages of the analytic method are in computational speed and flexibility, making it ideal for a microcomputer. -from Author

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

Don't Fear Optimality: Sampling for Probabilistic-Logic Sequence Models

NASA Astrophysics Data System (ADS)

Thon, Ingo

One of the current challenges in artificial intelligence is modeling dynamic environments that change due to the actions or activities undertaken by people or agents. The task of inferring hidden states, e.g. the activities or intentions of people, based on observations is called filtering. Standard probabilistic models such as Dynamic Bayesian Networks are able to solve this task efficiently using approximative methods such as particle filters. However, these models do not support logical or relational representations. The key contribution of this paper is the upgrade of a particle filter algorithm for use with a probabilistic logical representation through the definition of a proposal distribution. The performance of the algorithm depends largely on how well this distribution fits the target distribution. We adopt the idea of logical compilation into Binary Decision Diagrams for sampling. This allows us to use the optimal proposal distribution which is normally prohibitively slow.

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

USGS Publications Warehouse

Parsons, Thomas E.; Geist, Eric L.

2009-01-01

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

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

NASA Astrophysics Data System (ADS)

Guan, Xuefei; Zhang, Jingdan; Zhou, S. Kevin; Rasselkorde, El Mahjoub; Abbasi, Waheed A.

2014-02-01

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

Fatigue crack growth model RANDOM2 user manual. Appendix 1: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN program RANDOM2 is presented in the form of a user's manual. RANDOM2 is based on fracture mechanics using a probabilistic fatigue crack growth model. It predicts the random lifetime of an engine component to reach a given crack size. Details of the theoretical background, input data instructions, and a sample problem illustrating the use of the program are included.

Fatigue strength reduction model: RANDOM3 and RANDOM4 user manual. Appendix 2: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN programs RANDOM3 and RANDOM4 are documented in the form of a user's manual. Both programs are based on fatigue strength reduction, using a probabilistic constitutive model. The programs predict the random lifetime of an engine component to reach a given fatigue strength. The theoretical backgrounds, input data instructions, and sample problems illustrating the use of the programs are included.

Bayesian Probabilistic Projection of International Migration.

PubMed

Azose, Jonathan J; Raftery, Adrian E

2015-10-01

We propose a method for obtaining joint probabilistic projections of migration for all countries, broken down by age and sex. Joint trajectories for all countries are constrained to satisfy the requirement of zero global net migration. We evaluate our model using out-of-sample validation and compare point projections to the projected migration rates from a persistence model similar to the method used in the United Nations' World Population Prospects, and also to a state-of-the-art gravity model.

Bell's Inequality: Revolution in Quantum Physics or Just AN Inadequate Mathematical Model?

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

The main aim of this review is to stress the role of mathematical models in physics. The Bell inequality (BI) is often called the "most famous inequality of the 20th century." It is commonly accepted that its violation in corresponding experiments induced a revolution in quantum physics. Unlike "old quantum mechanics" (of Einstein, Schrodinger Bohr, Heisenberg, Pauli, Landau, Fock), "modern quantum mechanics" (of Bell, Aspect, Zeilinger, Shimony, Green-berger, Gisin, Mermin) takes seriously so called quantum non-locality. We will show that the conclusion that one has to give up the realism (i.e., a possibility to assign results of measurements to physical systems) or the locality (i.e., to assume action at a distance) is heavily based on one special mathematical model. This model was invented by A. N. Kolmogorov in 1933. One should pay serious attention to the role of mathematical models in physics. The problems of the realism and locality induced by Bell's argument can be solved by using non-Kolmogorovian probabilistic models. We compare this situation with non-Euclidean geometric models in relativity theory.

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.

Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

PubMed

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.

Modelling default and likelihood reasoning as probabilistic

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A probabilistic analysis of plausible reasoning about defaults and about likelihood is presented. 'Likely' and 'by default' are in fact treated as duals in the same sense as 'possibility' and 'necessity'. To model these four forms probabilistically, a logic QDP and its quantitative counterpart DP are derived that allow qualitative and corresponding quantitative reasoning. Consistency and consequence results for subsets of the logics are given that require at most a quadratic number of satisfiability tests in the underlying propositional logic. The quantitative logic shows how to track the propagation error inherent in these reasoning forms. The methodology and sound framework of the system highlights their approximate nature, the dualities, and the need for complementary reasoning about relevance.

The application of probabilistic design theory to high temperature low cycle fatigue

NASA Technical Reports Server (NTRS)

Wirsching, P. H.

1981-01-01

Metal fatigue under stress and thermal cycling is a principal mode of failure in gas turbine engine hot section components such as turbine blades and disks and combustor liners. Designing for fatigue is subject to considerable uncertainty, e.g., scatter in cycles to failure, available fatigue test data and operating environment data, uncertainties in the models used to predict stresses, etc. Methods of analyzing fatigue test data for probabilistic design purposes are summarized. The general strain life as well as homo- and hetero-scedastic models are considered. Modern probabilistic design theory is reviewed and examples are presented which illustrate application to reliability analysis of gas turbine engine components.

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.

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

Probabilistic Model Development

NASA Technical Reports Server (NTRS)

Adam, James H., Jr.

2010-01-01

Objective: Develop a Probabilistic Model for the Solar Energetic Particle Environment. Develop a tool to provide a reference solar particle radiation environment that: 1) Will not be exceeded at a user-specified confidence level; 2) Will provide reference environments for: a) Peak flux; b) Event-integrated fluence; and c) Mission-integrated fluence. The reference environments will consist of: a) Elemental energy spectra; b) For protons, helium and heavier ions.

Probabilistic Exposure Analysis for Chemical Risk Characterization

PubMed Central

Bogen, Kenneth T.; Cullen, Alison C.; Frey, H. Christopher; Price, Paul S.

2009-01-01

This paper summarizes the state of the science of probabilistic exposure assessment (PEA) as applied to chemical risk characterization. Current probabilistic risk analysis methods applied to PEA are reviewed. PEA within the context of risk-based decision making is discussed, including probabilistic treatment of related uncertainty, interindividual heterogeneity, and other sources of variability. Key examples of recent experience gained in assessing human exposures to chemicals in the environment, and other applications to chemical risk characterization and assessment, are presented. It is concluded that, although improvements continue to be made, existing methods suffice for effective application of PEA to support quantitative analyses of the risk of chemically induced toxicity that play an increasing role in key decision-making objectives involving health protection, triage, civil justice, and criminal justice. Different types of information required to apply PEA to these different decision contexts are identified, and specific PEA methods are highlighted that are best suited to exposure assessment in these separate contexts. PMID:19223660

Probabilistic Finite Element Analysis & Design Optimization for Structural Designs

NASA Astrophysics Data System (ADS)

Deivanayagam, Arumugam

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

Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding.

PubMed

Rios, Anthony; Kavuluru, Ramakanth

2013-09-01

Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.

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

NASA Astrophysics Data System (ADS)

Xu, Lei; Zhai, Wanming; Gao, Jianmin

2017-11-01

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

Testing for ontological errors in probabilistic forecasting models of natural systems

PubMed Central

Marzocchi, Warner; Jordan, Thomas H.

2014-01-01

Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not. PMID:25097265

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

A Tutorial on Probablilistic Risk Assessement and its Role in Risk-Informed Decision Making

NASA Technical Reports Server (NTRS)

Dezfuli, Homayoon

2010-01-01

This slide presentation reviews risk assessment and its role in risk-informed decision making. It includes information on probabilistic risk assessment, typical risk management process, origins of risk matrix, performance measures, performance objectives and Bayes theorem.

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

NASA Astrophysics Data System (ADS)

Naseri Kouzehgarani, Asal

2009-12-01

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

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

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.

Probabilistic failure assessment with application to solid rocket motors

NASA Technical Reports Server (NTRS)

Jan, Darrell L.; Davidson, Barry D.; Moore, Nicholas R.

1990-01-01

A quantitative methodology is being developed for assessment of risk of failure of solid rocket motors. This probabilistic methodology employs best available engineering models and available information in a stochastic framework. The framework accounts for incomplete knowledge of governing parameters, intrinsic variability, and failure model specification error. Earlier case studies have been conducted on several failure modes of the Space Shuttle Main Engine. Work in progress on application of this probabilistic approach to large solid rocket boosters such as the Advanced Solid Rocket Motor for the Space Shuttle is described. Failure due to debonding has been selected as the first case study for large solid rocket motors (SRMs) since it accounts for a significant number of historical SRM failures. Impact of incomplete knowledge of governing parameters and failure model specification errors is expected to be important.

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.

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Arora, Nimar

2013-04-01

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

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

NASA Technical Reports Server (NTRS)

Bast, Callie C.; Boyce, Lola

1995-01-01

The development of methodology for a probabilistic material strength degradation is described. 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 five effects that typically reduce lifetime strength: high temperature, high-cycle mechanical fatigue, low-cycle mechanical fatigue, creep and thermal fatigue. 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 predictions of high-cycle mechanical fatigue and high temperature effects with experiments are presented. Results from this limited verification study strongly supported that material degradation can be represented by randomized multifactor interaction models.

A Practical Probabilistic Graphical Modeling Tool for Weighing Ecological Risk-Based Evidence

EPA Science Inventory

Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for e...

The cerebellum and decision making under uncertainty.

PubMed

Blackwood, Nigel; Ffytche, Dominic; Simmons, Andrew; Bentall, Richard; Murray, Robin; Howard, Robert

2004-06-01

This study aimed to identify the neural basis of probabilistic reasoning, a type of inductive inference that aids decision making under conditions of uncertainty. Eight normal subjects performed two separate two-alternative-choice tasks (the balls in a bottle and personality survey tasks) while undergoing functional magnetic resonance imaging (fMRI). The experimental conditions within each task were chosen so that they differed only in their requirement to make a decision under conditions of uncertainty (probabilistic reasoning and frequency determination required) or under conditions of certainty (frequency determination required). The same visual stimuli and motor responses were used in the experimental conditions. We provide evidence that the neo-cerebellum, in conjunction with the premotor cortex, inferior parietal lobule and medial occipital cortex, mediates the probabilistic inferences that guide decision making under uncertainty. We hypothesise that the neo-cerebellum constructs internal working models of uncertain events in the external world, and that such probabilistic models subserve the predictive capacity central to induction. Copyright 2004 Elsevier B.V.

Probabilistic sensitivity analysis for decision trees with multiple branches: use of the Dirichlet distribution in a Bayesian framework.

PubMed

Briggs, Andrew H; Ades, A E; Price, Martin J

2003-01-01

In structuring decision models of medical interventions, it is commonly recommended that only 2 branches be used for each chance node to avoid logical inconsistencies that can arise during sensitivity analyses if the branching probabilities do not sum to 1. However, information may be naturally available in an unconditional form, and structuring a tree in conditional form may complicate rather than simplify the sensitivity analysis of the unconditional probabilities. Current guidance emphasizes using probabilistic sensitivity analysis, and a method is required to provide probabilistic probabilities over multiple branches that appropriately represents uncertainty while satisfying the requirement that mutually exclusive event probabilities should sum to 1. The authors argue that the Dirichlet distribution, the multivariate equivalent of the beta distribution, is appropriate for this purpose and illustrate its use for generating a fully probabilistic transition matrix for a Markov model. Furthermore, they demonstrate that by adopting a Bayesian approach, the problem of observing zero counts for transitions of interest can be overcome.

Probabilistic wind/tornado/missile analyses for hazard and fragility evaluations

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

Park, Y.J.; Reich, M.

Detailed analysis procedures and examples are presented for the probabilistic evaluation of hazard and fragility against high wind, tornado, and tornado-generated missiles. In the tornado hazard analysis, existing risk models are modified to incorporate various uncertainties including modeling errors. A significant feature of this paper is the detailed description of the Monte-Carlo simulation analyses of tornado-generated missiles. A simulation procedure, which includes the wind field modeling, missile injection, solution of flight equations, and missile impact analysis, is described with application examples.

Dynamic competitive probabilistic principal components analysis.

PubMed

López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

2009-04-01

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

Confronting uncertainty in flood damage predictions

NASA Astrophysics Data System (ADS)

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

2015-04-01

Reliable flood damage models are a prerequisite for the practical usefulness of the model results. 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. 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 and 2006, 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.

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.

Automated Text Data Mining Analysis of Five Decades of Educational Leadership Research Literature: Probabilistic Topic Modeling of "EAQ" Articles From 1965 to 2014

ERIC Educational Resources Information Center

Wang, Yinying; Bowers, Alex J.; Fikis, David J.

2017-01-01

Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in…

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.

Probabilistic Climate Scenario Information for Risk Assessment

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Takayabu, I.

2014-12-01

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 Japan, we compared the physics ensemble experiments using the 60km global atmospheric model of the Meteorological Research Institute (MRI-AGCM) with multi-model ensemble experiments with global atmospheric-ocean coupled models (CMIP3) of SRES A1b scenario experiments. The MRI-AGCM shows relatively good skills particularly in tropics for temperature and geopotential height. Variability in surface air temperature of physical ensemble experiments with MRI-AGCM was within the range of one standard deviation of the CMIP3 model in the Asia region. On the other hand, the variability of precipitation was relatively well represented compared with the variation of the CMIP3 models. Models which show the similar reproducibility in the present climate shows different future climate change. We couldn't find clear relationships between present climate and future climate change in temperature and precipitation. We develop a new method to produce probabilistic information of climate change scenarios by weighting model ensemble experiments based on a regression model (Krishnamurti et al., Science, 1999). 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. The prototype of probabilistic information in Japan represents the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Acknowledgments: This study was supported by the SOUSEI Program, funded by Ministry of Education, Culture, Sports, Science and Technology, Government of Japan.

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

NASA Technical Reports Server (NTRS)

Warner, James E.; Zubair, Mohammad; Ranjan, Desh

2017-01-01

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

The Real and the Mathematical in Quantum Modeling: From Principles to Models and from Models to Principles

NASA Astrophysics Data System (ADS)

Plotnitsky, Arkady

2017-06-01

The history of mathematical modeling outside physics has been dominated by the use of classical mathematical models, C-models, primarily those of a probabilistic or statistical nature. More recently, however, quantum mathematical models, Q-models, based in the mathematical formalism of quantum theory have become more prominent in psychology, economics, and decision science. The use of Q-models in these fields remains controversial, in part because it is not entirely clear whether Q-models are necessary for dealing with the phenomena in question or whether C-models would still suffice. My aim, however, is not to assess the necessity of Q-models in these fields, but instead to reflect on what the possible applicability of Q-models may tell us about the corresponding phenomena there, vis-à-vis quantum phenomena in physics. In order to do so, I shall first discuss the key reasons for the use of Q-models in physics. In particular, I shall examine the fundamental principles that led to the development of quantum mechanics. Then I shall consider a possible role of similar principles in using Q-models outside physics. Psychology, economics, and decision science borrow already available Q-models from quantum theory, rather than derive them from their own internal principles, while quantum mechanics was derived from such principles, because there was no readily available mathematical model to handle quantum phenomena, although the mathematics ultimately used in quantum did in fact exist then. I shall argue, however, that the principle perspective on mathematical modeling outside physics might help us to understand better the role of Q-models in these fields and possibly to envision new models, conceptually analogous to but mathematically different from those of quantum theory, helpful or even necessary there or in physics itself. I shall suggest one possible type of such models, singularized probabilistic, SP, models, some of which are time-dependent, TDSP-models. The necessity of using such models may change the nature of mathematical modeling in science and, thus, the nature of science, as it happened in the case of Q-models, which not only led to a revolutionary transformation of physics but also opened new possibilities for scientific thinking and mathematical modeling beyond physics.

The meta-Gaussian Bayesian Processor of forecasts and associated preliminary experiments

NASA Astrophysics Data System (ADS)

Chen, Fajing; Jiao, Meiyan; Chen, Jing

2013-04-01

Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts.

Cancer evolution: mathematical models and computational inference.

PubMed

Beerenwinkel, Niko; Schwarz, Roland F; Gerstung, Moritz; Markowetz, Florian

2015-01-01

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society of Systematic Biologists.

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

Encoding probabilistic brain atlases using Bayesian inference.

PubMed

Van Leemput, Koen

2009-06-01

This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.

The role of linguistic experience in the processing of probabilistic information in production.

PubMed

Gustafson, Erin; Goldrick, Matthew

2018-01-01

Speakers track the probability that a word will occur in a particular context and utilize this information during phonetic processing. For example, content words that have high probability within a discourse tend to be realized with reduced acoustic/articulatory properties. Such probabilistic information may influence L1 and L2 speech processing in distinct ways (reflecting differences in linguistic experience across groups and the overall difficulty of L2 speech processing). To examine this issue, L1 and L2 speakers performed a referential communication task, describing sequences of simple actions. The two groups of speakers showed similar effects of discourse-dependent probabilistic information on production, suggesting that L2 speakers can successfully track discourse-dependent probabilities and use such information to modulate phonetic processing.

An Instructional Module on Mokken Scale Analysis

ERIC Educational Resources Information Center

Wind, Stefanie A.

2017-01-01

Mokken scale analysis (MSA) is a probabilistic-nonparametric approach to item response theory (IRT) that can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. This instructional module provides an introduction to MSA as a probabilistic-nonparametric framework in which to explore…

Lossed in translation: an off-the-shelf method to recover probabilistic beliefs from loss-averse agents.

PubMed

Offerman, Theo; Palley, Asa B

2016-01-01

Strictly proper scoring rules are designed to truthfully elicit subjective probabilistic beliefs from risk neutral agents. Previous experimental studies have identified two problems with this method: (i) risk aversion causes agents to bias their reports toward the probability of [Formula: see text], and (ii) for moderate beliefs agents simply report [Formula: see text]. Applying a prospect theory model of risk preferences, we show that loss aversion can explain both of these behavioral phenomena. Using the insights of this model, we develop a simple off-the-shelf probability assessment mechanism that encourages loss-averse agents to report true beliefs. In an experiment, we demonstrate the effectiveness of this modification in both eliminating uninformative reports and eliciting true probabilistic beliefs.

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2010-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multifactor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

Probabilistic Methods for Structural Reliability and Risk

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A probabilistic method is used to evaluate the structural reliability and risk of select metallic and composite structures. The method is a multiscale, multifunctional and it is based on the most elemental level. A multi-factor interaction model is used to describe the material properties which are subsequently evaluated probabilistically. The metallic structure is a two rotor aircraft engine, while the composite structures consist of laminated plies (multiscale) and the properties of each ply are the multifunctional representation. The structural component is modeled by finite element. The solution method for structural responses is obtained by an updated simulation scheme. The results show that the risk for the two rotor engine is about 0.0001 and the composite built-up structure is also 0.0001.

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

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

PubMed

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

2015-11-01

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

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

NASA Astrophysics Data System (ADS)

de'Michieli Vitturi, Mattia; Tarquini, Simone

2018-01-01

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

Probabilistic distributions of pinhole defects in atomic layer deposited films on polymeric substrates

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

Yersak, Alexander S., E-mail: alexander.yersak@colorado.edu; Lee, Yung-Cheng

Pinhole defects in atomic layer deposition (ALD) coatings were measured in an area of 30 cm{sup 2} in an ALD reactor, and these defects were represented by a probabilistic cluster model instead of a single defect density value with number of defects over area. With the probabilistic cluster model, the pinhole defects were simulated over a manufacturing scale surface area of ∼1 m{sup 2}. Large-area pinhole defect simulations were used to develop an improved and enhanced design method for ALD-based devices. A flexible thermal ground plane (FTGP) device requiring ALD hermetic coatings was used as an example. Using a single defectmore » density value, it was determined that for an application with operation temperatures higher than 60 °C, the FTGP device would not be possible. The new probabilistic cluster model shows that up to 40.3% of the FTGP would be acceptable. With this new approach the manufacturing yield of ALD-enabled or other thin film based devices with different design configurations can be determined. It is important to guide process optimization and control and design for manufacturability.« less

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

Effects of sample survey design on the accuracy of classification tree models in species distribution models

USGS Publications Warehouse

Edwards, T.C.; Cutler, D.R.; Zimmermann, N.E.; Geiser, L.; Moisen, Gretchen G.

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

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.

Probabilistic fatigue life prediction of metallic and composite materials

NASA Astrophysics Data System (ADS)

Xiang, Yibing

Fatigue is one of the most common failure modes for engineering structures, such as aircrafts, rotorcrafts and aviation transports. Both metallic materials and composite materials are widely used and affected by fatigue damage. Huge uncertainties arise from material properties, measurement noise, imperfect models, future anticipated loads and environmental conditions. These uncertainties are critical issues for accurate remaining useful life (RUL) prediction for engineering structures in service. Probabilistic fatigue prognosis considering various uncertainties is of great importance for structural safety. The objective of this study is to develop probabilistic fatigue life prediction models for metallic materials and composite materials. A fatigue model based on crack growth analysis and equivalent initial flaw size concept is proposed for metallic materials. Following this, the developed model is extended to include structural geometry effects (notch effect), environmental effects (corroded specimens) and manufacturing effects (shot peening effects). Due to the inhomogeneity and anisotropy, the fatigue model suitable for metallic materials cannot be directly applied to composite materials. A composite fatigue model life prediction is proposed based on a mixed-mode delamination growth model and a stiffness degradation law. After the development of deterministic fatigue models of metallic and composite materials, a general probabilistic life prediction methodology is developed. The proposed methodology combines an efficient Inverse First-Order Reliability Method (IFORM) for the uncertainty propogation in fatigue life prediction. An equivalent stresstransformation has been developed to enhance the computational efficiency under realistic random amplitude loading. A systematical reliability-based maintenance optimization framework is proposed for fatigue risk management and mitigation of engineering structures.

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

PubMed Central

Praveen, Paurush; Fröhlich, Holger

2013-01-01

Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available. PMID:23826291

Tsunamigenic scenarios for southern Peru and northern Chile seismic gap: Deterministic and probabilistic hybrid approach for hazard assessment

NASA Astrophysics Data System (ADS)

González-Carrasco, J. F.; Gonzalez, G.; Aránguiz, R.; Yanez, G. A.; Melgar, D.; Salazar, P.; Shrivastava, M. N.; Das, R.; Catalan, P. A.; Cienfuegos, R.

2017-12-01

Plausible worst-case tsunamigenic scenarios definition plays a relevant role in tsunami hazard assessment focused in emergency preparedness and evacuation planning for coastal communities. During the last decade, the occurrence of major and moderate tsunamigenic earthquakes along worldwide subduction zones has given clues about critical parameters involved in near-field tsunami inundation processes, i.e. slip spatial distribution, shelf resonance of edge waves and local geomorphology effects. To analyze the effects of these seismic and hydrodynamic variables over the epistemic uncertainty of coastal inundation, we implement a combined methodology using deterministic and probabilistic approaches to construct 420 tsunamigenic scenarios in a mature seismic gap of southern Peru and northern Chile, extended from 17ºS to 24ºS. The deterministic scenarios are calculated using a regional distribution of trench-parallel gravity anomaly (TPGA) and trench-parallel topography anomaly (TPTA), three-dimensional Slab 1.0 worldwide subduction zones geometry model and published interseismic coupling (ISC) distributions. As result, we find four higher slip deficit zones interpreted as major seismic asperities of the gap, used in a hierarchical tree scheme to generate ten tsunamigenic scenarios with seismic magnitudes fluctuates between Mw 8.4 to Mw 8.9. Additionally, we construct ten homogeneous slip scenarios as inundation baseline. For the probabilistic approach, we implement a Karhunen - Loève expansion to generate 400 stochastic tsunamigenic scenarios over the maximum extension of the gap, with the same magnitude range of the deterministic sources. All the scenarios are simulated through a non-hydrostatic tsunami model Neowave 2D, using a classical nesting scheme, for five coastal major cities in northern Chile (Arica, Iquique, Tocopilla, Mejillones and Antofagasta) obtaining high resolution data of inundation depth, runup, coastal currents and sea level elevation. The probabilistic kinematic tsunamigenic scenarios give a more realistic slip patterns, similar to maximum slip amount of major past earthquakes. For all studied sites, the peak of slip location and shelf resonance is a first order control for the observed coastal inundation depths results.

Development of probabilistic thinking-oriented learning tools for probability materials at junior high school students

NASA Astrophysics Data System (ADS)

Sari, Dwi Ivayana; Hermanto, Didik

2017-08-01

This research is a developmental research of probabilistic thinking-oriented learning tools for probability materials at ninth grade students. This study is aimed to produce a good probabilistic thinking-oriented learning tools. The subjects were IX-A students of MTs Model Bangkalan. The stages of this development research used 4-D development model which has been modified into define, design and develop. Teaching learning tools consist of lesson plan, students' worksheet, learning teaching media and students' achievement test. The research instrument used was a sheet of learning tools validation, a sheet of teachers' activities, a sheet of students' activities, students' response questionnaire and students' achievement test. The result of those instruments were analyzed descriptively to answer research objectives. The result was teaching learning tools in which oriented to probabilistic thinking of probability at ninth grade students which has been valid. Since teaching and learning tools have been revised based on validation, and after experiment in class produced that teachers' ability in managing class was effective, students' activities were good, students' responses to the learning tools were positive and the validity, sensitivity and reliability category toward achievement test. In summary, this teaching learning tools can be used by teacher to teach probability for develop students' probabilistic thinking.

Conceptual Tools for Understanding Nature - Proceedings of the 3rd International Symposium

NASA Astrophysics Data System (ADS)

Costa, G.; Calucci, M.

1997-04-01

The Table of Contents for the full book PDF is as follows: * Foreword * Some Limits of Science and Scientists * Three Limits of Scientific Knowledge * On Features and Meaning of Scientific Knowledge * How Science Approaches the World: Risky Truths versus Misleading Certitudes * On Discovery and Justification * Thought Experiments: A Philosophical Analysis * Causality: Epistemological Questions and Cognitive Answers * Scientific Inquiry via Rational Hypothesis Revision * Probabilistic Epistemology * The Transferable Belief Model for Uncertainty Representation * Chemistry and Complexity * The Difficult Epistemology of Medicine * Epidemiology, Causality and Medical Anthropology * Conceptual Tools for Transdisciplinary Unified Theory * Evolution and Learning in Economic Organizations * The Possible Role of Symmetry in Physics and Cosmology * Observational Cosmology and/or other Imaginable Models of the Universe

Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework

NASA Astrophysics Data System (ADS)

Ben Alaya, M. A.; Ouarda, T. B. M. J.; Chebana, F.

2018-01-01

Probabilistic regression approaches for downscaling daily precipitation are very useful. They provide the whole conditional distribution at each forecast step to better represent the temporal variability. The question addressed in this paper is: how to simulate spatiotemporal characteristics of multisite daily precipitation from probabilistic regression models? Recent publications point out the complexity of multisite properties of daily precipitation and highlight the need for using a non-Gaussian flexible tool. This work proposes a reasonable compromise between simplicity and flexibility avoiding model misspecification. A suitable nonparametric bootstrapping (NB) technique is adopted. A downscaling model which merges a vector generalized linear model (VGLM as a probabilistic regression tool) and the proposed bootstrapping technique is introduced to simulate realistic multisite precipitation series. The model is applied to data sets from the southern part of the province of Quebec, Canada. It is shown that the model is capable of reproducing both at-site properties and the spatial structure of daily precipitations. Results indicate the superiority of the proposed NB technique, over a multivariate autoregressive Gaussian framework (i.e. Gaussian copula).

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 Priority Message Checking Modeling Based on Controller Area Networks

NASA Astrophysics Data System (ADS)

Lin, Cheng-Min

Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.

Probabilistic Modeling of Intracranial Pressure Effects on Optic Nerve Biomechanics

NASA Technical Reports Server (NTRS)

Ethier, C. R.; Feola, Andrew J.; Raykin, Julia; Myers, Jerry G.; Nelson, Emily S.; Samuels, Brian C.

2016-01-01

Altered intracranial pressure (ICP) is involved/implicated in several ocular conditions: papilledema, glaucoma and Visual Impairment and Intracranial Pressure (VIIP) syndrome. The biomechanical effects of altered ICP on optic nerve head (ONH) tissues in these conditions are uncertain but likely important. We have quantified ICP-induced deformations of ONH tissues, using finite element (FE) and probabilistic modeling (Latin Hypercube Simulations (LHS)) to consider a range of tissue properties and relevant pressures.

Geothermal probabilistic cost study

NASA Technical Reports Server (NTRS)

Orren, L. H.; Ziman, G. M.; Jones, S. C.; Lee, T. K.; Noll, R.; Wilde, L.; Sadanand, V.

1981-01-01

A tool is presented to quantify the risks of geothermal projects, the Geothermal Probabilistic Cost Model (GPCM). The GPCM model was used to evaluate a geothermal reservoir for a binary-cycle electric plant at Heber, California. Three institutional aspects of the geothermal risk which can shift the risk among different agents was analyzed. The leasing of geothermal land, contracting between the producer and the user of the geothermal heat, and insurance against faulty performance were examined.

MATILDA Version 2: Rough Earth TIALD Model for Laser Probabilistic Risk Assessment in Hilly Terrain - Part I

DTIC Science & Technology

2017-03-13

support of airborne laser designator use during test and training exercises on military ranges. The initial MATILDA tool, MATILDA PRO Version-1.6.1...was based on the 2007 PRA model developed to perform range safety clearances for the UK Thermal Imaging Airborne Laser Designator (TIALD) system...AFRL Technical Reports. This Technical Report, designated Part I, con- tains documentation of the computational procedures for probabilistic fault

Rats bred for high alcohol drinking are more sensitive to delayed and probabilistic outcomes.

PubMed

Wilhelm, C J; Mitchell, S H

2008-10-01

Alcoholics and heavy drinkers score higher on measures of impulsivity than nonalcoholics and light drinkers. This may be because of factors that predate drug exposure (e.g. genetics). This study examined the role of genetics by comparing impulsivity measures in ethanol-naive rats selectively bred based on their high [high alcohol drinking (HAD)] or low [low alcohol drinking (LAD)] consumption of ethanol. Replicates 1 and 2 of the HAD and LAD rats, developed by the University of Indiana Alcohol Research Center, completed two different discounting tasks. Delay discounting examines sensitivity to rewards that are delayed in time and is commonly used to assess 'choice' impulsivity. Probability discounting examines sensitivity to the uncertain delivery of rewards and has been used to assess risk taking and risk assessment. High alcohol drinking rats discounted delayed and probabilistic rewards more steeply than LAD rats. Discount rates associated with probabilistic and delayed rewards were weakly correlated, while bias was strongly correlated with discount rate in both delay and probability discounting. The results suggest that selective breeding for high alcohol consumption selects for animals that are more sensitive to delayed and probabilistic outcomes. Sensitivity to delayed or probabilistic outcomes may be predictive of future drinking in genetically predisposed individuals.

Selective Estrogen Receptor Modulation Increases Hippocampal Activity during Probabilistic Association Learning in Schizophrenia

PubMed Central

Kindler, Jochen; Weickert, Cynthia Shannon; Skilleter, Ashley J; Catts, Stanley V; Lenroot, Rhoshel; Weickert, Thomas W

2015-01-01

People with schizophrenia show probabilistic association learning impairment in conjunction with abnormal neural activity. The selective estrogen receptor modulator (SERM) raloxifene preserves neural activity during memory in healthy older men and improves memory in schizophrenia. Here, we tested the extent to which raloxifene modifies neural activity during learning in schizophrenia. Nineteen people with schizophrenia participated in a twelve-week randomized, double-blind, placebo-controlled, cross-over adjunctive treatment trial of the SERM raloxifene administered orally at 120 mg daily to assess brain activity during probabilistic association learning using functional magnetic resonance imaging (fMRI). Raloxifene improved probabilistic association learning and significantly increased fMRI BOLD activity in the hippocampus and parahippocampal gyrus relative to placebo. A separate region of interest confirmatory analysis in 21 patients vs 36 healthy controls showed a positive association between parahippocampal neural activity and learning in patients, but no such relationship in the parahippocampal gyrus of healthy controls. Thus, selective estrogen receptor modulation by raloxifene concurrently increases activity in the parahippocampal gyrus and improves probabilistic association learning in schizophrenia. These results support a role for estrogen receptor modulation of mesial temporal lobe neural activity in the remediation of learning disabilities in both men and women with schizophrenia. PMID:25829142

Target Coverage in Wireless Sensor Networks with Probabilistic Sensors

PubMed Central

Shan, Anxing; Xu, Xianghua; Cheng, Zongmao

2016-01-01

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm. PMID:27618902

Probabilistic performance-assessment modeling of the mixed waste landfill at Sandia National Laboratories.

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

Peace, Gerald; Goering, Timothy James; Miller, Mark Laverne

2007-01-01

A probabilistic performance assessment has been conducted to evaluate the fate and transport of radionuclides (americium-241, cesium-137, cobalt-60, plutonium-238, plutonium-239, radium-226, radon-222, strontium-90, thorium-232, tritium, uranium-238), heavy metals (lead and cadmium), and volatile organic compounds (VOCs) at the Mixed Waste Landfill (MWL). Probabilistic analyses were performed to quantify uncertainties inherent in the system and models for a 1,000-year period, and sensitivity analyses were performed to identify parameters and processes that were most important to the simulated performance metrics. Comparisons between simulated results and measured values at the MWL were made to gain confidence in the models and perform calibrations whenmore » data were available. In addition, long-term monitoring requirements and triggers were recommended based on the results of the quantified uncertainty and sensitivity analyses.« less

Assessing the empirical validity of the "take-the-best" heuristic as a model of human probabilistic inference.

PubMed

Bröder, A

2000-09-01

The boundedly rational 'Take-The-Best" heuristic (TTB) was proposed by G. Gigerenzer, U. Hoffrage, and H. Kleinbölting (1991) as a model of fast and frugal probabilistic inferences. Although the simple lexicographic rule proved to be successful in computer simulations, direct empirical demonstrations of its adequacy as a psychological model are lacking because of several methodical problems. In 4 experiments with a total of 210 participants, this question was addressed. Whereas Experiment 1 showed that TTB is not valid as a universal hypothesis about probabilistic inferences, up to 28% of participants in Experiment 2 and 53% of participants in Experiment 3 were classified as TTB users. Experiment 4 revealed that investment costs for information seem to be a relevant factor leading participants to switch to a noncompensatory TTB strategy. The observed individual differences in strategy use imply the recommendation of an idiographic approach to decision-making research.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system components, part 2

NASA Technical Reports Server (NTRS)

1991-01-01

The technical effort and computer code enhancements performed during the sixth year of the Probabilistic Structural Analysis Methods program are summarized. Various capabilities are described to probabilistically combine structural response and structural resistance to compute component reliability. A library of structural resistance models is implemented in the Numerical Evaluations of Stochastic Structures Under Stress (NESSUS) code that included fatigue, fracture, creep, multi-factor interaction, and other important effects. In addition, a user interface was developed for user-defined resistance models. An accurate and efficient reliability method was developed and was successfully implemented in the NESSUS code to compute component reliability based on user-selected response and resistance models. A risk module was developed to compute component risk with respect to cost, performance, or user-defined criteria. The new component risk assessment capabilities were validated and demonstrated using several examples. Various supporting methodologies were also developed in support of component risk assessment.

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

PubMed Central

Drugowitsch, Jan; Pouget, Alexandre

2012-01-01

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

Quantum structure in economics: The Ellsberg paradox

NASA Astrophysics Data System (ADS)

Aerts, Diederik; Sozzo, Sandro

2012-03-01

The expected utility hypothesis and Savage's Sure-Thing Principle are violated in real life decisions, as shown by the Allais and Ellsberg paradoxes. The popular explanation in terms of ambiguity aversion is not completely accepted. As a consequence, uncertainty is still problematical in economics. To overcome these difficulties a distinction between risk and ambiguity has been introduced which depends on the existence of a Kolmogorovian probabilistic structure modeling these uncertainties. On the other hand, evidence of everyday life suggests that context plays a fundamental role in human decisions under uncertainty. Moreover, it is well known from physics that any probabilistic structure modeling contextual interactions between entities structurally needs a non-Kolmogorovian framework admitting a quantum-like representation. For this reason, we have recently introduced a notion of contextual risk to mathematically capture situations in which ambiguity occurs. We prove in this paper that the contextual risk approach can be applied to the Ellsberg paradox, and elaborate a sphere model within our hidden measurement formalism which reveals that it is the overall conceptual landscape that is responsible of the disagreement between actual human decisions and the predictions of expected utility theory, which generates the paradox. This result points to the presence of a quantum conceptual layer in human thought which is superposed to the usually assumed classical logical layer, and conceptually supports the thesis of several authors suggesting the presence of quantum structure in economics and decision theory.

A geomorphic approach to 100-year floodplain mapping for the Conterminous United States

NASA Astrophysics Data System (ADS)

Jafarzadegan, Keighobad; Merwade, Venkatesh; Saksena, Siddharth

2018-06-01

Floodplain mapping using hydrodynamic models is difficult in data scarce regions. Additionally, using hydrodynamic models to map floodplain over large stream network can be computationally challenging. Some of these limitations of floodplain mapping using hydrodynamic modeling can be overcome by developing computationally efficient statistical methods to identify floodplains in large and ungauged watersheds using publicly available data. This paper proposes a geomorphic model to generate probabilistic 100-year floodplain maps for the Conterminous United States (CONUS). The proposed model first categorizes the watersheds in the CONUS into three classes based on the height of the water surface corresponding to the 100-year flood from the streambed. Next, the probability that any watershed in the CONUS belongs to one of these three classes is computed through supervised classification using watershed characteristics related to topography, hydrography, land use and climate. The result of this classification is then fed into a probabilistic threshold binary classifier (PTBC) to generate the probabilistic 100-year floodplain maps. The supervised classification algorithm is trained by using the 100-year Flood Insurance Rated Maps (FIRM) from the U.S. Federal Emergency Management Agency (FEMA). FEMA FIRMs are also used to validate the performance of the proposed model in areas not included in the training. Additionally, HEC-RAS model generated flood inundation extents are used to validate the model performance at fifteen sites that lack FEMA maps. Validation results show that the probabilistic 100-year floodplain maps, generated by proposed model, match well with both FEMA and HEC-RAS generated maps. On average, the error of predicted flood extents is around 14% across the CONUS. The high accuracy of the validation results shows the reliability of the geomorphic model as an alternative approach for fast and cost effective delineation of 100-year floodplains for the CONUS.

Disruption of the Right Temporoparietal Junction Impairs Probabilistic Belief Updating.

PubMed

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

2017-05-31

Generating and updating probabilistic models of the environment is a fundamental modus operandi of the human brain. Although crucial for various cognitive functions, the neural mechanisms of these inference processes remain to be elucidated. Here, we show the causal involvement of the right temporoparietal junction (rTPJ) in updating probabilistic beliefs and we provide new insights into the chronometry of the process by combining online transcranial magnetic stimulation (TMS) with computational modeling of behavioral responses. Female and male participants performed a modified location-cueing paradigm, where false information about the percentage of cue validity (%CV) was provided in half of the experimental blocks to prompt updating of prior expectations. Online double-pulse TMS over rTPJ 300 ms (but not 50 ms) after target appearance selectively decreased participants' updating of false prior beliefs concerning %CV, reflected in a decreased learning rate of a Rescorla-Wagner model. Online TMS over rTPJ also impacted on participants' explicit beliefs, causing them to overestimate %CV. These results confirm the involvement of rTPJ in updating of probabilistic beliefs, thereby advancing our understanding of this area's function during cognitive processing. SIGNIFICANCE STATEMENT Contemporary views propose that the brain maintains probabilistic models of the world to minimize surprise about sensory inputs. Here, we provide evidence that the right temporoparietal junction (rTPJ) is causally involved in this process. Because neuroimaging has suggested that rTPJ is implicated in divergent cognitive domains, the demonstration of an involvement in updating internal models provides a novel unifying explanation for these findings. We used computational modeling to characterize how participants change their beliefs after new observations. By interfering with rTPJ activity through online transcranial magnetic stimulation, we showed that participants were less able to update prior beliefs with TMS delivered at 300 ms after target onset. Copyright © 2017 the authors 0270-6474/17/375419-10$15.00/0.

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Feedback-based probabilistic category learning is selectively impaired in attention/hyperactivity deficit disorder.

PubMed

Gabay, Yafit; Goldfarb, Liat

2017-07-01

Although Attention-Deficit Hyperactivity Disorder (ADHD) is closely linked to executive function deficits, it has recently been attributed to procedural learning impairments that are quite distinct from the former. These observations challenge the ability of the executive function framework solely to account for the diverse range of symptoms observed in ADHD. A recent neurocomputational model emphasizes the role of striatal dopamine (DA) in explaining ADHD's broad range of deficits, but the link between this model and procedural learning impairments remains unclear. Significantly, feedback-based procedural learning is hypothesized to be disrupted in ADHD because of the involvement of striatal DA in this type of learning. In order to test this assumption, we employed two variants of a probabilistic category learning task known from the neuropsychological literature. Feedback-based (FB) and paired associate-based (PA) probabilistic category learning were employed in a non-medicated sample of ADHD participants and neurotypical participants. In the FB task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of the response. In the PA learning task, participants viewed the cue and its associated outcome simultaneously without receiving an overt response or corrective feedback. In both tasks, participants were trained across 150 trials. Learning was assessed in a subsequent test without a presentation of the outcome or corrective feedback. Results revealed an interesting disassociation in which ADHD participants performed as well as control participants in the PA task, but were impaired compared with the controls in the FB task. The learning curve during FB training differed between the two groups. Taken together, these results suggest that the ability to incrementally learn by feedback is selectively disrupted in ADHD participants. These results are discussed in relation to both the ADHD dopaminergic dysfunction model and recent findings implicating procedural learning impairments in those with ADHD. Copyright © 2017 Elsevier Inc. All rights reserved.

Quick probabilistic binary image matching: changing the rules of the game

NASA Astrophysics Data System (ADS)

Mustafa, Adnan A. Y.

2016-09-01

A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real images that show the prediction accuracy of the model.

Probabilistic analysis for fatigue strength degradation of materials

NASA Technical Reports Server (NTRS)

Royce, Lola

1989-01-01

This report presents the results of the first year of a research program conducted for NASA-LeRC by the University of Texas at San Antonio. The research included development of methodology that provides a probabilistic treatment of lifetime prediction of structural components of aerospace propulsion systems subjected to fatigue. Material strength degradation models, based on primitive variables, include both a fatigue strength reduction model and a fatigue crack growth model. Linear elastic fracture mechanics is utilized in the latter model. Probabilistic analysis is based on simulation, and both maximum entropy and maximum penalized likelihood methods are used for the generation of probability density functions. The resulting constitutive relationships are included in several computer programs, RANDOM2, RANDOM3, and RANDOM4. These programs determine the random lifetime of an engine component, in mechanical load cycles, to reach a critical fatigue strength or crack size. The material considered was a cast nickel base superalloy, one typical of those used in the Space Shuttle Main Engine.

Grammaticality, Acceptability, and Probability: A Probabilistic View of Linguistic Knowledge.

PubMed

Lau, Jey Han; Clark, Alexander; Lappin, Shalom

2017-07-01

The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary and probabilistic theories of grammaticality. These judgements are gradient in nature, and so cannot be directly accommodated in a binary formal grammar. However, it is also not possible to simply reduce acceptability to probability. The acceptability of a sentence is not the same as the likelihood of its occurrence, which is, in part, determined by factors like sentence length and lexical frequency. In this paper, we present the results of a set of large-scale experiments using crowd-sourced acceptability judgments that demonstrate gradience to be a pervasive feature in acceptability judgments. We then show how one can predict acceptability judgments on the basis of probability by augmenting probabilistic language models with an acceptability measure. This is a function that normalizes probability values to eliminate the confounding factors of length and lexical frequency. We describe a sequence of modeling experiments with unsupervised language models drawn from state-of-the-art machine learning methods in natural language processing. Several of these models achieve very encouraging levels of accuracy in the acceptability prediction task, as measured by the correlation between the acceptability measure scores and mean human acceptability values. We consider the relevance of these results to the debate on the nature of grammatical competence, and we argue that they support the view that linguistic knowledge can be intrinsically probabilistic. Copyright © 2016 Cognitive Science Society, Inc.

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.

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).

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

NASA Astrophysics Data System (ADS)

Li, Z.; Ghaith, M.

2017-12-01

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

Effects of varying the step particle distribution on a probabilistic transport model

NASA Astrophysics Data System (ADS)

Bouzat, S.; Farengo, R.

2005-12-01

The consequences of varying the step particle distribution on a probabilistic transport model, which captures the basic features of transport in plasmas and was recently introduced in Ref. 1 [B. Ph. van Milligen et al., Phys. Plasmas 11, 2272 (2004)], are studied. Different superdiffusive transport mechanisms generated by a family of distributions with algebraic decays (Tsallis distributions) are considered. It is observed that the possibility of changing the superdiffusive transport mechanism improves the flexibility of the model for describing different situations. The use of the model to describe the low (L) and high (H) confinement modes is also analyzed.

Probabilistic biosphere modeling for the long-term safety assessment of geological disposal facilities for radioactive waste using first- and second-order Monte Carlo simulation.

PubMed

Ciecior, Willy; Röhlig, Klaus-Jürgen; Kirchner, Gerald

2018-10-01

In the present paper, deterministic as well as first- and second-order probabilistic biosphere modeling approaches are compared. Furthermore, the sensitivity of the influence of the probability distribution function shape (empirical distribution functions and fitted lognormal probability functions) representing the aleatory uncertainty (also called variability) of a radioecological model parameter as well as the role of interacting parameters are studied. Differences in the shape of the output distributions for the biosphere dose conversion factor from first-order Monte Carlo uncertainty analysis using empirical and fitted lognormal distribution functions for input parameters suggest that a lognormal approximation is possibly not always an adequate representation of the aleatory uncertainty of a radioecological parameter. Concerning the comparison of the impact of aleatory and epistemic parameter uncertainty on the biosphere dose conversion factor, the latter here is described using uncertain moments (mean, variance) while the distribution itself represents the aleatory uncertainty of the parameter. From the results obtained, the solution space of second-order Monte Carlo simulation is much larger than that from first-order Monte Carlo simulation. Therefore, the influence of epistemic uncertainty of a radioecological parameter on the output result is much larger than that one caused by its aleatory uncertainty. Parameter interactions are only of significant influence in the upper percentiles of the distribution of results as well as only in the region of the upper percentiles of the model parameters. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probabilistic computer model of optimal runway turnoffs

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

Probabilistic flood damage modelling at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2014-05-01

Decisions on flood risk management and adaptation are usually based on risk analyses. Such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments. Most damage models have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood damage models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we show how the model BT-FLEMO (Bagging decision Tree based Flood Loss Estimation MOdel) can be applied on the meso-scale, namely on the basis of ATKIS land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany. The application of BT-FLEMO provides a probability distribution of estimated damage to residential buildings per municipality. Validation is undertaken on the one hand via a comparison with eight other damage models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official damage data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of damage estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation model BT-FLEMO is that it inherently provides quantitative information about the uncertainty of the prediction. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64.

Comparative assessment of analytical approaches to quantify the risk for introduction of rare animal diseases: the example of avian influenza in Spain.

PubMed

Sánchez-Vizcaíno, Fernando; Perez, Andrés; Martínez-López, Beatriz; Sánchez-Vizcaíno, José Manuel

2012-08-01

Trade of animals and animal products imposes an uncertain and variable risk for exotic animal diseases introduction into importing countries. Risk analysis provides importing countries with an objective, transparent, and internationally accepted method for assessing that risk. Over the last decades, European Union countries have conducted probabilistic risk assessments quite frequently to quantify the risk for rare animal diseases introduction into their territories. Most probabilistic animal health risk assessments have been typically classified into one-level and multilevel binomial models. One-level models are more simple than multilevel models because they assume that animals or products originate from one single population. However, it is unknown whether such simplification may result in substantially different results compared to those obtained through the use of multilevel models. Here, data used on a probabilistic multilevel binomial model formulated to assess the risk for highly pathogenic avian influenza introduction into Spain were reanalyzed using a one-level binomial model and their outcomes were compared. An alternative ordinal model is also proposed here, which makes use of simpler assumptions and less information compared to those required by traditional one-level and multilevel approaches. Results suggest that, at least under certain circumstances, results of the one-level and ordinal approaches are similar to those obtained using multilevel models. Consequently, we argue that, when data are insufficient to run traditional probabilistic models, the ordinal approach presented here may be a suitable alternative to rank exporting countries in terms of the risk that they impose for the spread of rare animal diseases into disease-free countries. © 2012 Society for Risk Analysis.

SU-E-I-87: Automated Liver Segmentation Method for CBCT Dataset by Combining Sparse Shape Composition and Probabilistic Atlas Construction

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

Li, Dengwang; Liu, Li; Chen, Jinhu

2014-06-01

Purpose: The aiming of this study was to extract liver structures for daily Cone beam CT (CBCT) images automatically. Methods: Datasets were collected from 50 intravenous contrast planning CT images, which were regarded as training dataset for probabilistic atlas and shape prior model construction. Firstly, probabilistic atlas and shape prior model based on sparse shape composition (SSC) were constructed by iterative deformable registration. Secondly, the artifacts and noise were removed from the daily CBCT image by an edge-preserving filtering using total variation with L1 norm (TV-L1). Furthermore, the initial liver region was obtained by registering the incoming CBCT image withmore » the atlas utilizing edge-preserving deformable registration with multi-scale strategy, and then the initial liver region was converted to surface meshing which was registered with the shape model where the major variation of specific patient was modeled by sparse vectors. At the last stage, the shape and intensity information were incorporated into joint probabilistic model, and finally the liver structure was extracted by maximum a posteriori segmentation.Regarding the construction process, firstly the manually segmented contours were converted into meshes, and then arbitrary patient data was chosen as reference image to register with the rest of training datasets by deformable registration algorithm for constructing probabilistic atlas and prior shape model. To improve the efficiency of proposed method, the initial probabilistic atlas was used as reference image to register with other patient data for iterative construction for removing bias caused by arbitrary selection. Results: The experiment validated the accuracy of the segmentation results quantitatively by comparing with the manually ones. The volumetric overlap percentage between the automatically generated liver contours and the ground truth were on an average 88%–95% for CBCT images. Conclusion: The experiment demonstrated that liver structures of CBCT with artifacts can be extracted accurately for following adaptive radiation therapy. This work is supported by National Natural Science Foundation of China (No. 61201441), Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (No. BS2012DX038), Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN23), Jinan youth science and technology star (No.20120109)« less

On a true value of risk

NASA Astrophysics Data System (ADS)

Kozine, Igor

2018-04-01

The paper suggests looking on probabilistic risk quantities and concepts through the prism of accepting one of the views: whether a true value of risk exists or not. It is argued that discussions until now have been primarily focused on closely related topics that are different from the topic of the current paper. The paper examines operational consequences of adhering to each of the views and contrasts them. It is demonstrated that operational differences on how and what probabilistic measures can be assessed and how they can be interpreted appear tangible. In particular, this concerns prediction intervals, the use of Byes rule, models of complete ignorance, hierarchical models of uncertainty, assignment of probabilities over possibility space and interpretation of derived probabilistic measures. Behavioural implications of favouring the either view are also briefly described.

UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Giesy, Daniel P.

2012-01-01

UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering.

Towards a multilevel cognitive probabilistic representation of space

NASA Astrophysics Data System (ADS)

Tapus, Adriana; Vasudevan, Shrihari; Siegwart, Roland

2005-03-01

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with "Object Graph Models" (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

Random mechanics: Nonlinear vibrations, turbulences, seisms, swells, fatigue

NASA Astrophysics Data System (ADS)

Kree, P.; Soize, C.

The random modeling of physical phenomena, together with probabilistic methods for the numerical calculation of random mechanical forces, are analytically explored. Attention is given to theoretical examinations such as probabilistic concepts, linear filtering techniques, and trajectory statistics. Applications of the methods to structures experiencing atmospheric turbulence, the quantification of turbulence, and the dynamic responses of the structures are considered. A probabilistic approach is taken to study the effects of earthquakes on structures and to the forces exerted by ocean waves on marine structures. Theoretical analyses by means of vector spaces and stochastic modeling are reviewed, as are Markovian formulations of Gaussian processes and the definition of stochastic differential equations. Finally, random vibrations with a variable number of links and linear oscillators undergoing the square of Gaussian processes are investigated.

Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis

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

Ferson, Scott; Nelsen, Roger B.; Hajagos, Janos

2015-05-01

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.

GENERAL: A modified weighted probabilistic cellular automaton traffic flow model

NASA Astrophysics Data System (ADS)

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

2009-08-01

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

Identification of failure type in corroded pipelines: a bayesian probabilistic approach.

PubMed

Breton, T; Sanchez-Gheno, J C; Alamilla, J L; Alvarez-Ramirez, J

2010-07-15

Spillover of hazardous materials from transport pipelines can lead to catastrophic events with serious and dangerous environmental impact, potential fire events and human fatalities. The problem is more serious for large pipelines when the construction material is under environmental corrosion conditions, as in the petroleum and gas industries. In this way, predictive models can provide a suitable framework for risk evaluation, maintenance policies and substitution procedure design that should be oriented to reduce increased hazards. This work proposes a bayesian probabilistic approach to identify and predict the type of failure (leakage or rupture) for steel pipelines under realistic corroding conditions. In the first step of the modeling process, the mechanical performance of the pipe is considered for establishing conditions under which either leakage or rupture failure can occur. In the second step, experimental burst tests are used to introduce a mean probabilistic boundary defining a region where the type of failure is uncertain. In the boundary vicinity, the failure discrimination is carried out with a probabilistic model where the events are considered as random variables. In turn, the model parameters are estimated with available experimental data and contrasted with a real catastrophic event, showing good discrimination capacity. The results are discussed in terms of policies oriented to inspection and maintenance of large-size pipelines in the oil and gas industry. 2010 Elsevier B.V. All rights reserved.

A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data

PubMed Central

Calabrese, Evan; Badea, Alexandra; Cofer, Gary; Qi, Yi; Johnson, G. Allan

2015-01-01

Interest in structural brain connectivity has grown with the understanding that abnormal neural connections may play a role in neurologic and psychiatric diseases. Small animal connectivity mapping techniques are particularly important for identifying aberrant connectivity in disease models. Diffusion magnetic resonance imaging tractography can provide nondestructive, 3D, brain-wide connectivity maps, but has historically been limited by low spatial resolution, low signal-to-noise ratio, and the difficulty in estimating multiple fiber orientations within a single image voxel. Small animal diffusion tractography can be substantially improved through the combination of ex vivo MRI with exogenous contrast agents, advanced diffusion acquisition and reconstruction techniques, and probabilistic fiber tracking. Here, we present a comprehensive, probabilistic tractography connectome of the mouse brain at microscopic resolution, and a comparison of these data with a neuronal tracer-based connectivity data from the Allen Brain Atlas. This work serves as a reference database for future tractography studies in the mouse brain, and demonstrates the fundamental differences between tractography and neuronal tracer data. PMID:26048951

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.

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

NASA Astrophysics Data System (ADS)

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

2013-09-01

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

Probabilistic grammatical model for helix‐helix contact site classification

PubMed Central

2013-01-01

Background Hidden Markov Models power many state‐of‐the‐art tools in the field of protein bioinformatics. While excelling in their tasks, these methods of protein analysis do not convey directly information on medium‐ and long‐range residue‐residue interactions. This requires an expressive power of at least context‐free grammars. However, application of more powerful grammar formalisms to protein analysis has been surprisingly limited. Results In this work, we present a probabilistic grammatical framework for problem‐specific protein languages and apply it to classification of transmembrane helix‐helix pairs configurations. The core of the model consists of a probabilistic context‐free grammar, automatically inferred by a genetic algorithm from only a generic set of expert‐based rules and positive training samples. The model was applied to produce sequence based descriptors of four classes of transmembrane helix‐helix contact site configurations. The highest performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability of representing structural features of helix‐helix contact sites. Conclusions We demonstrated that our probabilistic context‐free framework for analysis of protein sequences outperforms the state of the art in the task of helix‐helix contact site classification. However, this is achieved without necessarily requiring modeling long range dependencies between interacting residues. A significant feature of our approach is that grammar rules and parse trees are human‐readable. Thus they could provide biologically meaningful information for molecular biologists. PMID:24350601

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Finding models to detect Alzheimer's disease by fusing structural and neuropsychological information

NASA Astrophysics Data System (ADS)

Giraldo, Diana L.; García-Arteaga, Juan D.; Velasco, Nelson; Romero, Eduardo

2015-12-01

Alzheimer's disease (AD) is a neurodegenerative disease that affects higher brain functions. Initial diagnosis of AD is based on the patient's clinical history and a battery of neuropsychological tests. The accuracy of the diagnosis is highly dependent on the examiner's skills and on the evolution of a variable clinical frame. This work presents an automatic strategy that learns probabilistic brain models for different stages of the disease, reducing the complexity, parameter adjustment and computational costs. The proposed method starts by setting a probabilistic class description using the information stored in the neuropsychological test, followed by constructing the different structural class models using membership values from the learned probabilistic functions. These models are then used as a reference frame for the classification problem: a new case is assigned to a particular class simply by projecting to the different models. The validation was performed using a leave-one-out cross-validation, two classes were used: Normal Control (NC) subjects and patients diagnosed with mild AD. In this experiment it is possible to achieve a sensibility and specificity of 80% and 79% respectively.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Treesearch

Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...

Recovering a Probabilistic Knowledge Structure by Constraining Its Parameter Space

ERIC Educational Resources Information Center

Stefanutti, Luca; Robusto, Egidio

2009-01-01

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne ("Knowledge Spaces," Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observable response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless…

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.

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.

Discounting of Monetary Rewards that are Both Delayed and Probabilistic: Delay and Probability Combine Multiplicatively, not Additively

PubMed Central

Vanderveldt, Ariana; Green, Leonard; Myerson, Joel

2014-01-01

The value of an outcome is affected both by the delay until its receipt (delay discounting) and by the likelihood of its receipt (probability discounting). Despite being well-described by the same hyperboloid function, delay and probability discounting involve fundamentally different processes, as revealed, for example, by the differential effects of reward amount. Previous research has focused on the discounting of delayed and probabilistic rewards separately, with little research examining more complex situations in which rewards are both delayed and probabilistic. In two experiments, participants made choices between smaller rewards that were both immediate and certain and larger rewards that were both delayed and probabilistic. Analyses revealed significant interactions between delay and probability factors inconsistent with an additive model. In contrast, a hyperboloid discounting model in which delay and probability were combined multiplicatively provided an excellent fit to the data. These results suggest that the hyperboloid is a good descriptor of decision making in complicated monetary choice situations like those people encounter in everyday life. PMID:24933696

Reasoning about Probabilistic Security Using Task-PIOAs

NASA Astrophysics Data System (ADS)

Jaggard, Aaron D.; Meadows, Catherine; Mislove, Michael; Segala, Roberto

Task-structured probabilistic input/output automata (Task-PIOAs) are concurrent probabilistic automata that, among other things, have been used to provide a formal framework for the universal composability paradigms of protocol security. One of their advantages is that that they allow one to distinguish high-level nondeterminism that can affect the outcome of the protocol, from low-level choices, which can't. We present an alternative approach to analyzing the structure of Task-PIOAs that relies on ordered sets. We focus on two of the components that are required to define and apply Task-PIOAs: discrete probability theory and automata theory. We believe our development gives insight into the structure of Task-PIOAs and how they can be utilized to model crypto-protocols. We illustrate our approach with an example from anonymity, an area that has not previously been addressed using Task-PIOAs. We model Chaum's Dining Cryptographers Protocol at a level that does not require cryptographic primitives in the analysis. We show via this example how our approach can leverage a proof of security in the case a principal behaves deterministically to prove security when that principal behaves probabilistically.

Evaluation of Sex-Specific Movement Patterns in Judo Using Probabilistic Neural Networks.

PubMed

Miarka, Bianca; Sterkowicz-Przybycien, Katarzyna; Fukuda, David H

2017-10-01

The purpose of the present study was to create a probabilistic neural network to clarify the understanding of movement patterns in international judo competitions by gender. Analysis of 773 male and 638 female bouts was utilized to identify movements during the approach, gripping, attack (including biomechanical designations), groundwork, defense, and pause phases. Probabilistic neural network and chi-square (χ 2 ) tests modeled and compared frequencies (p ≤ .05). Women (mean [interquartile range]: 9.9 [4; 14]) attacked more than men (7.0 [3; 10]) while attempting a greater number of arm/leg lever (women: 2.7 [1; 6]; men: 4.0 [0; 4]) and trunk/leg lever (women: 0.8 [0; 1]; men: 2.4 [0; 4]) techniques but fewer maximal length-moment arm techniques (women: 0.7 [0; 1]; men: 1.0 [0; 2]). Male athletes displayed one-handed gripping of the back and sleeve, whereas female athletes executed a greater number of groundwork techniques. An optimized probabilistic neural network model, using patterns from the gripping, attack, groundwork, and pause phases, produced an overall prediction accuracy of 76% for discrimination between men and women.

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

NASA Astrophysics Data System (ADS)

Smith, L. A.

2012-04-01

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

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating

PubMed Central

Lee, Young-Joo; Cho, Soojin

2016-01-01

Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed. PMID:26950125

A Probabilistic Model of Meter Perception: Simulating Enculturation.

PubMed

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

2017-01-01

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

Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles

NASA Astrophysics Data System (ADS)

Yang, Xiu-Qun; Yang, Dejian; Xie, Qian; Zhang, Yaocun; Ren, Xuejuan; Tang, Youmin

2017-04-01

Based on historical forecasts of three quasi-operational multi-model ensemble (MME) systems, this study assesses the superiority of coupled MME over contributing single-model ensembles (SMEs) and over uncoupled atmospheric MME in predicting the Western North Pacific-East Asian summer monsoon variability. The probabilistic and deterministic forecast skills are measured by Brier skill score (BSS) and anomaly correlation (AC), respectively. A forecast-format dependent MME superiority over SMEs is found. The probabilistic forecast skill of the MME is always significantly better than that of each SME, while the deterministic forecast skill of the MME can be lower than that of some SMEs. The MME superiority arises from both the model diversity and the ensemble size increase in the tropics, and primarily from the ensemble size increase in the subtropics. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the dramatic improvement in reliability, while resolution is not always improved, similar to AC. A monotonic resolution-AC relationship is further found and qualitatively explained, whereas little relationship can be identified between reliability and AC. It is argued that the MME's success in improving the reliability arises from an effective reduction of the overconfidence in forecast distributions. Moreover, it is examined that the seasonal predictions with coupled MME are more skillful than those with the uncoupled atmospheric MME forced by persisting sea surface temperature (SST) anomalies, since the coupled MME has better predicted the SST anomaly evolution in three key regions.

The Use of Mouse Models to Study Epigenetics

PubMed Central

Blewitt, Marnie; Whitelaw, Emma

2013-01-01

Much of what we know about the role of epigenetics in the determination of phenotype has come from studies of inbred mice. Some unusual expression patterns arising from endogenous and transgenic murine alleles, such as the Agouti coat color alleles, have allowed the study of variegation, variable expressivity, transgenerational epigenetic inheritance, parent-of-origin effects, and position effects. These phenomena have taught us much about gene silencing and the probabilistic nature of epigenetic processes. Based on some of these alleles, large-scale mutagenesis screens have broadened our knowledge of epigenetic control by identifying and characterizing novel genes involved in these processes. PMID:24186070

Probabilistic Design Analysis (PDA) Approach to Determine the Probability of Cross-System Failures for a Space Launch Vehicle

NASA Technical Reports Server (NTRS)

Shih, Ann T.; Lo, Yunnhon; Ward, Natalie C.

2010-01-01

Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I project

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.

A probabilistic approach to quantifying hydrologic thresholds regulating migration of adult Atlantic salmon into spawning streams

NASA Astrophysics Data System (ADS)

Lazzaro, G.; Soulsby, C.; Tetzlaff, D.; Botter, G.

2017-03-01

Atlantic salmon is an economically and ecologically important fish species, whose survival is dependent on successful spawning in headwater rivers. Streamflow dynamics often have a strong control on spawning because fish require sufficiently high discharges to move upriver and enter spawning streams. However, these streamflow effects are modulated by biological factors such as the number and the timing of returning fish in relation to the annual spawning window in the fall/winter. In this paper, we develop and apply a novel probabilistic approach to quantify these interactions using a parsimonious outflux-influx model linking the number of female salmon emigrating (i.e., outflux) and returning (i.e., influx) to a spawning stream in Scotland. The model explicitly accounts for the interannual variability of the hydrologic regime and the hydrological connectivity of spawning streams to main rivers. Model results are evaluated against a detailed long-term (40 years) hydroecological data set that includes annual fluxes of salmon, allowing us to explicitly assess the role of discharge variability. The satisfactory model results show quantitatively that hydrologic variability contributes to the observed dynamics of salmon returns, with a good correlation between the positive (negative) peaks in the immigration data set and the exceedance (nonexceedance) probability of a threshold flow (0.3 m3/s). Importantly, model performance deteriorates when the interannual variability of flow regime is disregarded. The analysis suggests that flow thresholds and hydrological connectivity for spawning return represent a quantifiable and predictable feature of salmon rivers, which may be helpful in decision making where flow regimes are altered by water abstractions.

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.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Combinatorial Interdependence in Lottery

ERIC Educational Resources Information Center

Helman, Danny

2005-01-01

This paper examines a real life question of gamble facing lottery players. Combinatorial dependence plays a central role in shaping the game probabilistic structure, but might not carry the merited weight in punters' considerations.

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.

Specifying design conservatism: Worst case versus probabilistic analysis

NASA Technical Reports Server (NTRS)

Miles, Ralph F., Jr.

1993-01-01

Design conservatism is the difference between specified and required performance, and is introduced when uncertainty is present. The classical approach of worst-case analysis for specifying design conservatism is presented, along with the modern approach of probabilistic analysis. The appropriate degree of design conservatism is a tradeoff between the required resources and the probability and consequences of a failure. A probabilistic analysis properly models this tradeoff, while a worst-case analysis reveals nothing about the probability of failure, and can significantly overstate the consequences of failure. Two aerospace examples will be presented that illustrate problems that can arise with a worst-case analysis.

Probabilistic safety assessment of the design of a tall buildings under the extreme load

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

Králik, Juraj, E-mail: juraj.kralik@stuba.sk

2016-06-08

The paper describes some experiences from the deterministic and probabilistic analysis of the safety of the tall building structure. There are presented the methods and requirements of Eurocode EN 1990, standard ISO 2394 and JCSS. The uncertainties of the model and resistance of the structures are considered using the simulation methods. The MONTE CARLO, LHS and RSM probabilistic methods are compared with the deterministic results. On the example of the probability analysis of the safety of the tall buildings is demonstrated the effectiveness of the probability design of structures using Finite Element Methods.

Probabilistic safety assessment of the design of a tall buildings under the extreme load

NASA Astrophysics Data System (ADS)

Králik, Juraj

2016-06-01

The paper describes some experiences from the deterministic and probabilistic analysis of the safety of the tall building structure. There are presented the methods and requirements of Eurocode EN 1990, standard ISO 2394 and JCSS. The uncertainties of the model and resistance of the structures are considered using the simulation methods. The MONTE CARLO, LHS and RSM probabilistic methods are compared with the deterministic results. On the example of the probability analysis of the safety of the tall buildings is demonstrated the effectiveness of the probability design of structures using Finite Element Methods.

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.

A Physically-Based and Distributed Tool for Modeling the Hydrological and Mechanical Processes of Shallow Landslides

NASA Astrophysics Data System (ADS)

Arnone, E.; Noto, L. V.; Dialynas, Y. G.; Caracciolo, D.; Bras, R. L.

2015-12-01

This work presents the capabilities of a model, i.e. the tRIBS-VEGGIE-Landslide, in two different versions, i.e. developed within a probabilistic framework and coupled with a root cohesion module. The probabilistic model treats geotechnical and soil retention curve parameters as random variables across the basin and estimates theoretical probability distributions of slope stability and the associated "factor of safety" commonly used to describe the occurrence of shallow landslides. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, conditioned on soil moisture dynamics at each watershed location. The framework has been tested in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure. Another version of the model accounts for the additional cohesion exerted by vegetation roots. The approach is to use the Fiber Bundle Model (FBM) framework that allows for the evaluation of the root strength as a function of the stress-strain relationships of bundles of fibers. The model requires the knowledge of the root architecture to evaluate the additional reinforcement from each root diameter class. The root architecture is represented with a branching topology model based on Leonardo's rule. The methodology has been tested on a simple case study to explore the role of both hydrological and mechanical root effects. Results demonstrate that the effects of root water uptake can at times be more significant than the mechanical reinforcement; and that the additional resistance provided by roots depends heavily on the vegetation root structure and length.

Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model.

PubMed

Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali

2015-01-21

Enzyme catalysis is one of the most essential and striking processes among of all the complex processes that have evolved in living organisms. Enzymes are biological catalysts, which play a significant role in industrial applications as well as in medical areas, due to profound specificity, selectivity and catalytic efficiency. Refining catalytic efficiency of enzymes has become the most challenging job of enzyme engineering, into acidic and alkaline. Discrimination of acidic and alkaline enzymes through experimental approaches is difficult, sometimes impossible due to lack of established structures. Therefore, it is highly desirable to develop a computational model for discriminating acidic and alkaline enzymes from primary sequences. In this study, we have developed a robust, accurate and high throughput computational model using two discrete sample representation methods Pseudo amino acid composition (PseAAC) and split amino acid composition. Various classification algorithms including probabilistic neural network (PNN), K-nearest neighbor, decision tree, multi-layer perceptron and support vector machine are applied to predict acidic and alkaline with high accuracy. 10-fold cross validation test and several statistical measures namely, accuracy, F-measure, and area under ROC are used to evaluate the performance of the proposed model. The performance of the model is examined using two benchmark datasets to demonstrate the effectiveness of the model. The empirical results show that the performance of PNN in conjunction with PseAAC is quite promising compared to existing approaches in the literature so for. It has achieved 96.3% accuracy on dataset1 and 99.2% on dataset2. It is ascertained that the proposed model might be useful for basic research and drug related application areas. Copyright © 2014 Elsevier Ltd. All rights reserved.

Advancing the citizen scientist's contributions to documenting and understanding natural hazards: a proof of concept for linking crowdsourced and remotely sensed data on landslide hazards in El Salvador

NASA Astrophysics Data System (ADS)

Anderson, E. R.; Griffin, R.; Markert, K. N.

2017-12-01

Scientists, practitioners, policymakers, and citizen groups, share a role in ensuring "that all sectors have access to, understand and can use scientific information for better informed decision-making" (Sendai Framework 2015-2030). When it comes to understanding hazards and exposure, inventories on disaster events are often limited. Thus, there are many opportunities for citizen scientists to engage in improving the collective understanding—and ultimately reduction—of disaster risk. Landslides are very difficult to forecast on spatial and temporal scales meaningful for early warning and evacuation. Heuristic hazard mapping methods are very common in regional hazard zonation and rely on expert knowledge of previous events and local conditions, but they often lack a temporal component. As new data analysis packages are becoming more open and accessible, probabilistic approaches that consider high resolution spatial and temporal dimensions are becoming more common, but this is only possible when rich inventories of landslide events exist. The work presented offers a proof of concept on incorporating crowd-sourced data to improve landslide hazard model performance. Starting with a national inventory of 90 catalogued landslides in El Salvador for a study period of 1998 to 2011, we simulate the addition of over 600 additional crowd-sourced landslide events that would have been identified through human interpretation of high resolution imagery in the Google Earth time slider feature. There is a noticeable improvement in performance statistics between static heuristic hazard models and probabilistic models that incorporate the events identified by the "crowd." Such a dynamic incorporation of crowd-sourced data on hazard events is not so far-fetched. Given the engagement of "local observers" in El Salvador who augment in situ hydro-meteorological measurements, the growing access to Earth observation data to the lay person, and immense interest behind connecting citizen scientists to remote sensing data through hackathons such as the NASA Space Apps Challenges, we envision a much more dynamic, collective understanding of landslide hazards. Here we present a better scenario of what we could have known had data from the crowd been incorporated into probabilistic hazard models on a regular basis.

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

Control of Stochastic Master Equation Models of Genetic Regulatory Networks by Approximating Their Average Behavior

NASA Astrophysics Data System (ADS)

Umut Caglar, Mehmet; Pal, Ranadip

2010-10-01

The central dogma of molecular biology states that ``information cannot be transferred back from protein to either protein or nucleic acid.'' However, this assumption is not exactly correct in most of the cases. There are a lot of feedback loops and interactions between different levels of systems. These types of interactions are hard to analyze due to the lack of data in the cellular level and probabilistic nature of interactions. Probabilistic models like Stochastic Master Equation (SME) or deterministic models like differential equations (DE) can be used to analyze these types of interactions. SME models based on chemical master equation (CME) can provide detailed representation of genetic regulatory system, but their use is restricted by the large data requirements and computational costs of calculations. The differential equations models on the other hand, have low calculation costs and much more adequate to generate control procedures on the system; but they are not adequate to investigate the probabilistic nature of interactions. In this work the success of the mapping between SME and DE is analyzed, and the success of a control policy generated by DE model with respect to SME model is examined. Index Terms--- Stochastic Master Equation models, Differential Equation Models, Control Policy Design, Systems biology

Computational Everyday Life Human Behavior Model as Servicable Knowledge

NASA Astrophysics Data System (ADS)

Motomura, Yoichi; Nishida, Yoshifumi

A project called `Open life matrix' is not only a research activity but also real problem solving as an action research. This concept is realized by large-scale data collection, probabilistic causal structure model construction and information service providing using the model. One concrete outcome of this project is childhood injury prevention activity in new team consist of hospital, government, and many varieties of researchers. The main result from the project is a general methodology to apply probabilistic causal structure models as servicable knowledge for action research. In this paper, the summary of this project and future direction to emphasize action research driven by artificial intelligence technology are discussed.

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].

Surrogate modeling of joint flood risk across coastal watersheds

NASA Astrophysics Data System (ADS)

Bass, Benjamin; Bedient, Philip

2018-03-01

This study discusses the development and performance of a rapid prediction system capable of representing the joint rainfall-runoff and storm surge flood response of tropical cyclones (TCs) for probabilistic risk analysis. Due to the computational demand required for accurately representing storm surge with the high-fidelity ADvanced CIRCulation (ADCIRC) hydrodynamic model and its coupling with additional numerical models to represent rainfall-runoff, a surrogate or statistical model was trained to represent the relationship between hurricane wind- and pressure-field characteristics and their peak joint flood response typically determined from physics based numerical models. This builds upon past studies that have only evaluated surrogate models for predicting peak surge, and provides the first system capable of probabilistically representing joint flood levels from TCs. The utility of this joint flood prediction system is then demonstrated by improving upon probabilistic TC flood risk products, which currently account for storm surge but do not take into account TC associated rainfall-runoff. Results demonstrate the source apportionment of rainfall-runoff versus storm surge and highlight that slight increases in flood risk levels may occur due to the interaction between rainfall-runoff and storm surge as compared to the Federal Emergency Management Association's (FEMAs) current practices.

Probing the Small-scale Structure in Strongly Lensed Systems via Transdimensional Inference

NASA Astrophysics Data System (ADS)

Daylan, Tansu; Cyr-Racine, Francis-Yan; Diaz Rivero, Ana; Dvorkin, Cora; Finkbeiner, Douglas P.

2018-02-01

Strong lensing is a sensitive probe of the small-scale density fluctuations in the Universe. We implement a pipeline to model strongly lensed systems using probabilistic cataloging, which is a transdimensional, hierarchical, and Bayesian framework to sample from a metamodel (union of models with different dimensionality) consistent with observed photon count maps. Probabilistic cataloging allows one to robustly characterize modeling covariances within and across lens models with different numbers of subhalos. Unlike traditional cataloging of subhalos, it does not require model subhalos to improve the goodness of fit above the detection threshold. Instead, it allows the exploitation of all information contained in the photon count maps—for instance, when constraining the subhalo mass function. We further show that, by not including these small subhalos in the lens model, fixed-dimensional inference methods can significantly mismodel the data. Using a simulated Hubble Space Telescope data set, we show that the subhalo mass function can be probed even when many subhalos in the sample catalogs are individually below the detection threshold and would be absent in a traditional catalog. The implemented software, Probabilistic Cataloger (PCAT) is made publicly available at https://github.com/tdaylan/pcat.

Predictability of short-range forecasting: a multimodel approach

NASA Astrophysics Data System (ADS)

García-Moya, Jose-Antonio; Callado, Alfons; Escribà, Pau; Santos, Carlos; Santos-Muñoz, Daniel; Simarro, Juan

2011-05-01

Numerical weather prediction (NWP) models (including mesoscale) have limitations when it comes to dealing with severe weather events because extreme weather is highly unpredictable, even in the short range. A probabilistic forecast based on an ensemble of slightly different model runs may help to address this issue. Among other ensemble techniques, Multimodel ensemble prediction systems (EPSs) are proving to be useful for adding probabilistic value to mesoscale deterministic models. A Multimodel Short Range Ensemble Prediction System (SREPS) focused on forecasting the weather up to 72 h has been developed at the Spanish Meteorological Service (AEMET). The system uses five different limited area models (LAMs), namely HIRLAM (HIRLAM Consortium), HRM (DWD), the UM (UKMO), MM5 (PSU/NCAR) and COSMO (COSMO Consortium). These models run with initial and boundary conditions provided by five different global deterministic models, namely IFS (ECMWF), UM (UKMO), GME (DWD), GFS (NCEP) and CMC (MSC). AEMET-SREPS (AE) validation on the large-scale flow, using ECMWF analysis, shows a consistent and slightly underdispersive system. For surface parameters, the system shows high skill forecasting binary events. 24-h precipitation probabilistic forecasts are verified using an up-scaling grid of observations from European high-resolution precipitation networks, and compared with ECMWF-EPS (EC).

Groundwater Remediation using Bayesian Information-Gap Decision Theory

NASA Astrophysics Data System (ADS)

O'Malley, D.; Vesselinov, V. V.

2016-12-01

Probabilistic analyses of groundwater remediation scenarios frequently fail because the probability of an adverse, unanticipated event occurring is often high. In general, models of flow and transport in contaminated aquifers are always simpler than reality. Further, when a probabilistic analysis is performed, probability distributions are usually chosen more for convenience than correctness. The Bayesian Information-Gap Decision Theory (BIGDT) was designed to mitigate the shortcomings of the models and probabilistic decision analyses by leveraging a non-probabilistic decision theory - information-gap decision theory. BIGDT considers possible models that have not been explicitly enumerated and does not require us to commit to a particular probability distribution for model and remediation-design parameters. Both the set of possible models and the set of possible probability distributions grow as the degree of uncertainty increases. The fundamental question that BIGDT asks is "How large can these sets be before a particular decision results in an undesirable outcome?". The decision that allows these sets to be the largest is considered to be the best option. In this way, BIGDT enables robust decision-support for groundwater remediation problems. Here we apply BIGDT to in a representative groundwater remediation scenario where different options for hydraulic containment and pump & treat are being considered. BIGDT requires many model runs and for complex models high-performance computing resources are needed. These analyses are carried out on synthetic problems, but are applicable to real-world problems such as LANL site contaminations. BIGDT is implemented in Julia (a high-level, high-performance dynamic programming language for technical computing) and is part of the MADS framework (http://mads.lanl.gov/ and https://github.com/madsjulia/Mads.jl).

Probabilistically modeling lava flows with MOLASSES

NASA Astrophysics Data System (ADS)

Richardson, J. A.; Connor, L.; Connor, C.; Gallant, E.

2017-12-01

Modeling lava flows through Cellular Automata methods enables a computationally inexpensive means to quickly forecast lava flow paths and ultimate areal extents. We have developed a lava flow simulator, MOLASSES, that forecasts lava flow inundation over an elevation model from a point source eruption. This modular code can be implemented in a deterministic fashion with given user inputs that will produce a single lava flow simulation. MOLASSES can also be implemented in a probabilistic fashion where given user inputs define parameter distributions that are randomly sampled to create many lava flow simulations. This probabilistic approach enables uncertainty in input data to be expressed in the model results and MOLASSES outputs a probability map of inundation instead of a determined lava flow extent. Since the code is comparatively fast, we use it probabilistically to investigate where potential vents are located that may impact specific sites and areas, as well as the unconditional probability of lava flow inundation of sites or areas from any vent. We have validated the MOLASSES code to community-defined benchmark tests and to the real world lava flows at Tolbachik (2012-2013) and Pico do Fogo (2014-2015). To determine the efficacy of the MOLASSES simulator at accurately and precisely mimicking the inundation area of real flows, we report goodness of fit using both model sensitivity and the Positive Predictive Value, the latter of which is a Bayesian posterior statistic. Model sensitivity is often used in evaluating lava flow simulators, as it describes how much of the lava flow was successfully modeled by the simulation. We argue that the positive predictive value is equally important in determining how good a simulator is, as it describes the percentage of the simulation space that was actually inundated by lava.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

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

Probabilistic structural analysis of space propulsion system LOX post

NASA Technical Reports Server (NTRS)

Newell, J. F.; Rajagopal, K. R.; Ho, H. W.; Cunniff, J. M.

1990-01-01

The probabilistic structural analysis program NESSUS (Numerical Evaluation of Stochastic Structures Under Stress; Cruse et al., 1988) is applied to characterize the dynamic loading and response of the Space Shuttle main engine (SSME) LOX post. The design and operation of the SSME are reviewed; the LOX post structure is described; and particular attention is given to the generation of composite load spectra, the finite-element model of the LOX post, and the steps in the NESSUS structural analysis. The results are presented in extensive tables and graphs, and it is shown that NESSUS correctly predicts the structural effects of changes in the temperature loading. The probabilistic approach also facilitates (1) damage assessments for a given failure model (based on gas temperature, heat-shield gap, and material properties) and (2) correlation of the gas temperature with operational parameters such as engine thrust.

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.

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

Dopamine D3 Receptor Availability Is Associated with Inflexible Decision Making.

PubMed

Groman, Stephanie M; Smith, Nathaniel J; Petrullli, J Ryan; Massi, Bart; Chen, Lihui; Ropchan, Jim; Huang, Yiyun; Lee, Daeyeol; Morris, Evan D; Taylor, Jane R

2016-06-22

Dopamine D2/3 receptor signaling is critical for flexible adaptive behavior; however, it is unclear whether D2, D3, or both receptor subtypes modulate precise signals of feedback and reward history that underlie optimal decision making. Here, PET with the radioligand [(11)C]-(+)-PHNO was used to quantify individual differences in putative D3 receptor availability in rodents trained on a novel three-choice spatial acquisition and reversal-learning task with probabilistic reinforcement. Binding of [(11)C]-(+)-PHNO in the midbrain was negatively related to the ability of rats to adapt to changes in rewarded locations, but not to the initial learning. Computational modeling of choice behavior in the reversal phase indicated that [(11)C]-(+)-PHNO binding in the midbrain was related to the learning rate and sensitivity to positive, but not negative, feedback. Administration of a D3-preferring agonist likewise impaired reversal performance by reducing the learning rate and sensitivity to positive feedback. These results demonstrate a previously unrecognized role for D3 receptors in select aspects of reinforcement learning and suggest that individual variation in midbrain D3 receptors influences flexible behavior. Our combined neuroimaging, behavioral, pharmacological, and computational approach implicates the dopamine D3 receptor in decision-making processes that are altered in psychiatric disorders. Flexible decision-making behavior is dependent upon dopamine D2/3 signaling in corticostriatal brain regions. However, the role of D3 receptors in adaptive, goal-directed behavior has not been thoroughly investigated. By combining PET imaging with the D3-preferring radioligand [(11)C]-(+)-PHNO, pharmacology, a novel three-choice probabilistic discrimination and reversal task and computational modeling of behavior in rats, we report that naturally occurring variation in [(11)C]-(+)-PHNO receptor availability relates to specific aspects of flexible decision making. We confirm these relationships using a D3-preferring agonist, thus identifying a unique role of midbrain D3 receptors in decision-making processes. Copyright © 2016 the authors 0270-6474/16/366732-10$15.00/0.

Stochastic model for fatigue crack size and cost effective design decisions. [for aerospace structures

NASA Technical Reports Server (NTRS)

Hanagud, S.; Uppaluri, B.

1975-01-01

This paper describes a methodology for making cost effective fatigue design decisions. The methodology is based on a probabilistic model for the stochastic process of fatigue crack growth with time. The development of a particular model for the stochastic process is also discussed in the paper. The model is based on the assumption of continuous time and discrete space of crack lengths. Statistical decision theory and the developed probabilistic model are used to develop the procedure for making fatigue design decisions on the basis of minimum expected cost or risk function and reliability bounds. Selections of initial flaw size distribution, NDT, repair threshold crack lengths, and inspection intervals are discussed.

A simulation model for probabilistic analysis of Space Shuttle abort modes

NASA Technical Reports Server (NTRS)

Hage, R. T.

1993-01-01

A simulation model which was developed to provide a probabilistic analysis tool to study the various space transportation system abort mode situations is presented. The simulation model is based on Monte Carlo simulation of an event-tree diagram which accounts for events during the space transportation system's ascent and its abort modes. The simulation model considers just the propulsion elements of the shuttle system (i.e., external tank, main engines, and solid boosters). The model was developed to provide a better understanding of the probability of occurrence and successful completion of abort modes during the vehicle's ascent. The results of the simulation runs discussed are for demonstration purposes only, they are not official NASA probability estimates.

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

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.

Spatial planning using probabilistic flood maps

NASA Astrophysics Data System (ADS)

Alfonso, Leonardo; Mukolwe, Micah; Di Baldassarre, Giuliano

2015-04-01

Probabilistic flood maps account for uncertainty in flood inundation modelling and convey a degree of certainty in the outputs. Major sources of uncertainty include input data, topographic data, model structure, observation data and parametric uncertainty. Decision makers prefer less ambiguous information from modellers; this implies that uncertainty is suppressed to yield binary flood maps. Though, suppressing information may potentially lead to either surprise or misleading decisions. Inclusion of uncertain information in the decision making process is therefore desirable and transparent. To this end, we utilise the Prospect theory and information from a probabilistic flood map to evaluate potential decisions. Consequences related to the decisions were evaluated using flood risk analysis. Prospect theory explains how choices are made given options for which probabilities of occurrence are known and accounts for decision makers' characteristics such as loss aversion and risk seeking. Our results show that decision making is pronounced when there are high gains and loss, implying higher payoffs and penalties, therefore a higher gamble. Thus the methodology may be appropriately considered when making decisions based on uncertain information.

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

PubMed

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

2017-12-15

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

On the probabilistic structure of water age: Probabilistic Water Age

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

Porporato, Amilcare; Calabrese, Salvatore

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

On the probabilistic structure of water age: Probabilistic Water Age

DOE PAGES

Porporato, Amilcare; Calabrese, Salvatore

2015-04-23

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

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

PubMed Central

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

2015-01-01

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

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

New ShakeMaps for Georgia Resulting from Collaboration with EMME

NASA Astrophysics Data System (ADS)

Kvavadze, N.; Tsereteli, N. S.; Varazanashvili, O.; Alania, V.

2015-12-01

Correct assessment of probabilistic seismic hazard and risks maps are first step for advance planning and action to reduce seismic risk. Seismic hazard maps for Georgia were calculated based on modern approach that was developed in the frame of EMME (Earthquake Modl for Middle east region) project. EMME was one of GEM's successful endeavors at regional level. With EMME and GEM assistance, regional models were analyzed to identify the information and additional work needed for the preparation national hazard models. Probabilistic seismic hazard map (PSH) provides the critical bases for improved building code and construction. The most serious deficiency in PSH assessment for the territory of Georgia is the lack of high-quality ground motion data. Due to this an initial hybrid empirical ground motion model is developed for PGA and SA at selected periods. An application of these coefficients for ground motion models have been used in probabilistic seismic hazard assessment. Obtained results of seismic hazard maps show evidence that there were gaps in seismic hazard assessment and the present normative seismic hazard map needed a careful recalculation.

Verification and Optimal Control of Context-Sensitive Probabilistic Boolean Networks Using Model Checking and Polynomial Optimization

PubMed Central

Hiraishi, Kunihiko

2014-01-01

One of the significant topics in systems biology is to develop control theory of gene regulatory networks (GRNs). In typical control of GRNs, expression of some genes is inhibited (activated) by manipulating external stimuli and expression of other genes. It is expected to apply control theory of GRNs to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of GRNs, and gene expression is expressed by a binary value (ON or OFF). In particular, a context-sensitive probabilistic Boolean network (CS-PBN), which is one of the extended models of BNs, is used. For CS-PBNs, the verification problem and the optimal control problem are considered. For the verification problem, a solution method using the probabilistic model checker PRISM is proposed. For the optimal control problem, a solution method using polynomial optimization is proposed. Finally, a numerical example on the WNT5A network, which is related to melanoma, is presented. The proposed methods provide us useful tools in control theory of GRNs. PMID:24587766

Probabilistic Common Spatial Patterns for Multichannel EEG Analysis

PubMed Central

Chen, Zhe; Gao, Xiaorong; Li, Yuanqing; Brown, Emery N.; Gao, Shangkai

2015-01-01

Common spatial patterns (CSP) is a well-known spatial filtering algorithm for multichannel electroencephalogram (EEG) analysis. In this paper, we cast the CSP algorithm in a probabilistic modeling setting. Specifically, probabilistic CSP (P-CSP) is proposed as a generic EEG spatio-temporal modeling framework that subsumes the CSP and regularized CSP algorithms. The proposed framework enables us to resolve the overfitting issue of CSP in a principled manner. We derive statistical inference algorithms that can alleviate the issue of local optima. In particular, an efficient algorithm based on eigendecomposition is developed for maximum a posteriori (MAP) estimation in the case of isotropic noise. For more general cases, a variational algorithm is developed for group-wise sparse Bayesian learning for the P-CSP model and for automatically determining the model size. The two proposed algorithms are validated on a simulated data set. Their practical efficacy is also demonstrated by successful applications to single-trial classifications of three motor imagery EEG data sets and by the spatio-temporal pattern analysis of one EEG data set recorded in a Stroop color naming task. PMID:26005228

On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy.

PubMed

Amigó, José M; Hirata, Yoshito; Aihara, Kazuyuki

2017-08-01

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

NASA Astrophysics Data System (ADS)

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

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.

Interference in the classical probabilistic model and its representation in complex Hilbert space

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei Yu.

2005-10-01

The notion of a context (complex of physical conditions, that is to say: specification of the measurement setup) is basic in this paper.We show that the main structures of quantum theory (interference of probabilities, Born's rule, complex probabilistic amplitudes, Hilbert state space, representation of observables by operators) are present already in a latent form in the classical Kolmogorov probability model. However, this model should be considered as a calculus of contextual probabilities. In our approach it is forbidden to consider abstract context independent probabilities: “first context and only then probability”. We construct the representation of the general contextual probabilistic dynamics in the complex Hilbert space. Thus dynamics of the wave function (in particular, Schrödinger's dynamics) can be considered as Hilbert space projections of a realistic dynamics in a “prespace”. The basic condition for representing of the prespace-dynamics is the law of statistical conservation of energy-conservation of probabilities. In general the Hilbert space projection of the “prespace” dynamics can be nonlinear and even irreversible (but it is always unitary). Methods developed in this paper can be applied not only to quantum mechanics, but also to classical statistical mechanics. The main quantum-like structures (e.g., interference of probabilities) might be found in some models of classical statistical mechanics. Quantum-like probabilistic behavior can be demonstrated by biological systems. In particular, it was recently found in some psychological experiments.

DAMS: A Model to Assess Domino Effects by Using Agent-Based Modeling and Simulation.

PubMed

Zhang, Laobing; Landucci, Gabriele; Reniers, Genserik; Khakzad, Nima; Zhou, Jianfeng

2017-12-19

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases. © 2017 Society for Risk Analysis.

Probabilistic modelling of human exposure to intense sweeteners in Italian teenagers: validation and sensitivity analysis of a probabilistic model including indicators of market share and brand loyalty.

PubMed

Arcella, D; Soggiu, M E; Leclercq, C

2003-10-01

For the assessment of exposure to food-borne chemicals, the most commonly used methods in the European Union follow a deterministic approach based on conservative assumptions. Over the past few years, to get a more realistic view of exposure to food chemicals, risk managers are getting more interested in the probabilistic approach. Within the EU-funded 'Monte Carlo' project, a stochastic model of exposure to chemical substances from the diet and a computer software program were developed. The aim of this paper was to validate the model with respect to the intake of saccharin from table-top sweeteners and cyclamate from soft drinks by Italian teenagers with the use of the software and to evaluate the impact of the inclusion/exclusion of indicators on market share and brand loyalty through a sensitivity analysis. Data on food consumption and the concentration of sweeteners were collected. A food frequency questionnaire aimed at identifying females who were high consumers of sugar-free soft drinks and/or of table top sweeteners was filled in by 3982 teenagers living in the District of Rome. Moreover, 362 subjects participated in a detailed food survey by recording, at brand level, all foods and beverages ingested over 12 days. Producers were asked to provide the intense sweeteners' concentration of sugar-free products. Results showed that consumer behaviour with respect to brands has an impact on exposure assessments. Only probabilistic models that took into account indicators of market share and brand loyalty met the validation criteria.

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.

Sentence-Based Attentional Mechanisms in Word Learning: Evidence from a Computational Model

PubMed Central

Alishahi, Afra; Fazly, Afsaneh; Koehne, Judith; Crocker, Matthew W.

2012-01-01

When looking for the referents of novel nouns, adults and young children are sensitive to cross-situational statistics (Yu and Smith, 2007; Smith and Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau and Gleitman, 1985; Altmann and Kamide, 1999; Kako and Trueswell, 2000). Koehne and Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context in an adult language learning scenario. Their studies reveal that these learning mechanisms interact in a complex manner: they can be used in a complementary way when context helps reduce referential uncertainty; they influence word learning about equally strongly when cross-situational and contextual evidence are in conflict; and contextual cues block aspects of cross-situational learning when both mechanisms are independently applicable. To address this complex pattern of findings, we present a probabilistic computational model of word learning which extends a previous cross-situational model (Fazly et al., 2010) with an attention mechanism based on sentential cues. Our model uses a framework that seamlessly combines the two sources of evidence in order to study their emerging pattern of interaction during the process of word learning. Simulations of the experiments of (Koehne and Crocker, 2010, 2011) reveal an overall pattern of results that are in line with their findings. Importantly, we demonstrate that our model does not need to explicitly assign priority to either source of evidence in order to produce these results: learning patterns emerge as a result of a probabilistic interaction between the two clue types. Moreover, using a computational model allows us to examine the developmental trajectory of the differential roles of cross-situational and sentential cues in word learning. PMID:22783211

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Probabilistic Tsunami Hazard Assessment: the Seaside, Oregon Pilot Study

NASA Astrophysics Data System (ADS)

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

2004-12-01

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

VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

PubMed Central

Daunizeau, Jean; Adam, Vincent; Rigoux, Lionel

2014-01-01

This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization. PMID:24465198

Application of Probability Methods to Assess Crash Modeling Uncertainty

NASA Technical Reports Server (NTRS)

Lyle, Karen H.; Stockwell, Alan E.; Hardy, Robin C.

2003-01-01

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stress-strain behaviors, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the effects of finite element modeling assumptions on the predicted responses. The vertical drop test of a Fokker F28 fuselage section will be the focus of this paper. The results of a probabilistic analysis using finite element simulations will be compared with experimental data.

Application of Probability Methods to Assess Crash Modeling Uncertainty

NASA Technical Reports Server (NTRS)

Lyle, Karen H.; Stockwell, Alan E.; Hardy, Robin C.

2007-01-01

Full-scale aircraft crash simulations performed with nonlinear, transient dynamic, finite element codes can incorporate structural complexities such as: geometrically accurate models; human occupant models; and advanced material models to include nonlinear stress-strain behaviors, and material failure. Validation of these crash simulations is difficult due to a lack of sufficient information to adequately determine the uncertainty in the experimental data and the appropriateness of modeling assumptions. This paper evaluates probabilistic approaches to quantify the effects of finite element modeling assumptions on the predicted responses. The vertical drop test of a Fokker F28 fuselage section will be the focus of this paper. The results of a probabilistic analysis using finite element simulations will be compared with experimental data.

Spatiotemporal movement planning and rapid adaptation for manual interaction.

PubMed

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

2013-01-01

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

Joint Probabilistic Projection of Female and Male Life Expectancy

PubMed Central

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

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.

2012-04-01

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

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Thio, H. K.; Løvholt, F.; Harbitz, C. B.; Polet, J.; Lorito, S.; Basili, R.; Volpe, M.; Romano, F.; Selva, J.; Piatanesi, A.; Davies, G.; Griffin, J.; Baptista, M. A.; Omira, R.; Babeyko, A. Y.; Power, W. L.; Salgado Gálvez, M.; Behrens, J.; Yalciner, A. C.; Kanoglu, U.; Pekcan, O.; Ross, S.; Parsons, T.; LeVeque, R. J.; Gonzalez, F. I.; Paris, R.; Shäfer, A.; Canals, M.; Fraser, S. A.; Wei, Y.; Weiss, R.; Zaniboni, F.; Papadopoulos, G. A.; Didenkulova, I.; Necmioglu, O.; Suppasri, A.; Lynett, P. J.; Mokhtari, M.; Sørensen, M.; von Hillebrandt-Andrade, C.; Aguirre Ayerbe, I.; Aniel-Quiroga, Í.; Guillas, S.; Macias, J.

2016-12-01

The large tsunami disasters of the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Lorito, S.; Basili, R.; Harbitz, C. B.; Løvholt, F.; Polet, J.; Thio, H. K.

2017-12-01

The tsunamis occurred worldwide in the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but often disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

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.

Predicting Rib Fracture Risk With Whole-Body Finite Element Models: Development and Preliminary Evaluation of a Probabilistic Analytical Framework

PubMed Central

Forman, Jason L.; Kent, Richard W.; Mroz, Krystoffer; Pipkorn, Bengt; Bostrom, Ola; Segui-Gomez, Maria

2012-01-01

This study sought to develop a strain-based probabilistic method to predict rib fracture risk with whole-body finite element (FE) models, and to describe a method to combine the results with collision exposure information to predict injury risk and potential intervention effectiveness in the field. An age-adjusted ultimate strain distribution was used to estimate local rib fracture probabilities within an FE model. These local probabilities were combined to predict injury risk and severity within the whole ribcage. The ultimate strain distribution was developed from a literature dataset of 133 tests. Frontal collision simulations were performed with the THUMS (Total HUman Model for Safety) model with four levels of delta-V and two restraints: a standard 3-point belt and a progressive 3.5–7 kN force-limited, pretensioned (FL+PT) belt. The results of three simulations (29 km/h standard, 48 km/h standard, and 48 km/h FL+PT) were compared to matched cadaver sled tests. The numbers of fractures predicted for the comparison cases were consistent with those observed experimentally. Combining these results with field exposure informantion (ΔV, NASS-CDS 1992–2002) suggests a 8.9% probability of incurring AIS3+ rib fractures for a 60 year-old restrained by a standard belt in a tow-away frontal collision with this restraint, vehicle, and occupant configuration, compared to 4.6% for the FL+PT belt. This is the first study to describe a probabilistic framework to predict rib fracture risk based on strains observed in human-body FE models. Using this analytical framework, future efforts may incorporate additional subject or collision factors for multi-variable probabilistic injury prediction. PMID:23169122

The Global Tsunami Model (GTM)

NASA Astrophysics Data System (ADS)

Løvholt, Finn

2017-04-01

The large tsunami disasters of the last two decades have highlighted the need for a thorough understanding of the risk posed by relatively infrequent but disastrous tsunamis and the importance of a comprehensive and consistent methodology for quantifying the hazard. In the last few years, several methods for probabilistic tsunami hazard analysis have been developed and applied to different parts of the world. In an effort to coordinate and streamline these activities and make progress towards implementing the Sendai Framework of Disaster Risk Reduction (SFDRR) we have initiated a Global Tsunami Model (GTM) working group with the aim of i) enhancing our understanding of tsunami hazard and risk on a global scale and developing standards and guidelines for it, ii) providing a portfolio of validated tools for probabilistic tsunami hazard and risk assessment at a range of scales, and iii) developing a global tsunami hazard reference model. This GTM initiative has grown out of the tsunami component of the Global Assessment of Risk (GAR15), which has resulted in an initial global model of probabilistic tsunami hazard and risk. Started as an informal gathering of scientists interested in advancing tsunami hazard analysis, the GTM is currently in the process of being formalized through letters of interest from participating institutions. The initiative has now been endorsed by the United Nations International Strategy for Disaster Reduction (UNISDR) and the World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR). We will provide an update on the state of the project and the overall technical framework, and discuss the technical issues that are currently being addressed, including earthquake source recurrence models, the use of aleatory variability and epistemic uncertainty, and preliminary results for a probabilistic global hazard assessment, which is an update of the model included in UNISDR GAR15.

How might Model-based Probabilities Extracted from Imperfect Models Guide Rational Decisions: The Case for non-probabilistic odds

NASA Astrophysics Data System (ADS)

Smith, Leonard A.

2010-05-01

This contribution concerns "deep" or "second-order" uncertainty, such as the uncertainty in our probability forecasts themselves. It asks the question: "Is it rational to take (or offer) bets using model-based probabilities as if they were objective probabilities?" If not, what alternative approaches for determining odds, perhaps non-probabilistic odds, might prove useful in practice, given the fact we know our models are imperfect? We consider the case where the aim is to provide sustainable odds: not to produce a profit but merely to rationally expect to break even in the long run. In other words, to run a quantified risk of ruin that is relatively small. Thus the cooperative insurance schemes of coastal villages provide a more appropriate parallel than a casino. A "better" probability forecast would lead to lower premiums charged and less volatile fluctuations in the cash reserves of the village. Note that the Bayesian paradigm does not constrain one to interpret model distributions as subjective probabilities, unless one believes the model to be empirically adequate for the task at hand. In geophysics, this is rarely the case. When a probability forecast is interpreted as the objective probability of an event, the odds on that event can be easily computed as one divided by the probability of the event, and one need not favour taking either side of the wager. (Here we are using "odds-for" not "odds-to", the difference being whether of not the stake is returned; odds of one to one are equivalent to odds of two for one.) The critical question is how to compute sustainable odds based on information from imperfect models. We suggest that this breaks the symmetry between the odds-on an event and the odds-against it. While a probability distribution can always be translated into odds, interpreting the odds on a set of events might result in "implied-probabilities" that sum to more than one. And/or the set of odds may be incomplete, not covering all events. We ask whether or not probabilities based on imperfect models can be expected to yield probabilistic odds which are sustainable. Evidence is provided that suggest this is not the case. Even with very good models (good in an Root-Mean-Square sense), the risk of ruin of probabilistic odds is significantly higher than might be expected. Methods for constructing model-based non-probabilistic odds which are sustainable are discussed. The aim here is to be relevant to real world decision support, and so unrealistic assumptions of equal knowledge, equal compute power, or equal access to information are to be avoided. Finally, the use of non-probabilistic odds as a method for communicating deep uncertainty (uncertainty in a probability forecast itself) is discussed in the context of other methods, such as stating one's subjective probability that the models will prove inadequate in each particular instance (that is, the Probability of a "Big Surprise").

Weighing costs and losses: A decision making game using probabilistic forecasts

NASA Astrophysics Data System (ADS)

Werner, Micha; Ramos, Maria-Helena; Wetterhall, Frederik; Cranston, Michael; van Andel, Schalk-Jan; Pappenberger, Florian; Verkade, Jan

2017-04-01

Probabilistic forecasts are increasingly recognised as an effective and reliable tool to communicate uncertainties. The economic value of probabilistic forecasts has been demonstrated by several authors, showing the benefit to using probabilistic forecasts over deterministic forecasts in several sectors, including flood and drought warning, hydropower, and agriculture. Probabilistic forecasting is also central to the emerging concept of risk-based decision making, and underlies emerging paradigms such as impact-based forecasting. Although the economic value of probabilistic forecasts is easily demonstrated in academic works, its evaluation in practice is more complex. The practical use of probabilistic forecasts requires decision makers to weigh the cost of an appropriate response to a probabilistic warning against the projected loss that would occur if the event forecast becomes reality. In this paper, we present the results of a simple game that aims to explore how decision makers are influenced by the costs required for taking a response and the potential losses they face in case the forecast flood event occurs. Participants play the role of one of three possible different shop owners. Each type of shop has losses of quite different magnitude, should a flood event occur. The shop owners are presented with several forecasts, each with a probability of a flood event occurring, which would inundate their shop and lead to those losses. In response, they have to decide if they want to do nothing, raise temporary defences, or relocate their inventory. Each action comes at a cost; and the different shop owners therefore have quite different cost/loss ratios. The game was played on four occasions. Players were attendees of the ensemble hydro-meteorological forecasting session of the 2016 EGU Assembly, professionals participating at two other conferences related to hydrometeorology, and a group of students. All audiences were familiar with the principles of forecasting and water-related risks, and one of the audiences comprised a group of experts in probabilistic forecasting. Results show that the different shop owners do take the costs of taking action and the potential losses into account in their decisions. Shop owners with a low cost/loss ratio were found to be more inclined to take actions based on the forecasts, though the absolute value of the losses also increased the willingness to take action. Little differentiation was found between the different groups of players.

A Historical Overview and Contemporary Expansion of Psychological Theories of Determinism, Probabilistic Causality, Indeterminate Free Will, and Moral and Legal Responsibility

ERIC Educational Resources Information Center

Wilks, Duffy; Ratheal, Juli D'Ann

2009-01-01

The authors provide a historical overview of the development of contemporary theories of counseling and psychology in relation to determinism, probabilistic causality, indeterminate free will, and moral and legal responsibility. They propose a unique model of behavioral causality that incorporates a theory of indeterminate free will, a concept…

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

A PROBABILISTIC ARSENIC EXPOSURE ASSESSMENT FOR CHILDREN WHO CONTACT CHROMATED COPPER ARSENATE ( CAA )-TREATED PLAYSETS AND DECKS: PART 2 SENSITIVITY AND UNCERTAINTY ANALYSIS

EPA Science Inventory

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two part paper. This Part 2 paper discusses sensitivity and uncertainty analyses conducted to assess the key m...

A unified probabilistic framework for spontaneous facial action modeling and understanding.

PubMed

Tong, Yan; Chen, Jixu; Ji, Qiang

2010-02-01

Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. Because of these challenges, current research in facial expression recognition is limited to posed expressions and often in frontal view. A spontaneous facial expression is characterized by rigid head movements and nonrigid facial muscular movements. More importantly, it is the coherent and consistent spatiotemporal interactions among rigid and nonrigid facial motions that produce a meaningful facial expression. Recognizing this fact, we introduce a unified probabilistic facial action model based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent rigid and nonrigid facial motions, their spatiotemporal dependencies, and their image measurements. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, facial action recognition is accomplished through probabilistic inference by systematically integrating visual measurements with the facial action model. Experiments show that compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing both rigid and nonrigid facial motions, especially for spontaneous facial expressions.

Composite load spectra for select space propulsion structural components

NASA Technical Reports Server (NTRS)

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

1986-01-01

A multiyear program is performed with the objective to develop generic load models with multiple levels of progressive sophistication to simulate the composite (combined) load spectra that are induced in space propulsion system components, representative of Space Shuttle Main Engines (SSME), such as transfer ducts, turbine blades, and liquid oxygen (LOX) posts. Progress of the first year's effort includes completion of a sufficient portion of each task -- probabilistic models, code development, validation, and an initial operational code. This code has from its inception an expert system philosophy that could be added to throughout the program and in the future. The initial operational code is only applicable to turbine blade type loadings. The probabilistic model included in the operational code has fitting routines for loads that utilize a modified Discrete Probabilistic Distribution termed RASCAL, a barrier crossing method and a Monte Carlo method. An initial load model was developed by Battelle that is currently used for the slowly varying duty cycle type loading. The intent is to use the model and related codes essentially in the current form for all loads that are based on measured or calculated data that have followed a slowly varying profile.

Integrating statistical and process-based models to produce probabilistic landslide hazard at regional scale

NASA Astrophysics Data System (ADS)

Strauch, R. L.; Istanbulluoglu, E.

2017-12-01

We develop a landslide hazard modeling approach that integrates a data-driven statistical model and a probabilistic process-based shallow landslide model for mapping probability of landslide initiation, transport, and deposition at regional scales. The empirical model integrates the influence of seven site attribute (SA) classes: elevation, slope, curvature, aspect, land use-land cover, lithology, and topographic wetness index, on over 1,600 observed landslides using a frequency ratio (FR) approach. A susceptibility index is calculated by adding FRs for each SA on a grid-cell basis. Using landslide observations we relate susceptibility index to an empirically-derived probability of landslide impact. This probability is combined with results from a physically-based model to produce an integrated probabilistic map. Slope was key in landslide initiation while deposition was linked to lithology and elevation. Vegetation transition from forest to alpine vegetation and barren land cover with lower root cohesion leads to higher frequency of initiation. Aspect effects are likely linked to differences in root cohesion and moisture controlled by solar insulation and snow. We demonstrate the model in the North Cascades of Washington, USA and identify locations of high and low probability of landslide impacts that can be used by land managers in their design, planning, and maintenance.

Probabilistic evaluation of SSME structural components

NASA Astrophysics Data System (ADS)

Rajagopal, K. R.; Newell, J. F.; Ho, H.

1991-05-01

The application is described of Composite Load Spectra (CLS) and Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) family of computer codes to the probabilistic structural analysis of four Space Shuttle Main Engine (SSME) space propulsion system components. These components are subjected to environments that are influenced by many random variables. The applications consider a wide breadth of uncertainties encountered in practice, while simultaneously covering a wide area of structural mechanics. This has been done consistent with the primary design requirement for each component. The probabilistic application studies are discussed using finite element models that have been typically used in the past in deterministic analysis studies.

Generalized probabilistic scale space for image restoration.

PubMed

Wong, Alexander; Mishra, Akshaya K

2010-10-01

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.

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

PubMed Central

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

2014-01-01

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

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

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

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

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

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

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

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

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

Spencer, Benjamin; Hoffman, William; Sen, Sonat

2015-10-01

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

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 robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

2016-12-01

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

The composite load spectra project

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

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

PubMed

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

2003-11-01

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

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.

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

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.

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Probabilistic graphlet transfer for photo cropping.

PubMed

Zhang, Luming; Song, Mingli; Zhao, Qi; Liu, Xiao; Bu, Jiajun; Chen, Chun

2013-02-01

As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.

Modular analysis of the probabilistic genetic interaction network.

PubMed

Hou, Lin; Wang, Lin; Qian, Minping; Li, Dong; Tang, Chao; Zhu, Yunping; Deng, Minghua; Li, Fangting

2011-03-15

Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network. Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.

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

bayesPop: Probabilistic Population Projections

PubMed Central

Ševčíková, Hana; Raftery, Adrian E.

2016-01-01

We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age- and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects. PMID:28077933

Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction

PubMed Central

Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

2015-01-01

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error. PMID:27081304

Advanced Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Technical Exchange Meeting

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

Smith, Curtis

2013-09-01

During FY13, the INL developed an advanced SMR PRA framework which has been described in the report Small Modular Reactor (SMR) Probabilistic Risk Assessment (PRA) Detailed Technical Framework Specification, INL/EXT-13-28974 (April 2013). In this framework, the various areas are considered: Probabilistic models to provide information specific to advanced SMRs Representation of specific SMR design issues such as having co-located modules and passive safety features Use of modern open-source and readily available analysis methods Internal and external events resulting in impacts to safety All-hazards considerations Methods to support the identification of design vulnerabilities Mechanistic and probabilistic data needs to support modelingmore » and tools In order to describe this framework more fully and obtain feedback on the proposed approaches, the INL hosted a technical exchange meeting during August 2013. This report describes the outcomes of that meeting.« less

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 Mesomechanical Fatigue Model

NASA Technical Reports Server (NTRS)

Tryon, Robert G.

1997-01-01

A probabilistic mesomechanical fatigue life model is proposed to link the microstructural material heterogeneities to the statistical scatter in the macrostructural response. The macrostructure is modeled as an ensemble of microelements. Cracks nucleation within the microelements and grow from the microelements to final fracture. Variations of the microelement properties are defined using statistical parameters. A micromechanical slip band decohesion model is used to determine the crack nucleation life and size. A crack tip opening displacement model is used to determine the small crack growth life and size. Paris law is used to determine the long crack growth life. The models are combined in a Monte Carlo simulation to determine the statistical distribution of total fatigue life for the macrostructure. The modeled response is compared to trends in experimental observations from the literature.

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.

Access control and privilege management in electronic health record: a systematic literature review.

PubMed

Jayabalan, Manoj; O'Daniel, Thomas

2016-12-01

This study presents a systematic literature review of access control for electronic health record systems to protect patient's privacy. Articles from 2006 to 2016 were extracted from the ACM Digital Library, IEEE Xplore Digital Library, Science Direct, MEDLINE, and MetaPress using broad eligibility criteria, and chosen for inclusion based on analysis of ISO22600. Cryptographic standards and methods were left outside the scope of this review. Three broad classes of models are being actively investigated and developed: access control for electronic health records, access control for interoperability, and access control for risk analysis. Traditional role-based access control models are extended with spatial, temporal, probabilistic, dynamic, and semantic aspects to capture contextual information and provide granular access control. Maintenance of audit trails and facilities for overriding normal roles to allow full access in emergency cases are common features. Access privilege frameworks utilizing ontology-based knowledge representation for defining the rules have attracted considerable interest, due to the higher level of abstraction that makes it possible to model domain knowledge and validate access requests efficiently.

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.

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.

Towards Characterization, Modeling, and Uncertainty Quantification in Multi-scale Mechanics of Oragnic-rich Shales

NASA Astrophysics Data System (ADS)

Abedi, S.; Mashhadian, M.; Noshadravan, A.

2015-12-01

Increasing the efficiency and sustainability in operation of hydrocarbon recovery from organic-rich shales requires a fundamental understanding of chemomechanical properties of organic-rich shales. This understanding is manifested in form of physics-bases predictive models capable of capturing highly heterogeneous and multi-scale structure of organic-rich shale materials. In this work we present a framework of experimental characterization, micromechanical modeling, and uncertainty quantification that spans from nanoscale to macroscale. Application of experiments such as coupled grid nano-indentation and energy dispersive x-ray spectroscopy and micromechanical modeling attributing the role of organic maturity to the texture of the material, allow us to identify unique clay mechanical properties among different samples that are independent of maturity of shale formations and total organic content. The results can then be used to inform the physically-based multiscale model for organic rich shales consisting of three levels that spans from the scale of elementary building blocks (e.g. clay minerals in clay-dominated formations) of organic rich shales to the scale of the macroscopic inorganic/organic hard/soft inclusion composite. Although this approach is powerful in capturing the effective properties of organic-rich shale in an average sense, it does not account for the uncertainty in compositional and mechanical model parameters. Thus, we take this model one step forward by systematically incorporating the main sources of uncertainty in modeling multiscale behavior of organic-rich shales. In particular we account for the uncertainty in main model parameters at different scales such as porosity, elastic properties and mineralogy mass percent. To that end, we use Maximum Entropy Principle and random matrix theory to construct probabilistic descriptions of model inputs based on available information. The Monte Carlo simulation is then carried out to propagate the uncertainty and consequently construct probabilistic descriptions of properties at multiple length-scales. The combination of experimental characterization and stochastic multi-scale modeling presented in this work improves the robustness in the prediction of essential subsurface parameters in engineering scale.

Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification.

PubMed

Li, Haoxiang; Hua, Gang

2018-04-01

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic part model. We extract local descriptors (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each descriptor with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of the face parts of all face images in the training corpus, namely the probabilistic elastic part (PEP) model. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms, which naturally defines a part. Given one or multiple face images of the same subject, the PEP-model builds its PEP representation by sequentially concatenating descriptors identified by each Gaussian component in a maximum likelihood sense. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that we achieve state-of-the-art face verification accuracy with the proposed representations on the Labeled Face in the Wild (LFW) dataset, the YouTube video face database, and the CMU MultiPIE dataset.

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

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.

Behavioral and Temporal Pattern Detection Within Financial Data With Hidden Information

DTIC Science & Technology

2012-02-01

probabilistic pattern detector to monitor the pattern. 15. SUBJECT TERMS Runtime verification, Hidden data, Hidden Markov models, Formal specifications...sequences in many other fields besides financial systems [L, TV, LC, LZ ]. Rather, the technique suggested in this paper is positioned as a hybrid...operation of the pattern detector . Section 7 describes the operation of the probabilistic pattern-matching monitor, and section 8 describes three

Conjoint-measurement framework for the study of probabilistic information processing.

NASA Technical Reports Server (NTRS)

Wallsten, T. S.

1972-01-01

The theory of conjoint measurement described by Krantz et al. (1971) is shown to indicate how a descriptive model of human processing of probabilistic information built around Bayes' rule is to be tested and how it is to be used to obtain subjective scale values. Specific relationships concerning these scale values are shown to emerge, and the theoretical prospects resulting from this development are discussed.

Maritime Threat Detection Using Probabilistic Graphical Models

DTIC Science & Technology

2012-01-01

CRF, unlike an HMM, can represent local features, and does not require feature concatenation. MLNs For MLNs, we used Alchemy ( Alchemy 2011), an...open source statistical relational learning and probabilistic inferencing package. Alchemy supports generative and discriminative weight learning, and...that Alchemy creates a new formula for every possible combination of the values for a1 and a2 that fit the type specified in their predicate

Terminal Model Of Newtonian Dynamics

NASA Technical Reports Server (NTRS)

Zak, Michail

1994-01-01

Paper presents study of theory of Newtonian dynamics of terminal attractors and repellers, focusing on issues of reversibility vs. irreversibility and deterministic evolution vs. probabilistic or chaotic evolution of dynamic systems. Theory developed called "terminal dynamics" emphasizes difference between it and classical Newtonian dynamics. Also holds promise for explaining irreversibility, unpredictability, probabilistic behavior, and chaos in turbulent flows, in thermodynamic phenomena, and in other dynamic phenomena and systems.

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...

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.

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

NASA Astrophysics Data System (ADS)

Zolfaghari, M. R.; Peyghaleh, E.

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Develop Probabilistic Tsunami Design Maps for ASCE 7

NASA Astrophysics Data System (ADS)

Wei, Y.; Thio, H. K.; Chock, G.; Titov, V. V.

2014-12-01

A national standard for engineering design for tsunami effects has not existed before and this significant risk is mostly ignored in engineering design. The American Society of Civil Engineers (ASCE) 7 Tsunami Loads and Effects Subcommittee is completing a chapter for the 2016 edition of ASCE/SEI 7 Standard. Chapter 6, Tsunami Loads and Effects, would become the first national tsunami design provisions. These provisions will apply to essential facilities and critical infrastructure. This standard for tsunami loads and effects will apply to designs as part of the tsunami preparedness. The provisions will have significance as the post-tsunami recovery tool, to plan and evaluate for reconstruction. Maps of 2,500-year probabilistic tsunami inundation for Alaska, Washington, Oregon, California, and Hawaii need to be developed for use with the ASCE design provisions. These new tsunami design zone maps will define the coastal zones where structures of greater importance would be designed for tsunami resistance and community resilience. The NOAA Center for Tsunami Research (NCTR) has developed 75 tsunami inundation models as part of the operational tsunami model forecast capability for the U.S. coastline. NCTR, UW, and URS are collaborating with ASCE to develop the 2,500-year tsunami design maps for the Pacific states using these tsunami models. This ensures the probabilistic criteria are established in ASCE's tsunami design maps. URS established a Probabilistic Tsunami Hazard Assessment approach consisting of a large amount of tsunami scenarios that include both epistemic uncertainty and aleatory variability (Thio et al., 2010). Their study provides 2,500-year offshore tsunami heights at the 100-m water depth, along with the disaggregated earthquake sources. NOAA's tsunami models are used to identify a group of sources that produce these 2,500-year tsunami heights. The tsunami inundation limits and runup heights derived from these sources establish the tsunami design map for the study site. ASCE's Energy Grad Line Analysis then uses these modeling constraints to derive hydrodynamic forces for structures within the tsunami design zone. The probabilistic tsunami design maps will be validated by comparison to state inundation maps under the coordination of the National Tsunami Hazard Mitigation Program.

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

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

A probabilistic method for constructing wave time-series at inshore locations using model scenarios

USGS Publications Warehouse

Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.

2014-01-01

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Pharmacological Fingerprints of Contextual Uncertainty

PubMed Central

Ruge, Diane; Stephan, Klaas E.

2016-01-01

Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses. PMID:27846219

Demonstration of the Application of Composite Load Spectra (CLS) and Probabilistic Structural Analysis (PSAM) Codes to SSME Heat Exchanger Turnaround Vane

NASA Technical Reports Server (NTRS)

Rajagopal, Kadambi R.; DebChaudhury, Amitabha; Orient, George

2000-01-01

This report describes a probabilistic structural analysis performed to determine the probabilistic structural response under fluctuating random pressure loads for the Space Shuttle Main Engine (SSME) turnaround vane. It uses a newly developed frequency and distance dependent correlation model that has features to model the decay phenomena along the flow and across the flow with the capability to introduce a phase delay. The analytical results are compared using two computer codes SAFER (Spectral Analysis of Finite Element Responses) and NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) and with experimentally observed strain gage data. The computer code NESSUS with an interface to a sub set of Composite Load Spectra (CLS) code is used for the probabilistic analysis. A Fatigue code was used to calculate fatigue damage due to the random pressure excitation. The random variables modeled include engine system primitive variables that influence the operating conditions, convection velocity coefficient, stress concentration factor, structural damping, and thickness of the inner and outer vanes. The need for an appropriate correlation model in addition to magnitude of the PSD is emphasized. The study demonstrates that correlation characteristics even under random pressure loads are capable of causing resonance like effects for some modes. The study identifies the important variables that contribute to structural alternate stress response and drive the fatigue damage for the new design. Since the alternate stress for the new redesign is less than the endurance limit for the material, the damage due high cycle fatigue is negligible.

Explaining differences between bioaccumulation measurements in laboratory and field data through use of a probabilistic modeling approach

USGS Publications Warehouse

Selck, Henriette; Drouillard, Ken; Eisenreich, Karen; Koelmans, Albert A.; Palmqvist, Annemette; Ruus, Anders; Salvito, Daniel; Schultz, Irv; Stewart, Robin; Weisbrod, Annie; van den Brink, Nico W.; van den Heuvel-Greve, Martine

2012-01-01

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps.

Many roads to synchrony: natural time scales and their algorithms.

PubMed

James, Ryan G; Mahoney, John R; Ellison, Christopher J; Crutchfield, James P

2014-04-01

We consider two important time scales-the Markov and cryptic orders-that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these orders exactly and that they are most efficiently calculated from the ε-machine, a process's minimal unifilar model. Surprisingly, though the Markov order is a basic concept from stochastic process theory, it is not a probabilistic property of a process. Rather, it is a topological property and, moreover, it is not computable from any finite-state model other than the ε-machine. Via an exhaustive survey, we close by demonstrating that infinite Markov and infinite cryptic orders are a dominant feature in the space of finite-memory processes. We draw out the roles played in statistical mechanical spin systems by these two complementary length scales.

Probabilistic simulation of the human factor in structural reliability

NASA Technical Reports Server (NTRS)

Chamis, C. C.

1993-01-01

A formal approach is described in an attempt to computationally simulate the probable ranges of uncertainties of the human factor in structural probabilistic assessments. A multi-factor interaction equation (MFIE) model has been adopted for this purpose. Human factors such as marital status, professional status, home life, job satisfaction, work load and health, are considered to demonstrate the concept. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Suitability of the MFIE in the subsequently probabilistic sensitivity studies are performed to assess the validity of the whole approach. Results obtained show that the uncertainties for no error range from five to thirty percent for the most optimistic case.

Probabilistic simulation of the human factor in structural reliability

NASA Astrophysics Data System (ADS)

Chamis, Christos C.; Singhal, Surendra N.

1994-09-01

The formal approach described herein computationally simulates the probable ranges of uncertainties for the human factor in probabilistic assessments of structural reliability. Human factors such as marital status, professional status, home life, job satisfaction, work load, and health are studied by using a multifactor interaction equation (MFIE) model to demonstrate the approach. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Subsequently performed probabilistic sensitivity studies assess the suitability of the MFIE as well as the validity of the whole approach. Results show that uncertainties range from 5 to 30 percent for the most optimistic case, assuming 100 percent for no error (perfect performance).

Typing mineral deposits using their associated rocks, grades and tonnages using a probabilistic neural network

USGS Publications Warehouse

Singer, D.A.

2006-01-01

A probabilistic neural network is employed to classify 1610 mineral deposits into 18 types using tonnage, average Cu, Mo, Ag, Au, Zn, and Pb grades, and six generalized rock types. The purpose is to examine whether neural networks might serve for integrating geoscience information available in large mineral databases to classify sites by deposit type. Successful classifications of 805 deposits not used in training - 87% with grouped porphyry copper deposits - and the nature of misclassifications demonstrate the power of probabilistic neural networks and the value of quantitative mineral-deposit models. The results also suggest that neural networks can classify deposits as well as experienced economic geologists. ?? International Association for Mathematical Geology 2006.

Development of Probabilistic Structural Analysis Integrated with Manufacturing Processes

NASA Technical Reports Server (NTRS)

Pai, Shantaram S.; Nagpal, Vinod K.

2007-01-01

An effort has been initiated to integrate manufacturing process simulations with probabilistic structural analyses in order to capture the important impacts of manufacturing uncertainties on component stress levels and life. Two physics-based manufacturing process models (one for powdered metal forging and the other for annular deformation resistance welding) have been linked to the NESSUS structural analysis code. This paper describes the methodology developed to perform this integration including several examples. Although this effort is still underway, particularly for full integration of a probabilistic analysis, the progress to date has been encouraging and a software interface that implements the methodology has been developed. The purpose of this paper is to report this preliminary development.

Probabilistic micromechanics for metal matrix composites

NASA Astrophysics Data System (ADS)

Engelstad, S. P.; Reddy, J. N.; Hopkins, Dale A.

A probabilistic micromechanics-based nonlinear analysis procedure is developed to predict and quantify the variability in the properties of high temperature metal matrix composites. Monte Carlo simulation is used to model the probabilistic distributions of the constituent level properties including fiber, matrix, and interphase properties, volume and void ratios, strengths, fiber misalignment, and nonlinear empirical parameters. The procedure predicts the resultant ply properties and quantifies their statistical scatter. Graphite copper and Silicon Carbide Titanlum Aluminide (SCS-6 TI15) unidirectional plies are considered to demonstrate the predictive capabilities. The procedure is believed to have a high potential for use in material characterization and selection to precede and assist in experimental studies of new high temperature metal matrix composites.

Probabilistic Simulation of the Human Factor in Structural Reliability

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Singhal, Surendra N.

1994-01-01

The formal approach described herein computationally simulates the probable ranges of uncertainties for the human factor in probabilistic assessments of structural reliability. Human factors such as marital status, professional status, home life, job satisfaction, work load, and health are studied by using a multifactor interaction equation (MFIE) model to demonstrate the approach. Parametric studies in conjunction with judgment are used to select reasonable values for the participating factors (primitive variables). Subsequently performed probabilistic sensitivity studies assess the suitability of the MFIE as well as the validity of the whole approach. Results show that uncertainties range from 5 to 30 percent for the most optimistic case, assuming 100 percent for no error (perfect performance).

A probabilistic drought forecasting framework: A combined dynamical and statistical approach

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

Yan, Hongxiang; Moradkhani, Hamid; Zarekarizi, Mahkameh

In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initialmore » condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration.« less

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-05-25

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

Modality, probability, and mental models.

PubMed

Hinterecker, Thomas; Knauff, Markus; Johnson-Laird, P N

2016-10-01

We report 3 experiments investigating novel sorts of inference, such as: A or B or both. Therefore, possibly (A and B). Where the contents were sensible assertions, for example, Space tourism will achieve widespread popularity in the next 50 years or advances in material science will lead to the development of antigravity materials in the next 50 years, or both . Most participants accepted the inferences as valid, though they are invalid in modal logic and in probabilistic logic too. But, the theory of mental models predicts that individuals should accept them. In contrast, inferences of this sort—A or B but not both. Therefore, A or B or both—are both logically valid and probabilistically valid. Yet, as the model theory also predicts, most reasoners rejected them. The participants’ estimates of probabilities showed that their inferences tended not to be based on probabilistic validity, but that they did rate acceptable conclusions as more probable than unacceptable conclusions. We discuss the implications of the results for current theories of reasoning. PsycINFO Database Record (c) 2016 APA, all rights reserved

Addressing the Hard Factors for Command File Errors by Probabilistic Reasoning

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Bryant, Larry

2014-01-01

Command File Errors (CFE) are managed using standard risk management approaches at the Jet Propulsion Laboratory. Over the last few years, more emphasis has been made on the collection, organization, and analysis of these errors for the purpose of reducing the CFE rates. More recently, probabilistic modeling techniques have been used for more in depth analysis of the perceived error rates of the DAWN mission and for managing the soft factors in the upcoming phases of the mission. We broadly classify the factors that can lead to CFE's as soft factors, which relate to the cognition of the operators and hard factors which relate to the Mission System which is composed of the hardware, software and procedures used for the generation, verification & validation and execution of commands. The focus of this paper is to use probabilistic models that represent multiple missions at JPL to determine the root cause and sensitivities of the various components of the mission system and develop recommendations and techniques for addressing them. The customization of these multi-mission models to a sample interplanetary spacecraft is done for this purpose.

Efficient Location Uncertainty Treatment for Probabilistic Modelling of Portfolio Loss from Earthquake Events

NASA Astrophysics Data System (ADS)

Scheingraber, Christoph; Käser, Martin; Allmann, Alexander

2017-04-01

Probabilistic seismic risk analysis (PSRA) is a well-established method for modelling loss from earthquake events. In the insurance industry, it is widely employed for probabilistic modelling of loss to a distributed portfolio. In this context, precise exposure locations are often unknown, which results in considerable loss uncertainty. The treatment of exposure uncertainty has already been identified as an area where PSRA would benefit from increased research attention. However, so far, epistemic location uncertainty has not been in the focus of a large amount of research. We propose a new framework for efficient treatment of location uncertainty. To demonstrate the usefulness of this novel method, a large number of synthetic portfolios resembling real-world portfolios is systematically analyzed. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on loss variability. Several sampling criteria to increase the computational efficiency of the framework are proposed and put into the wider context of well-established Monte-Carlo variance reduction techniques. The performance of each of the proposed criteria is analyzed.

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Probabilistic framework for product design optimization and risk management

NASA Astrophysics Data System (ADS)

Keski-Rahkonen, J. K.

2018-05-01

Probabilistic methods have gradually gained ground within engineering practices but currently it is still the industry standard to use deterministic safety margin approaches to dimensioning components and qualitative methods to manage product risks. These methods are suitable for baseline design work but quantitative risk management and product reliability optimization require more advanced predictive approaches. Ample research has been published on how to predict failure probabilities for mechanical components and furthermore to optimize reliability through life cycle cost analysis. This paper reviews the literature for existing methods and tries to harness their best features and simplify the process to be applicable in practical engineering work. Recommended process applies Monte Carlo method on top of load-resistance models to estimate failure probabilities. Furthermore, it adds on existing literature by introducing a practical framework to use probabilistic models in quantitative risk management and product life cycle costs optimization. The main focus is on mechanical failure modes due to the well-developed methods used to predict these types of failures. However, the same framework can be applied on any type of failure mode as long as predictive models can be developed.