WorldWideScience

Sample records for probabilistic choice models

  1. Generalized outcome-based strategy classification: comparing deterministic and probabilistic choice models.

    Science.gov (United States)

    Hilbig, Benjamin E; Moshagen, Morten

    2014-12-01

    Model comparisons are a vital tool for disentangling which of several strategies a decision maker may have used--that is, which cognitive processes may have governed observable choice behavior. However, previous methodological approaches have been limited to models (i.e., decision strategies) with deterministic choice rules. As such, psychologically plausible choice models--such as evidence-accumulation and connectionist models--that entail probabilistic choice predictions could not be considered appropriately. To overcome this limitation, we propose a generalization of Bröder and Schiffer's (Journal of Behavioral Decision Making, 19, 361-380, 2003) choice-based classification method, relying on (1) parametric order constraints in the multinomial processing tree framework to implement probabilistic models and (2) minimum description length for model comparison. The advantages of the generalized approach are demonstrated through recovery simulations and an experiment. In explaining previous methods and our generalization, we maintain a nontechnical focus--so as to provide a practical guide for comparing both deterministic and probabilistic choice models.

  2. On synchronous parallel computations with independent probabilistic choice

    International Nuclear Information System (INIS)

    Reif, J.H.

    1984-01-01

    This paper introduces probabilistic choice to synchronous parallel machine models; in particular parallel RAMs. The power of probabilistic choice in parallel computations is illustrate by parallelizing some known probabilistic sequential algorithms. The authors characterize the computational complexity of time, space, and processor bounded probabilistic parallel RAMs in terms of the computational complexity of probabilistic sequential RAMs. They show that parallelism uniformly speeds up time bounded probabilistic sequential RAM computations by nearly a quadratic factor. They also show that probabilistic choice can be eliminated from parallel computations by introducing nonuniformity

  3. Relative Gains, Losses, and Reference Points in Probabilistic Choice in Rats

    Science.gov (United States)

    Marshall, Andrew T.; Kirkpatrick, Kimberly

    2015-01-01

    Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s) on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice) and one that probabilistically delivered reward (high-uncertainty). The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S) and high-uncertainty-larger (H-L) outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior. PMID:25658448

  4. Relative gains, losses, and reference points in probabilistic choice in rats.

    Directory of Open Access Journals (Sweden)

    Andrew T Marshall

    Full Text Available Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice and one that probabilistically delivered reward (high-uncertainty. The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S and high-uncertainty-larger (H-L outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior.

  5. The Probabilistic Nature of Preferential Choice

    Science.gov (United States)

    Rieskamp, Jorg

    2008-01-01

    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…

  6. The Effects of the Previous Outcome on Probabilistic Choice in Rats

    Science.gov (United States)

    Marshall, Andrew T.; Kirkpatrick, Kimberly

    2014-01-01

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

  7. Probabilistic escalation modelling

    Energy Technology Data Exchange (ETDEWEB)

    Korneliussen, G.; Eknes, M.L.; Haugen, K.; Selmer-Olsen, S. [Det Norske Veritas, Oslo (Norway)

    1997-12-31

    This paper describes how structural reliability methods may successfully be applied within quantitative risk assessment (QRA) as an alternative to traditional event tree analysis. The emphasis is on fire escalation in hydrocarbon production and processing facilities. This choice was made due to potential improvements over current QRA practice associated with both the probabilistic approach and more detailed modelling of the dynamics of escalating events. The physical phenomena important for the events of interest are explicitly modelled as functions of time. Uncertainties are represented through probability distributions. The uncertainty modelling enables the analysis to be simple when possible and detailed when necessary. The methodology features several advantages compared with traditional risk calculations based on event trees. (Author)

  8. Learning Probabilistic Logic Models from Probabilistic Examples.

    Science.gov (United States)

    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.

  9. Probabilistic Logical Characterization

    DEFF Research Database (Denmark)

    Hermanns, Holger; Parma, Augusto; Segala, Roberto

    2011-01-01

    Probabilistic automata exhibit both probabilistic and non-deterministic choice. They are therefore a powerful semantic foundation for modeling concurrent systems with random phenomena arising in many applications ranging from artificial intelligence, security, systems biology to performance...... modeling. Several variations of bisimulation and simulation relations have proved to be useful as means to abstract and compare different automata. This paper develops a taxonomy of logical characterizations of these relations on image-finite and image-infinite probabilistic automata....

  10. A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation.

    Science.gov (United States)

    2015-12-01

    This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling u...

  11. PROBABILISTIC RELATIONAL MODELS OF COMPLETE IL-SEMIRINGS

    OpenAIRE

    Tsumagari, Norihiro

    2012-01-01

    This paper studies basic properties of probabilistic multirelations which are generalized the semantic domain of probabilistic systems and then provides two probabilistic models of complete IL-semirings using probabilistic multirelations. Also it is shown that these models need not be models of complete idempotentsemirings.

  12. Probabilistic modeling of timber structures

    DEFF Research Database (Denmark)

    Köhler, Jochen; Sørensen, John Dalsgaard; Faber, Michael Havbro

    2007-01-01

    The present paper contains a proposal for the probabilistic modeling of timber material properties. It is produced in the context of the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS) [Joint Committee of Structural Safety. Probabilistic Model Code, Internet...... Publication: www.jcss.ethz.ch; 2001] and of the COST action E24 ‘Reliability of Timber Structures' [COST Action E 24, Reliability of timber structures. Several meetings and Publications, Internet Publication: http://www.km.fgg.uni-lj.si/coste24/coste24.htm; 2005]. The present proposal is based on discussions...... and comments from participants of the COST E24 action and the members of the JCSS. The paper contains a description of the basic reference properties for timber strength parameters and ultimate limit state equations for timber components. The recommended probabilistic model for these basic properties...

  13. Probabilistic Harmonic Modeling of Wind Power Plants

    DEFF Research Database (Denmark)

    Guest, Emerson; Jensen, Kim H.; Rasmussen, Tonny Wederberg

    2017-01-01

    A probabilistic sequence domain (SD) harmonic model of a grid-connected voltage-source converter is used to estimate harmonic emissions in a wind power plant (WPP) comprised of Type-IV wind turbines. The SD representation naturally partitioned converter generated voltage harmonics into those...... with deterministic phase and those with probabilistic phase. A case study performed on a string of ten 3MW, Type-IV wind turbines implemented in PSCAD was used to verify the probabilistic SD harmonic model. The probabilistic SD harmonic model can be employed in the planning phase of WPP projects to assess harmonic...

  14. When good pigeons make bad decisions: Choice with probabilistic delays and outcomes.

    Science.gov (United States)

    Pisklak, Jeffrey M; McDevitt, Margaret A; Dunn, Roger M; Spetch, Marcia L

    2015-11-01

    Pigeons chose between an (optimal) alternative that sometimes provided food after a 10-s delay and other times after a 40-s delay and another (suboptimal) alternative that sometimes provided food after 10 s but other times no food after 40 s. When outcomes were not signaled during the delays, pigeons strongly preferred the optimal alternative. When outcomes were signaled, choices of the suboptimal alternative increased and most pigeons preferred the alternative that provided no food after the long delay despite the cost in terms of obtained food. The pattern of results was similar whether the short delays occurred on 25% or 50% of the trials. Shortening the 40-s delay to food sharply reduced suboptimal choices, but shortening the delay to no food had little effect. The results suggest that a signaled delay to no food does not punish responding in probabilistic choice procedures. The findings are discussed in terms of conditioned reinforcement by signals for good news. © Society for the Experimental Analysis of Behavior.

  15. Probabilistic reasoning for assembly-based 3D modeling

    KAUST Repository

    Chaudhuri, Siddhartha

    2011-01-01

    Assembly-based modeling is a promising approach to broadening the accessibility of 3D modeling. In assembly-based modeling, new models are assembled from shape components extracted from a database. A key challenge in assembly-based modeling is the identification of relevant components to be presented to the user. In this paper, we introduce a probabilistic reasoning approach to this problem. Given a repository of shapes, our approach learns a probabilistic graphical model that encodes semantic and geometric relationships among shape components. The probabilistic model is used to present components that are semantically and stylistically compatible with the 3D model that is being assembled. Our experiments indicate that the probabilistic model increases the relevance of presented components. © 2011 ACM.

  16. Probabilistic reasoning with graphical security models

    NARCIS (Netherlands)

    Kordy, Barbara; Pouly, Marc; Schweitzer, Patrick

    This work provides a computational framework for meaningful probabilistic evaluation of attack–defense scenarios involving dependent actions. We combine the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. In order

  17. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  18. Probabilistic Modeling of Timber Structures

    DEFF Research Database (Denmark)

    Köhler, J.D.; Sørensen, John Dalsgaard; Faber, Michael Havbro

    2005-01-01

    The present paper contains a proposal for the probabilistic modeling of timber material properties. It is produced in the context of the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS) and of the COST action E24 'Reliability of Timber Structures'. The present...... proposal is based on discussions and comments from participants of the COST E24 action and the members of the JCSS. The paper contains a description of the basic reference properties for timber strength parameters and ultimate limit state equations for components and connections. The recommended...

  19. Probabilistic modelling of the high-pressure arc cathode spot displacement dynamic

    International Nuclear Information System (INIS)

    Coulombe, Sylvain

    2003-01-01

    A probabilistic modelling approach for the study of the cathode spot displacement dynamic in high-pressure arc systems is developed in an attempt to interpret the observed voltage fluctuations. The general framework of the model allows to define simple, probabilistic displacement rules, the so-called cathode spot dynamic rules, for various possible surface states (un-arced metal, arced, contaminated) and to study the resulting dynamic of the cathode spot displacements over one or several arc passages. The displacements of the type-A cathode spot (macro-spot) in a magnetically rotating arc using concentric electrodes made up of either clean or contaminated metal surfaces is considered. Experimental observations for this system revealed a 1/f -tilde1 signature in the frequency power spectrum (FPS) of the arc voltage for anchoring arc conditions on the cathode (e.g. clean metal surface), while it shows a 'white noise' signature for conditions favouring a smooth movement (e.g. oxide-contaminated cathode surface). Through an appropriate choice of the local probabilistic displacement rules, the model is able to correctly represent the dynamic behaviours of the type-A cathode spot, including the FPS for the arc elongation (i.e. voltage) and the arc erosion trace formation. The model illustrates that the cathode spot displacements between re-strikes can be seen as a diffusion process with a diffusion constant which depends on the surface structure. A physical interpretation for the jumping probability associated with the re-strike event is given in terms of the electron emission processes across dielectric contaminants present on the cathode surface

  20. A probabilistic graphical model based stochastic input model construction

    International Nuclear Information System (INIS)

    Wan, Jiang; Zabaras, Nicholas

    2014-01-01

    Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media

  1. Probabilistic modeling of discourse-aware sentence processing.

    Science.gov (United States)

    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.

  2. HMM_Model-Checker pour la vérification probabiliste HMM_Model ...

    African Journals Online (AJOL)

    ASSIA

    probabiliste –Télescope Hubble. Abstract. Probabilistic verification for embedded systems continues to attract more and more followers in the research community. Given a probabilistic model, a formula of temporal logic, describing a property of a system and an exploration algorithm to check whether the property is satisfied ...

  3. CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

    KAUST Repository

    Zhang, Kai-Ke

    2016-04-11

    Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.

  4. CAD Parts-Based Assembly Modeling by Probabilistic Reasoning

    KAUST Repository

    Zhang, Kai-Ke; Hu, Kai-Mo; Yin, Li-Cheng; Yan, Dongming; Wang, Bin

    2016-01-01

    Nowadays, increasing amount of parts and sub-assemblies are publicly available, which can be used directly for product development instead of creating from scratch. In this paper, we propose an interactive design framework for efficient and smart assembly modeling, in order to improve the design efficiency. Our approach is based on a probabilistic reasoning. Given a collection of industrial assemblies, we learn a probabilistic graphical model from the relationships between the parts of assemblies. Then in the modeling stage, this probabilistic model is used to suggest the most likely used parts compatible with the current assembly. Finally, the parts are assembled under certain geometric constraints. We demonstrate the effectiveness of our framework through a variety of assembly models produced by our prototype system. © 2015 IEEE.

  5. Probabilistic, meso-scale flood loss modelling

    Science.gov (United States)

    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.

  6. Transitive probabilistic CLIR models.

    NARCIS (Netherlands)

    Kraaij, W.; de Jong, Franciska M.G.

    2004-01-01

    Transitive translation could be a useful technique to enlarge the number of supported language pairs for a cross-language information retrieval (CLIR) system in a cost-effective manner. The paper describes several setups for transitive translation based on probabilistic translation models. The

  7. Efficient probabilistic model checking on general purpose graphic processors

    NARCIS (Netherlands)

    Bosnacki, D.; Edelkamp, S.; Sulewski, D.; Pasareanu, C.S.

    2009-01-01

    We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). For this purpose we exploit the fact that some of the basic algorithms for probabilistic model checking rely on matrix vector multiplication. Since this kind of linear algebraic

  8. Probabilistic Models for Solar Particle Events

    Science.gov (United States)

    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.

  9. Probabilistic Model Development

    Science.gov (United States)

    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.

  10. How social information affects information search and choice in probabilistic inferences.

    Science.gov (United States)

    Puskaric, Marin; von Helversen, Bettina; Rieskamp, Jörg

    2018-01-01

    When making decisions, people are often exposed to relevant information stemming from qualitatively different sources. For instance, when making a choice between two alternatives people can rely on the advice of other people (i.e., social information) or search for factual information about the alternatives (i.e., non-social information). Prior research in categorization has shown that social information is given special attention when both social and non-social information is available, even when the social information has no additional informational value. The goal of the current work is to investigate whether framing information as social or non-social also influences information search and choice in probabilistic inferences. In a first study, we found that framing cues (i.e., the information used to make a decision) with medium validity as social increased the probability that they were searched for compared to a task where the same cues were framed as non-social information, but did not change the strategy people relied on. A second and a third study showed that framing a cue with high validity as social information facilitated learning to rely on a non-compensatory decision strategy. Overall, the results suggest that social in comparison to non-social information is given more attention and is learned faster than non-social information. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. ISSUES ASSOCIATED WITH PROBABILISTIC FAILURE MODELING OF DIGITAL SYSTEMS

    International Nuclear Information System (INIS)

    CHU, T.L.; MARTINEZ-GURIDI, G.; LIHNER, J.; OVERLAND, D.

    2004-01-01

    The current U.S. Nuclear Regulatory Commission (NRC) licensing process of instrumentation and control (I and C) systems is based on deterministic requirements, e.g., single failure criteria, and defense in depth and diversity. Probabilistic considerations can be used as supplements to the deterministic process. The National Research Council has recommended development of methods for estimating failure probabilities of digital systems, including commercial off-the-shelf (COTS) equipment, for use in probabilistic risk assessment (PRA). NRC staff has developed informal qualitative and quantitative requirements for PRA modeling of digital systems. Brookhaven National Laboratory (BNL) has performed a review of the-state-of-the-art of the methods and tools that can potentially be used to model digital systems. The objectives of this paper are to summarize the review, discuss the issues associated with probabilistic modeling of digital systems, and identify potential areas of research that would enhance the state of the art toward a satisfactory modeling method that could be integrated with a typical probabilistic risk assessment

  12. Probabilistic approaches to recommendations

    CERN Document Server

    Barbieri, Nicola; Ritacco, Ettore

    2014-01-01

    The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robus

  13. Probabilistic Model for Fatigue Crack Growth in Welded Bridge Details

    DEFF Research Database (Denmark)

    Toft, Henrik Stensgaard; Sørensen, John Dalsgaard; Yalamas, Thierry

    2013-01-01

    In the present paper a probabilistic model for fatigue crack growth in welded steel details in road bridges is presented. The probabilistic model takes the influence of bending stresses in the joints into account. The bending stresses can either be introduced by e.g. misalignment or redistribution...... of stresses in the structure. The fatigue stress ranges are estimated from traffic measurements and a generic bridge model. Based on the probabilistic models for the resistance and load the reliability is estimated for a typical welded steel detail. The results show that large misalignments in the joints can...

  14. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

    Science.gov (United States)

    Orhan, A Emin; Ma, Wei Ji

    2017-07-26

    Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

  15. Integration of Advanced Probabilistic Analysis Techniques with Multi-Physics Models

    Energy Technology Data Exchange (ETDEWEB)

    Cetiner, Mustafa Sacit; none,; Flanagan, George F. [ORNL; Poore III, Willis P. [ORNL; Muhlheim, Michael David [ORNL

    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 types 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.

  16. Probabilistic Assessment of the Occurrence and Duration of Ice Accretion on Cables

    DEFF Research Database (Denmark)

    Roldsgaard, Joan Hee; Georgakis, Christos Thomas; Faber, Michael Havbro

    2015-01-01

    This paper presents an operational framework for assessing the probability of occurrence of in-cloud and precipitation icing and its duration. The framework utilizes the features of the Bayesian Probabilistic Networks. and its performance is illustrated through a case study of the cable-stayed...... Oresund Bridge. The Bayesian Probabilistic Network model used for the estimation of the occurrence and duration probabilities is studied and it is found to be robust with respect to changes in the choice of distribution types used to model the meteorological variables that influence the two icing...

  17. Undecidability of model-checking branching-time properties of stateless probabilistic pushdown process

    OpenAIRE

    Lin, T.

    2014-01-01

    In this paper, we settle a problem in probabilistic verification of infinite--state process (specifically, {\\it probabilistic pushdown process}). We show that model checking {\\it stateless probabilistic pushdown process} (pBPA) against {\\it probabilistic computational tree logic} (PCTL) is undecidable.

  18. Probabilistic language models in cognitive neuroscience: Promises and pitfalls.

    Science.gov (United States)

    Armeni, Kristijan; Willems, Roel M; Frank, Stefan L

    2017-12-01

    Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We highlight the advantages and potential pitfalls of this approach and indicate avenues for future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Fatigue modelling according to the JCSS Probabilistic model code

    NARCIS (Netherlands)

    Vrouwenvelder, A.C.W.M.

    2007-01-01

    The Joint Committee on Structural Safety is working on a Model Code for full probabilistic design. The code consists out of three major parts: Basis of design, Load Models and Models for Material and Structural Properties. The code is intended as the operational counter part of codes like ISO,

  20. Does a more sophisticated storm erosion model improve probabilistic erosion estimates?

    NARCIS (Netherlands)

    Ranasinghe, R.W.M.R.J.B.; Callaghan, D.; Roelvink, D.

    2013-01-01

    The dependency between the accuracy/uncertainty of storm erosion exceedance estimates obtained via a probabilistic model and the level of sophistication of the structural function (storm erosion model) embedded in the probabilistic model is assessed via the application of Callaghan et al.'s (2008)

  1. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2018-04-01

    Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

  2. A generative, probabilistic model of local protein structure

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Mardia, Kanti V.; Taylor, Charles C.

    2008-01-01

    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...

  3. Mastering probabilistic graphical models using Python

    CERN Document Server

    Ankan, Ankur

    2015-01-01

    If you are a researcher or a machine learning enthusiast, or are working in the data science field and have a basic idea of Bayesian learning or probabilistic graphical models, this book will help you to understand the details of graphical models and use them in your data science problems.

  4. An Individual-based Probabilistic Model for Fish Stock Simulation

    Directory of Open Access Journals (Sweden)

    Federico Buti

    2010-08-01

    Full Text Available We define an individual-based probabilistic model of a sole (Solea solea behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA, a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.

  5. Biological sequence analysis: probabilistic models of proteins and nucleic acids

    National Research Council Canada - National Science Library

    Durbin, Richard

    1998-01-01

    ... analysis methods are now based on principles of probabilistic modelling. Examples of such methods include the use of probabilistically derived score matrices to determine the significance of sequence alignments, the use of hidden Markov models as the basis for profile searches to identify distant members of sequence families, and the inference...

  6. Probabilistic Modeling of Wind Turbine Drivetrain Components

    DEFF Research Database (Denmark)

    Rafsanjani, Hesam Mirzaei

    Wind energy is one of several energy sources in the world and a rapidly growing industry in the energy sector. When placed in offshore or onshore locations, wind turbines are exposed to wave excitations, highly dynamic wind loads and/or the wakes from other wind turbines. Therefore, most components...... in a wind turbine experience highly dynamic and time-varying loads. These components may fail due to wear or fatigue, and this can lead to unplanned shutdown repairs that are very costly. The design by deterministic methods using safety factors is generally unable to account for the many uncertainties. Thus......, a reliability assessment should be based on probabilistic methods where stochastic modeling of failures is performed. This thesis focuses on probabilistic models and the stochastic modeling of the fatigue life of the wind turbine drivetrain. Hence, two approaches are considered for stochastic modeling...

  7. Probabilistically modeling lava flows with MOLASSES

    Science.gov (United States)

    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.

  8. Probabilistic Decision Graphs - Combining Verification and AI Techniques for Probabilistic Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2004-01-01

    We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic ...

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

    Science.gov (United States)

    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

  10. A probabilistic model of RNA conformational space

    DEFF Research Database (Denmark)

    Frellsen, Jes; Moltke, Ida; Thiim, Martin

    2009-01-01

    efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D......, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

  11. Modeling and control of an unstable system using probabilistic fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Sozhamadevi N.

    2015-09-01

    Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

  12. Probabilistic Modeling of the Renal Stone Formation Module

    Science.gov (United States)

    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

  13. Modelling probabilistic fatigue crack propagation rates for a mild structural steel

    Directory of Open Access Journals (Sweden)

    J.A.F.O. Correia

    2015-01-01

    Full Text Available A class of fatigue crack growth models based on elastic–plastic stress–strain histories at the crack tip region and local strain-life damage models have been proposed in literature. The fatigue crack growth is regarded as a process of continuous crack initializations over successive elementary material blocks, which may be governed by smooth strain-life damage data. Some approaches account for the residual stresses developing at the crack tip in the actual crack driving force assessment, allowing mean stresses and loading sequential effects to be modelled. An extension of the fatigue crack propagation model originally proposed by Noroozi et al. (2005 to derive probabilistic fatigue crack propagation data is proposed, in particular concerning the derivation of probabilistic da/dN-ΔK-R fields. The elastic-plastic stresses at the vicinity of the crack tip, computed using simplified formulae, are compared with the stresses computed using an elasticplastic finite element analyses for specimens considered in the experimental program proposed to derive the fatigue crack propagation data. Using probabilistic strain-life data available for the S355 structural mild steel, probabilistic crack propagation fields are generated, for several stress ratios, and compared with experimental fatigue crack propagation data. A satisfactory agreement between the predicted probabilistic fields and experimental data is observed.

  14. Structural reliability codes for probabilistic design

    DEFF Research Database (Denmark)

    Ditlevsen, Ove Dalager

    1997-01-01

    probabilistic code format has not only strong influence on the formal reliability measure, but also on the formal cost of failure to be associated if a design made to the target reliability level is considered to be optimal. In fact, the formal cost of failure can be different by several orders of size for two...... different, but by and large equally justifiable probabilistic code formats. Thus, the consequence is that a code format based on decision theoretical concepts and formulated as an extension of a probabilistic code format must specify formal values to be used as costs of failure. A principle of prudence...... is suggested for guiding the choice of the reference probabilistic code format for constant reliability. In the author's opinion there is an urgent need for establishing a standard probabilistic reliability code. This paper presents some considerations that may be debatable, but nevertheless point...

  15. Tensit - a novel probabilistic simulation tool for safety assessments. Tests and verifications using biosphere models

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Jakob; Vahlund, Fredrik; Kautsky, Ulrik

    2004-06-01

    This report documents the verification of a new simulation tool for dose assessment put together in a package under the name Tensit (Technical Nuclide Simulation Tool). The tool is developed to solve differential equation systems describing transport and decay of radionuclides. It is capable of handling both deterministic and probabilistic simulations. The verifications undertaken shows good results. Exceptions exist only where the reference results are unclear. Tensit utilise and connects two separate commercial softwares. The equation solving capability is derived from the Matlab/Simulink software environment to which Tensit adds a library of interconnectable building blocks. Probabilistic simulations are provided through a statistical software named at{sub R}isk that communicates with Matlab/Simulink. More information about these softwares can be found at www.palisade.com and www.mathworks.com. The underlying intention of developing this new tool has been to make available a cost efficient and easy to use means for advanced dose assessment simulations. The mentioned benefits are gained both through the graphical user interface provided by Simulink and at{sub R}isk, and the use of numerical equation solving routines in Matlab. To verify Tensit's numerical correctness, an implementation was done of the biosphere modules for dose assessments used in the earlier safety assessment project SR 97. Acquired probabilistic results for deterministic as well as probabilistic simulations have been compared with documented values. Additional verification has been made both with another simulation tool named AMBER and also against the international test case from PSACOIN named Level 1B. This report documents the models used for verification with equations and parameter values so that the results can be recreated. For a background and a more detailed description of the underlying processes in the models, the reader is referred to the original references. Finally, in the

  16. Tensit - a novel probabilistic simulation tool for safety assessments. Tests and verifications using biosphere models

    International Nuclear Information System (INIS)

    Jones, Jakob; Vahlund, Fredrik; Kautsky, Ulrik

    2004-06-01

    This report documents the verification of a new simulation tool for dose assessment put together in a package under the name Tensit (Technical Nuclide Simulation Tool). The tool is developed to solve differential equation systems describing transport and decay of radionuclides. It is capable of handling both deterministic and probabilistic simulations. The verifications undertaken shows good results. Exceptions exist only where the reference results are unclear. Tensit utilise and connects two separate commercial softwares. The equation solving capability is derived from the Matlab/Simulink software environment to which Tensit adds a library of interconnectable building blocks. Probabilistic simulations are provided through a statistical software named at R isk that communicates with Matlab/Simulink. More information about these softwares can be found at www.palisade.com and www.mathworks.com. The underlying intention of developing this new tool has been to make available a cost efficient and easy to use means for advanced dose assessment simulations. The mentioned benefits are gained both through the graphical user interface provided by Simulink and at R isk, and the use of numerical equation solving routines in Matlab. To verify Tensit's numerical correctness, an implementation was done of the biosphere modules for dose assessments used in the earlier safety assessment project SR 97. Acquired probabilistic results for deterministic as well as probabilistic simulations have been compared with documented values. Additional verification has been made both with another simulation tool named AMBER and also against the international test case from PSACOIN named Level 1B. This report documents the models used for verification with equations and parameter values so that the results can be recreated. For a background and a more detailed description of the underlying processes in the models, the reader is referred to the original references. Finally, in the perspective of

  17. Probabilistic Sophistication, Second Order Stochastic Dominance, and Uncertainty Aversion

    OpenAIRE

    Simone Cerreia-Vioglio; Fabio Maccheroni; Massimo Marinacci; Luigi Montrucchio

    2010-01-01

    We study the interplay of probabilistic sophistication, second order stochastic dominance, and uncertainty aversion, three fundamental notions in choice under uncertainty. In particular, our main result, Theorem 2, characterizes uncertainty averse preferences that satisfy second order stochastic dominance, as well as uncertainty averse preferences that are probabilistically sophisticated.

  18. Systems analysis approach to probabilistic modeling of fault trees

    International Nuclear Information System (INIS)

    Bartholomew, R.J.; Qualls, C.R.

    1985-01-01

    A method of probabilistic modeling of fault tree logic combined with stochastic process theory (Markov modeling) has been developed. Systems are then quantitatively analyzed probabilistically in terms of their failure mechanisms including common cause/common mode effects and time dependent failure and/or repair rate effects that include synergistic and propagational mechanisms. The modeling procedure results in a state vector set of first order, linear, inhomogeneous, differential equations describing the time dependent probabilities of failure described by the fault tree. The solutions of this Failure Mode State Variable (FMSV) model are cumulative probability distribution functions of the system. A method of appropriate synthesis of subsystems to form larger systems is developed and applied to practical nuclear power safety systems

  19. Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

    DEFF Research Database (Denmark)

    Valentin, Jan B.; Andreetta, Christian; Boomsma, Wouter

    2014-01-01

    We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length s....... The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. © 2013 Wiley Periodicals, Inc....

  20. Performance analysis of chi models using discrete-time probabilistic reward graphs

    NARCIS (Netherlands)

    Trcka, N.; Georgievska, S.; Markovski, J.; Andova, S.; Vink, de E.P.

    2008-01-01

    We propose the model of discrete-time probabilistic reward graphs (DTPRGs) for performance analysis of systems exhibiting discrete deterministic time delays and probabilistic behavior, via their interpretation as discrete-time Markov reward chains, full-fledged platform for qualitative and

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

    Energy Technology Data Exchange (ETDEWEB)

    Dyer, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Flach, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-12-12

    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 (AreaUAi/AreaSAi) 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.

  2. Probabilistic Radiological Performance Assessment Modeling and Uncertainty

    Science.gov (United States)

    Tauxe, J.

    2004-12-01

    A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A

  3. Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

    National Research Council Canada - National Science Library

    Sakhanenko, Nikita A; Luger, George F

    2008-01-01

    We propose a context-sensitive probabilistic modeling system (COSMOS) that reasons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information...

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

    Science.gov (United States)

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

    2014-01-01

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

  5. Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

    Science.gov (United States)

    Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

    2017-12-14

    Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

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

    Science.gov (United States)

    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.

  7. Next-generation probabilistic seismicity forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Hiemer, S.

    2014-07-01

    The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

  8. Next-generation probabilistic seismicity forecasting

    International Nuclear Information System (INIS)

    Hiemer, S.

    2014-01-01

    The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

  9. Application of probabilistic methods to safety R and D and design choices

    International Nuclear Information System (INIS)

    Gavigan, F.X.; Griffith, J.D.

    1977-01-01

    The Liquid Metal Fast Breeder Reactor (LMFBR) safety program is committed to identifying and exploiting areas in which probabilistic methods can be developed and used in making reactor safety R and D choices and optimizing designs of safety systems. Emphasis will be placed on a positive approach of solidifying and expanding our knowledge. This will provide the groundwork for a consensus on FBR risk. The management structure which will be used is based on a mechanistic approach to an LMFBR Core Disruptive Accident (CDA) with risk partitioned into ''Lines of Assurance,'' i.e., independent, phenomenologically-based barriers which will impede or mitigate the progression and consequences of accident sequences. Quantitative determination of the probability of breach of these barriers through the completion of work identified for each Line of Assurance will allow the quantification of the contribution to risk reduction associated with the success of each barrier. This process can lead to better use of resources by channeling R and D in directions which promise the greatest potential for reducing risk and by identifying an orderly approach to the development and demonstration of design features which will keep LMFBR risks at an acceptable level

  10. The subjective value of probabilistic outcomes: Impact of reward magnitude on choice with uncertain rewards in rats.

    Science.gov (United States)

    Zoratto, Francesca; Laviola, Giovanni; Adriani, Walter

    2016-03-23

    Interest is rising for animal modelling of Gambling disorder (GD), which is rapidly emerging as a mental health concern. In the present study, we assessed gambling proneness in male Wistar-Han rats using the "Probabilistic Delivery Task" (PDT). This operant protocol is based on choice between either certain, small amounts of food (SS) or larger amounts of food (LLL) delivered (or not) depending on a given (and progressively decreasing) probability. Here, we manipulated the ratio between large and small reward size to assess the impact of different magnitudes on rats' performance. Specifically, we drew a comparison between threefold (2 vs 6 pellets) and fivefold (1 vs 5 pellets) sizes. As a consequence, the "indifferent point" (IP, at which either choice is mathematically equivalent in terms of total foraging) was at 33% and 20% probability of delivery, respectively. Animals tested with the sharper contrast (i.e. fivefold ratio) exhibited sustained preference for LLL far beyond the IP, despite high uncertainty and low payoff, which rendered LLL a sub-optimal option. By contrast, animals facing a slighter contrast (i.e. threefold ratio) were increasingly disturbed by progressive rarefaction of rewards, as expressed by enhanced inadequate nose-poking: this was in accordance with their prompt shift in preference to SS, already shown around the IP. In conclusion, a five-folded LLL-to-SS ratio was not only more attractive, but also less frustrating than a three-folded one. Thus, a profile of gambling proneness in the PDT is more effectively induced by marked contrast between alternative options. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  11. Generative probabilistic models extend the scope of inferential structure determination

    DEFF Research Database (Denmark)

    Olsson, Simon; Boomsma, Wouter; Frellsen, Jes

    2011-01-01

    demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure......Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically...

  12. Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows

    NARCIS (Netherlands)

    Talebi, A.; Uijlenhoet, R.; Troch, P.A.

    2008-01-01

    Recently, D'Odorico and Fagherazzi (2003) proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003). Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building

  13. Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

    Science.gov (United States)

    Chua, Matthew; Chui, Chee-Kong

    2015-04-01

    Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Probabilistic flood damage modelling at the meso-scale

    Science.gov (United States)

    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.

  15. Probabilistic Modelling of Robustness and Resilience of Power Grid Systems

    DEFF Research Database (Denmark)

    Qin, Jianjun; Sansavini, Giovanni; Nielsen, Michael Havbro Faber

    2017-01-01

    The present paper proposes a framework for the modeling and analysis of resilience of networked power grid systems. A probabilistic systems model is proposed based on the JCSS Probabilistic Model Code (JCSS, 2001) and deterministic engineering systems modeling techniques such as the DC flow model...... cascading failure event scenarios (Nan and Sansavini, 2017). The concept of direct and indirect consequences proposed by the Joint Committee on Structural Safety (JCSS, 2008) is utilized to model the associated consequences. To facilitate a holistic modeling of robustness and resilience, and to identify how...... these characteristics may be optimized these characteristics, the power grid system is finally interlinked with its fundamental interdependent systems, i.e. a societal model, a regulatory system and control feedback loops. The proposed framework is exemplified with reference to optimal decision support for resilience...

  16. Financial Markets Analysis by Probabilistic Fuzzy Modelling

    NARCIS (Netherlands)

    J.H. van den Berg (Jan); W.-M. van den Bergh (Willem-Max); U. Kaymak (Uzay)

    2003-01-01

    textabstractFor successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno

  17. Financial markets analysis by probabilistic fuzzy modelling

    NARCIS (Netherlands)

    Berg, van den J.; Kaymak, U.; Bergh, van den W.M.

    2003-01-01

    For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS)

  18. Probabilistic Load Models for Simulating the Impact of Load Management

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Bak-Jensen, Birgitte; Chen, Zhe

    2009-01-01

    . It is concluded that the AR(12) model is favored with limited measurement data and that the joint-normal model may provide better results with a large data set. Both models can be applied in general to model load time series and used in time-sequential simulation of distribution system planning.......This paper analyzes a distribution system load time series through autocorrelation coefficient, power spectral density, probabilistic distribution and quantile value. Two probabilistic load models, i.e. the joint-normal model and the autoregressive model of order 12 (AR(12)), are proposed...... to simulate the impact of load management. The joint-normal model is superior in modeling the tail region of the hourly load distribution and implementing the change of hourly standard deviation. Whereas the AR(12) model requires much less parameter and is superior in modeling the autocorrelation...

  19. Probabilistic Modeling and Risk Assessment of Cable Icing

    DEFF Research Database (Denmark)

    Roldsgaard, Joan Hee

    This dissertation addresses the issues related to icing of structures with special emphasis on bridge cables. Cable supported bridges in cold climate suffers for ice accreting on the cables, this poses three different undesirable situations. Firstly the changed shape of the cable due to ice...... preliminary framework is modified for assessing the probability of occurrence of in-cloud and precipitation icing and its duration. Different probabilistic models are utilized for the representation of the meteorological variables and their appropriateness is evaluated both through goodness-of-fit tests...... are influencing the two icing mechanisms and their duration. The model is found to be more sensitive to changes in the discretization levels of the input variables. Thirdly the developed operational probabilistic framework for the assessment of the expected number of occurrences of ice/snow accretion on bridge...

  20. Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method.

    Science.gov (United States)

    Valentin, Jan B; Andreetta, Christian; Boomsma, Wouter; Bottaro, Sandro; Ferkinghoff-Borg, Jesper; Frellsen, Jes; Mardia, Kanti V; Tian, Pengfei; Hamelryck, Thomas

    2014-02-01

    We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length scale, which concern the dihedral angles in main chain and side chains, respectively. Conceptually, this constitutes a probabilistic and continuous alternative to the use of discrete fragment and rotamer libraries. The local model is combined with a nonlocal model that involves a small number of energy terms according to a physical force field, and some information on the overall secondary structure content. In this initial study we focus on the formulation of the joint model and the evaluation of the use of an energy vector as a descriptor of a protein's nonlocal structure; hence, we derive the parameters of the nonlocal model from the native structure without loss of generality. The local and nonlocal models are combined using the reference ratio method, which is a well-justified probabilistic construction. For evaluation, we use the resulting joint models to predict the structure of four proteins. The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. Copyright © 2013 Wiley Periodicals, Inc.

  1. A Methodology for Probabilistic Accident Management

    International Nuclear Information System (INIS)

    Munteanu, Ion; Aldemir, Tunc

    2003-01-01

    While techniques have been developed to tackle different tasks in accident management, there have been very few attempts to develop an on-line operator assistance tool for accident management and none that can be found in the literature that uses probabilistic arguments, which are important in today's licensing climate. The state/parameter estimation capability of the dynamic system doctor (DSD) approach is combined with the dynamic event-tree generation capability of the integrated safety assessment (ISA) methodology to address this issue. The DSD uses the cell-to-cell mapping technique for system representation that models the system evolution in terms of probability of transitions in time between sets of user-defined parameter/state variable magnitude intervals (cells) within a user-specified time interval (e.g., data sampling interval). The cell-to-cell transition probabilities are obtained from the given system model. The ISA follows the system dynamics in tree form and braches every time a setpoint for system/operator intervention is exceeded. The combined approach (a) can automatically account for uncertainties in the monitored system state, inputs, and modeling uncertainties through the appropriate choice of the cells, as well as providing a probabilistic measure to rank the likelihood of possible system states in view of these uncertainties; (b) allows flexibility in system representation; (c) yields the lower and upper bounds on the estimated values of state variables/parameters as well as their expected values; and (d) leads to fewer branchings in the dynamic event-tree generation. Using a simple but realistic pressurizer model, the potential use of the DSD-ISA methodology for on-line probabilistic accident management is illustrated

  2. Review of probabilistic models of the strength of composite materials

    International Nuclear Information System (INIS)

    Sutherland, L.S.; Guedes Soares, C.

    1997-01-01

    The available literature concerning probabilistic models describing the strength of composite materials has been reviewed to highlight the important aspects of this behaviour which will be of interest to the modelling and analysis of a complex system. The success with which these theories have been used to predict experimental results has been discussed. Since the brittle reinforcement phase largely controls the strength of composites, the probabilistic theories used to describe the strength of brittle materials, fibres and bundles of fibres have been detailed. The use of these theories to predict the strength of composite materials has been considered, along with further developments incorporating the damage accumulation observed in the failure of such materials. Probabilistic theories of the strength of short-fibre composites have been outlined. Emphasis has been placed throughout on straightforward engineering explanations of these theories and how they may be used, rather than providing comprehensive statistical descriptions

  3. Evaluation of seismic reliability of steel moment resisting frames rehabilitated by concentric braces with probabilistic models

    Directory of Open Access Journals (Sweden)

    Fateme Rezaei

    2017-08-01

    Full Text Available Probability of structure failure which has been designed by "deterministic methods" can be more than the one which has been designed in similar situation using probabilistic methods and models considering "uncertainties". The main purpose of this research was to evaluate the seismic reliability of steel moment resisting frames rehabilitated with concentric braces by probabilistic models. To do so, three-story and nine-story steel moment resisting frames were designed based on resistant criteria of Iranian code and then they were rehabilitated based on controlling drift limitations by concentric braces. Probability of frames failure was evaluated by probabilistic models of magnitude, location of earthquake, ground shaking intensity in the area of the structure, probabilistic model of building response (based on maximum lateral roof displacement and probabilistic methods. These frames were analyzed under subcrustal source by sampling probabilistic method "Risk Tools" (RT. Comparing the exceedance probability of building response curves (or selected points on it of the three-story and nine-story model frames (before and after rehabilitation, seismic response of rehabilitated frames, was reduced and their reliability was improved. Also the main effective variables in reducing the probability of frames failure were determined using sensitivity analysis by FORM probabilistic method. The most effective variables reducing the probability of frames failure are  in the magnitude model, ground shaking intensity model error and magnitude model error

  4. A decision network account of reasoning about other people's choices

    Science.gov (United States)

    Jern, Alan; Kemp, Charles

    2015-01-01

    The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. PMID:26010559

  5. Approximating methods for intractable probabilistic models: Applications in neuroscience

    DEFF Research Database (Denmark)

    Højen-Sørensen, Pedro

    2002-01-01

    This thesis investigates various methods for carrying out approximate inference in intractable probabilistic models. By capturing the relationships between random variables, the framework of graphical models hints at which sets of random variables pose a problem to the inferential step. The appro...

  6. A probabilistic maintenance model for diesel engines

    Science.gov (United States)

    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.

  7. Probabilistic models and machine learning in structural bioinformatics

    DEFF Research Database (Denmark)

    Hamelryck, Thomas

    2009-01-01

    . Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis...

  8. Probabilistic Role Models and the Guarded Fragment

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2004-01-01

    We propose a uniform semantic framework for interpreting probabilistic concept subsumption and probabilistic role quantification through statistical sampling distributions. This general semantic principle serves as the foundation for the development of a probabilistic version of the guarded fragm...... fragment of first-order logic. A characterization of equivalence in that logic in terms of bisimulations is given....

  9. Probabilistic role models and the guarded fragment

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2006-01-01

    We propose a uniform semantic framework for interpreting probabilistic concept subsumption and probabilistic role quantification through statistical sampling distributions. This general semantic principle serves as the foundation for the development of a probabilistic version of the guarded fragm...... fragment of first-order logic. A characterization of equivalence in that logic in terms of bisimulations is given....

  10. Probabilistic Compositional Models: solution of an equivalence problem

    Czech Academy of Sciences Publication Activity Database

    Kratochvíl, Václav

    2013-01-01

    Roč. 54, č. 5 (2013), s. 590-601 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Probabilistic model * Compositional model * Independence * Equivalence Subject RIV: BA - General Mathematics Impact factor: 1.977, year: 2013 http://library.utia.cas.cz/separaty/2013/MTR/kratochvil-0391079.pdf

  11. Metabolic level recognition of progesterone in dairy Holstein cows using probabilistic models

    Directory of Open Access Journals (Sweden)

    Ludmila N. Turino

    2014-05-01

    Full Text Available Administration of exogenous progesterone is widely used in hormonal protocols for estrous (resynchronization of dairy cattle without regarding pharmacological issues for dose calculation. This happens because it is difficult to estimate the metabolic level of progesterone for each individual cow before administration. In the present contribution, progesterone pharmacokinetics has been determined in lactating Holstein cows with different milk production yields. A Bayesian approach has been implemented to build two probabilistic progesterone pharmacokinetic models for high and low yield dairy cows. Such models are based on a one-compartment Hill structure. Posterior probabilistic models have been structurally set up and parametric probability density functions have been empirically estimated. Moreover, a global sensitivity analysis has been done to know sensitivity profile of each model. Finally, posterior probabilistic models have adequately recognized cow’s progesterone metabolic level in a validation set when Kullback-Leibler based indices were used. These results suggest that milk yield may be a good index for estimating pharmacokinetic level of progesterone.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    McClelland, James L

    2013-01-01

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

  14. A probabilistic model of RNA conformational space

    DEFF Research Database (Denmark)

    Frellsen, Jes; Moltke, Ida; Thiim, Martin

    2009-01-01

    , the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows...... conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

  15. Quantitative Analysis of Probabilistic Models of Software Product Lines with Statistical Model Checking

    DEFF Research Database (Denmark)

    ter Beek, Maurice H.; Legay, Axel; Lluch Lafuente, Alberto

    2015-01-01

    We investigate the suitability of statistical model checking techniques for analysing quantitative properties of software product line models with probabilistic aspects. For this purpose, we enrich the feature-oriented language FLAN with action rates, which specify the likelihood of exhibiting pa...

  16. On the logical specification of probabilistic transition models

    CSIR Research Space (South Africa)

    Rens, G

    2013-05-01

    Full Text Available We investigate the requirements for specifying the behaviors of actions in a stochastic domain. That is, we propose how to write sentences in a logical language to capture a model of probabilistic transitions due to the execution of actions of some...

  17. A Probabilistic Model of Meter Perception: Simulating Enculturation

    Directory of Open Access Journals (Sweden)

    Bastiaan van der Weij

    2017-05-01

    Full Text Available 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.

  18. A methodology for acquiring qualitative knowledge for probabilistic graphical models

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders L.

    2004-01-01

    We present a practical and general methodology that simplifies the task of acquiring and formulating qualitative knowledge for constructing probabilistic graphical models (PGMs). The methodology efficiently captures and communicates expert knowledge, and has significantly eased the model...

  19. Non-probabilistic defect assessment for structures with cracks based on interval model

    International Nuclear Information System (INIS)

    Dai, Qiao; Zhou, Changyu; Peng, Jian; Chen, Xiangwei; He, Xiaohua

    2013-01-01

    Highlights: • Non-probabilistic approach is introduced to defect assessment. • Definition and establishment of IFAC are put forward. • Determination of assessment rectangle is proposed. • Solution of non-probabilistic reliability index is presented. -- Abstract: Traditional defect assessment methods conservatively treat uncertainty of parameters as safety factors, while the probabilistic method is based on the clear understanding of detailed statistical information of parameters. In this paper, the non-probabilistic approach is introduced to the failure assessment diagram (FAD) to propose a non-probabilistic defect assessment method for structures with cracks. This novel defect assessment method contains three critical processes: establishment of the interval failure assessment curve (IFAC), determination of the assessment rectangle, and solution of the non-probabilistic reliability degree. Based on the interval theory, uncertain parameters such as crack sizes, material properties and loads are considered as interval variables. As a result, the failure assessment curve (FAC) will vary in a certain range, which is defined as IFAC. And the assessment point will vary within a rectangle zone which is defined as an assessment rectangle. Based on the interval model, the establishment of IFAC and the determination of the assessment rectangle are presented. Then according to the interval possibility degree method, the non-probabilistic reliability degree of IFAC can be determined. Meanwhile, in order to clearly introduce the non-probabilistic defect assessment method, a numerical example for the assessment of a pipe with crack is given. In addition, the assessment result of the proposed method is compared with that of the traditional probabilistic method, which confirms that this non-probabilistic defect assessment can reasonably resolve the practical problem with interval variables

  20. Non-probabilistic defect assessment for structures with cracks based on interval model

    Energy Technology Data Exchange (ETDEWEB)

    Dai, Qiao; Zhou, Changyu, E-mail: changyu_zhou@163.com; Peng, Jian; Chen, Xiangwei; He, Xiaohua

    2013-09-15

    Highlights: • Non-probabilistic approach is introduced to defect assessment. • Definition and establishment of IFAC are put forward. • Determination of assessment rectangle is proposed. • Solution of non-probabilistic reliability index is presented. -- Abstract: Traditional defect assessment methods conservatively treat uncertainty of parameters as safety factors, while the probabilistic method is based on the clear understanding of detailed statistical information of parameters. In this paper, the non-probabilistic approach is introduced to the failure assessment diagram (FAD) to propose a non-probabilistic defect assessment method for structures with cracks. This novel defect assessment method contains three critical processes: establishment of the interval failure assessment curve (IFAC), determination of the assessment rectangle, and solution of the non-probabilistic reliability degree. Based on the interval theory, uncertain parameters such as crack sizes, material properties and loads are considered as interval variables. As a result, the failure assessment curve (FAC) will vary in a certain range, which is defined as IFAC. And the assessment point will vary within a rectangle zone which is defined as an assessment rectangle. Based on the interval model, the establishment of IFAC and the determination of the assessment rectangle are presented. Then according to the interval possibility degree method, the non-probabilistic reliability degree of IFAC can be determined. Meanwhile, in order to clearly introduce the non-probabilistic defect assessment method, a numerical example for the assessment of a pipe with crack is given. In addition, the assessment result of the proposed method is compared with that of the traditional probabilistic method, which confirms that this non-probabilistic defect assessment can reasonably resolve the practical problem with interval variables.

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

    Science.gov (United States)

    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.

  2. Do probabilistic forecasts lead to better decisions?

    Directory of Open Access Journals (Sweden)

    M. H. Ramos

    2013-06-01

    Full Text Available The last decade has seen growing research in producing probabilistic hydro-meteorological forecasts and increasing their reliability. This followed the promise that, supplied with information about uncertainty, people would take better risk-based decisions. In recent years, therefore, research and operational developments have also started focusing attention on ways of communicating the probabilistic forecasts to decision-makers. Communicating probabilistic forecasts includes preparing tools and products for visualisation, but also requires understanding how decision-makers perceive and use uncertainty information in real time. At the EGU General Assembly 2012, we conducted a laboratory-style experiment in which several cases of flood forecasts and a choice of actions to take were presented as part of a game to participants, who acted as decision-makers. Answers were collected and analysed. In this paper, we present the results of this exercise and discuss if we indeed make better decisions on the basis of probabilistic forecasts.

  3. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.

    Science.gov (United States)

    Haefner, Ralf M; Berkes, Pietro; Fiser, József

    2016-05-04

    We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Sequential and simultaneous choices: testing the diet selection and sequential choice models.

    Science.gov (United States)

    Freidin, Esteban; Aw, Justine; Kacelnik, Alex

    2009-03-01

    We investigate simultaneous and sequential choices in starlings, using Charnov's Diet Choice Model (DCM) and Shapiro, Siller and Kacelnik's Sequential Choice Model (SCM) to integrate function and mechanism. During a training phase, starlings encountered one food-related option per trial (A, B or R) in random sequence and with equal probability. A and B delivered food rewards after programmed delays (shorter for A), while R ('rejection') moved directly to the next trial without reward. In this phase we measured latencies to respond. In a later, choice, phase, birds encountered the pairs A-B, A-R and B-R, the first implementing a simultaneous choice and the second and third sequential choices. The DCM predicts when R should be chosen to maximize intake rate, and SCM uses latencies of the training phase to predict choices between any pair of options in the choice phase. The predictions of both models coincided, and both successfully predicted the birds' preferences. The DCM does not deal with partial preferences, while the SCM does, and experimental results were strongly correlated to this model's predictions. We believe that the SCM may expose a very general mechanism of animal choice, and that its wider domain of success reflects the greater ecological significance of sequential over simultaneous choices.

  5. Optimization and evaluation of probabilistic-logic sequence models

    DEFF Research Database (Denmark)

    Christiansen, Henning; Lassen, Ole Torp

    to, in principle, Turing complete languages. In general, such models are computationally far to complex for direct use, so optimization by pruning and approximation are needed. % The first steps are made towards a methodology for optimizing such models by approximations using auxiliary models......Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...

  6. Choice of pesticide fate models

    International Nuclear Information System (INIS)

    Balderacchi, Matteo; Trevisan, Marco; Vischetti, Costantino

    2006-01-01

    The choice of a pesticide fate model at field scale is linked to the available input data. The article describes the available pesticide fate models at a field scale and the guidelines for the choice of the suitable model as function of the data input requested [it

  7. Probabilistic Modeling of the Fatigue Crack Growth Rate for Ni-base Alloy X-750

    International Nuclear Information System (INIS)

    Yoon, J.Y.; Nam, H.O.; Hwang, I.S.; Lee, T.H.

    2012-01-01

    Extending the operating life of existing nuclear power plants (NPP's) beyond 60 years. Many aging problems of passive components such as PWSCC, IASCC, FAC and Corrosion Fatigue; Safety analysis: Deterministic analysis + Probabilistic analysis; Many uncertainties of parameters or relationship in general probabilistic analysis such as probabilistic safety assessment (PSA); Bayesian inference: Decreasing uncertainties by updating unknown parameter; Ensuring the reliability of passive components (e.g. pipes) as well as active components (e.g. valve, pump) in NPP's; Developing probabilistic model for failures; Updating the fatigue crack growth rate (FCGR)

  8. Modeling decisions from experience: How models with a set of parameters for aggregate choices explain individual choices

    Directory of Open Access Journals (Sweden)

    Neha Sharma

    2017-10-01

    Full Text Available One of the paradigms (called “sampling paradigm” in judgment and decision-making involves decision-makers sample information before making a final consequential choice. In the sampling paradigm, certain computational models have been proposed where a set of single or distribution parameters is calibrated to the choice proportions of a group of participants (aggregate and hierarchical models. However, currently little is known on how aggregate and hierarchical models would account for choices made by individual participants in the sampling paradigm. In this paper, we test the ability of aggregate and hierarchical models to explain choices made by individual participants. Several models, Ensemble, Cumulative Prospect Theory (CPT, Best Estimation and Simulation Techniques (BEAST, Natural-Mean Heuristic (NMH, and Instance-Based Learning (IBL, had their parameters calibrated to individual choices in a large dataset involving the sampling paradigm. Later, these models were generalized to two large datasets in the sampling paradigm. Results revealed that the aggregate models (like CPT and IBL accounted for individual choices better than hierarchical models (like Ensemble and BEAST upon generalization to problems that were like those encountered during calibration. Furthermore, the CPT model, which relies on differential valuing of gains and losses, respectively, performed better than other models during calibration and generalization on datasets with similar set of problems. The IBL model, relying on recency and frequency of sampled information, and the NMH model, relying on frequency of sampled information, performed better than other models during generalization to a challenging dataset. Sequential analyses of results from different models showed how these models accounted for transitions from the last sample to final choice in human data. We highlight the implications of using aggregate and hierarchical models in explaining individual choices

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

    Science.gov (United States)

    Sukumaran, Jeet; Knowles, L Lacey

    2018-04-20

    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.

  10. A Probabilistic Typhoon Risk Model for Vietnam

    Science.gov (United States)

    Haseemkunju, A.; Smith, D. F.; Brolley, J. M.

    2017-12-01

    Annually, the coastal Provinces of low-lying Mekong River delta region in the southwest to the Red River Delta region in Northern Vietnam is exposed to severe wind and flood risk from landfalling typhoons. On average, about two to three tropical cyclones with a maximum sustained wind speed of >=34 knots make landfall along the Vietnam coast. Recently, Typhoon Wutip (2013) crossed Central Vietnam as a category 2 typhoon causing significant damage to properties. As tropical cyclone risk is expected to increase with increase in exposure and population growth along the coastal Provinces of Vietnam, insurance/reinsurance, and capital markets need a comprehensive probabilistic model to assess typhoon risk in Vietnam. In 2017, CoreLogic has expanded the geographical coverage of its basin-wide Western North Pacific probabilistic typhoon risk model to estimate the economic and insured losses from landfalling and by-passing tropical cyclones in Vietnam. The updated model is based on 71 years (1945-2015) of typhoon best-track data and 10,000 years of a basin-wide simulated stochastic tracks covering eight countries including Vietnam. The model is capable of estimating damage from wind, storm surge and rainfall flooding using vulnerability models, which relate typhoon hazard to building damageability. The hazard and loss models are validated against past historical typhoons affecting Vietnam. Notable typhoons causing significant damage in Vietnam are Lola (1993), Frankie (1996), Xangsane (2006), and Ketsana (2009). The central and northern coastal provinces of Vietnam are more vulnerable to wind and flood hazard, while typhoon risk in the southern provinces are relatively low.

  11. Probabilistic Data Modeling and Querying for Location-Based Data Warehouses

    DEFF Research Database (Denmark)

    Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach

    Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical and unc...

  12. Probabilistic Data Modeling and Querying for Location-Based Data Warehouses

    DEFF Research Database (Denmark)

    Timko, Igor; Dyreson, Curtis E.; Pedersen, Torben Bach

    2005-01-01

    Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical and unc...

  13. Failure probabilistic model of CNC lathes

    International Nuclear Information System (INIS)

    Wang Yiqiang; Jia Yazhou; Yu Junyi; Zheng Yuhua; Yi Shangfeng

    1999-01-01

    A field failure analysis of computerized numerical control (CNC) lathes is described. Field failure data was collected over a period of two years on approximately 80 CNC lathes. A coding system to code failure data was devised and a failure analysis data bank of CNC lathes was established. The failure position and subsystem, failure mode and cause were analyzed to indicate the weak subsystem of a CNC lathe. Also, failure probabilistic model of CNC lathes was analyzed by fuzzy multicriteria comprehensive evaluation

  14. Probabilistic transport models for fusion

    International Nuclear Information System (INIS)

    Milligen, B.Ph. van; Carreras, B.A.; Lynch, V.E.; Sanchez, R.

    2005-01-01

    A generalization of diffusive (Fickian) transport is considered, in which particle motion is described by probability distributions. We design a simple model that includes a critical mechanism to switch between two transport channels, and show that it exhibits various interesting characteristics, suggesting that the ideas of probabilistic transport might provide a framework for the description of a range of unusual transport phenomena observed in fusion plasmas. The model produces power degradation and profile consistency, as well as a scaling of the confinement time with system size reminiscent of the gyro-Bohm/Bohm scalings observed in fusion plasmas, and rapid propagation of disturbances. In the present work we show how this model may also produce on-axis peaking of the profiles with off-axis fuelling. It is important to note that the fluid limit of a simple model like this, characterized by two transport channels, does not correspond to the usual (Fickian) transport models commonly used for modelling transport in fusion plasmas, and behaves in a fundamentally different way. (author)

  15. Probabilistic modelling and analysis of stand-alone hybrid power systems

    International Nuclear Information System (INIS)

    Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

    2013-01-01

    As a part of the Hybrid Intelligent Algorithm, a model based on an ANN (artificial neural network) has been proposed in this paper to represent hybrid system behaviour considering the uncertainty related to wind speed and solar radiation, battery bank lifetime, and fuel prices. The Hybrid Intelligent Algorithm suggests a combination of probabilistic analysis based on a Monte Carlo simulation approach and artificial neural network training embedded in a genetic algorithm optimisation model. The installation of a typical hybrid system was analysed. Probabilistic analysis was used to generate an input–output dataset of 519 samples that was later used to train the ANNs to reduce the computational effort required. The generalisation ability of the ANNs was measured in terms of RMSE (Root Mean Square Error), MBE (Mean Bias Error), MAE (Mean Absolute Error), and R-squared estimators using another data group of 200 samples. The results obtained from the estimation of the expected energy not supplied, the probability of a determined reliability level, and the estimation of expected value of net present cost show that the presented model is able to represent the main characteristics of a typical hybrid power system under uncertain operating conditions. - Highlights: • This paper presents a probabilistic model for stand-alone hybrid power system. • The model considers the main sources of uncertainty related to renewable resources. • The Hybrid Intelligent Algorithm has been applied to represent hybrid system behaviour. • The installation of a typical hybrid system was analysed. • The results obtained from the study case validate the presented model

  16. The Effects of Heuristics and Apophenia on Probabilistic Choice.

    Science.gov (United States)

    Ellerby, Zack W; Tunney, Richard J

    2017-01-01

    Given a repeated choice between two or more options with independent and identically distributed reward probabilities, overall pay-offs can be maximized by the exclusive selection of the option with the greatest likelihood of reward. The tendency to match response proportions to reward contingencies is suboptimal. Nevertheless, this behaviour is well documented. A number of explanatory accounts have been proposed for probability matching. These include failed pattern matching, driven by apophenia, and a heuristic-driven response that can be overruled with sufficient deliberation. We report two experiments that were designed to test the relative effects on choice behaviour of both an intuitive versus strategic approach to the task and belief that there was a predictable pattern in the reward sequence, through a combination of both direct experimental manipulation and post-experimental self-report. Mediation analysis was used to model the pathways of effects. Neither of two attempted experimental manipulations of apophenia, nor self-reported levels of apophenia, had a significant effect on proportions of maximizing choices. However, the use of strategy over intuition proved a consistent predictor of maximizing, across all experimental conditions. A parallel analysis was conducted to assess the effect of controlling for individual variance in perceptions of reward contingencies. Although this analysis suggested that apophenia did increase probability matching in the standard task preparation, this effect was found to result from an unforeseen relationship between self-reported apophenia and perceived reward probabilities. A Win-Stay Lose-Shift (WSLS ) analysis indicated no reliable relationship between WSLS and either intuition or strategy use.

  17. Probabilistic Modelling of Timber Material Properties

    DEFF Research Database (Denmark)

    Nielsen, Michael Havbro Faber; Köhler, Jochen; Sørensen, John Dalsgaard

    2001-01-01

    The probabilistic modeling of timber material characteristics is considered with special emphasis to the modeling of the effect of different quality control and selection procedures used as means for grading of timber in the production line. It is shown how statistical models may be established...... on the basis of the same type of information which is normally collected as a part of the quality control procedures and furthermore, how the efficiency of different control procedures may be compared. The tail behavior of the probability distributions of timber material characteristics play an important role...... such that they may readily be applied in structural reliability analysis and the format appears to be appropriate for codification purposes of quality control and selection for grading procedures...

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

    Science.gov (United States)

    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.

  19. Probabilistic Modelling of Information Propagation in Wireless Mobile Ad-Hoc Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Hansen, Martin Bøgsted; Schwefel, Hans-Peter

    2005-01-01

    In this paper the dynamics of broadcasting wireless ad-hoc networks is studied through probabilistic modelling. A randomized transmission discipline is assumed in accordance with existing MAC definitions such as WLAN with Decentralized Coordination or IEEE-802.15.4. Message reception is assumed...... to be governed by node power-down policies and is equivalently assumed to be randomized. Altogether randomization facilitates a probabilistic model in the shape of an integro-differential equation governing the propagation of information, where brownian node mobility may be accounted for by including an extra...... diffusion term. The established model is analyzed for transient behaviour and a travelling wave solution facilitates expressions for propagation speed as well as parametrized analysis of network reliability and node power consumption. Applications of the developed models for node localization and network...

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

    Energy Technology Data Exchange (ETDEWEB)

    Yao, Yin; Gao, Wenzhong; Momoh, James; Muljadi, Eduard

    2016-02-11

    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 microgrid 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.

  1. A note on probabilistic models over strings: the linear algebra approach.

    Science.gov (United States)

    Bouchard-Côté, Alexandre

    2013-12-01

    Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

  2. Fully probabilistic design of hierarchical Bayesian models

    Czech Academy of Sciences Publication Activity Database

    Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine

    2016-01-01

    Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf

  3. The Gain-Loss Model: A Probabilistic Skill Multimap Model for Assessing Learning Processes

    Science.gov (United States)

    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…

  4. Probabilistic Survivability Versus Time Modeling

    Science.gov (United States)

    Joyner, James J., Sr.

    2016-01-01

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

  5. A constraints-induced model of park choice

    NARCIS (Netherlands)

    Stemerding, M.P.; Oppewal, H.; Timmermans, H.J.P.

    1999-01-01

    Conjoint choice models have been used widely in the consumer-choice literature as an approach to measure and predict consumer-choice behavior. These models typically assume that consumer preferences and choice rules are independent from any constraints that might impact the behavior of interest.

  6. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

    Science.gov (United States)

    David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

    2017-04-01

    This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  7. Paradoxical choice in rats: Subjective valuation and mechanism of choice.

    Science.gov (United States)

    Ojeda, Andrés; Murphy, Robin A; Kacelnik, Alex

    2018-07-01

    Decision-makers benefit from information only when they can use it to guide behavior. However, recent experiments found that pigeons and starlings value information that they cannot use. Here we show that this paradox is also present in rats, and explore the underlying decision process. Subjects chose between two options that delivered food probabilistically after a fixed delay. In one option ("info"), outcomes (food/no-food) were signaled immediately after choice, whereas in the alternative ("non-info") the outcome was uncertain until the delay lapsed. Rats sacrificed up to 20% potential rewards by preferring the info option, but reversed preference when the cost was 60%. This reversal contrasts with the results found with pigeons and starlings and may reflect species' differences worth of further investigation. Results are consistent with predictions of the Sequential Choice Model (SCM), that proposes that choices are driven by the mechanisms that control action in sequential encounters. As expected from the SCM, latencies to respond in single-option trials predicted preferences in choice trials, and latencies in choice trials were the same or shorter than in single-option trials. We argue that the congruence of results in distant vertebrates probably reflects evolved adaptations to shared fundamental challenges in nature, and that the apparently paradoxical overvaluing of information is not sub-optimal as has been claimed, even though its functional significance is not yet understood. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. When to conduct probabilistic linkage vs. deterministic linkage? A simulation study.

    Science.gov (United States)

    Zhu, Ying; Matsuyama, Yutaka; Ohashi, Yasuo; Setoguchi, Soko

    2015-08-01

    When unique identifiers are unavailable, successful record linkage depends greatly on data quality and types of variables available. While probabilistic linkage theoretically captures more true matches than deterministic linkage by allowing imperfection in identifiers, studies have shown inconclusive results likely due to variations in data quality, implementation of linkage methodology and validation method. The simulation study aimed to understand data characteristics that affect the performance of probabilistic vs. deterministic linkage. We created ninety-six scenarios that represent real-life situations using non-unique identifiers. We systematically introduced a range of discriminative power, rate of missing and error, and file size to increase linkage patterns and difficulties. We assessed the performance difference of linkage methods using standard validity measures and computation time. Across scenarios, deterministic linkage showed advantage in PPV while probabilistic linkage showed advantage in sensitivity. Probabilistic linkage uniformly outperformed deterministic linkage as the former generated linkages with better trade-off between sensitivity and PPV regardless of data quality. However, with low rate of missing and error in data, deterministic linkage performed not significantly worse. The implementation of deterministic linkage in SAS took less than 1min, and probabilistic linkage took 2min to 2h depending on file size. Our simulation study demonstrated that the intrinsic rate of missing and error of linkage variables was key to choosing between linkage methods. In general, probabilistic linkage was a better choice, but for exceptionally good quality data (<5% error), deterministic linkage was a more resource efficient choice. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. From sub-source to source: Interpreting results of biological trace investigations using probabilistic models

    NARCIS (Netherlands)

    Oosterman, W.T.; Kokshoorn, B.; Maaskant-van Wijk, P.A.; de Zoete, J.

    2015-01-01

    The current method of reporting a putative cell type is based on a non-probabilistic assessment of test results by the forensic practitioner. Additionally, the association between donor and cell type in mixed DNA profiles can be exceedingly complex. We present a probabilistic model for

  10. Probabilistic model for sterilization of food

    International Nuclear Information System (INIS)

    Chepurko, V.V.; Malinovskij, O.V.

    1986-01-01

    The probabilistic model for radiation sterilization is proposed based on the followng suppositions: (1) initial contamination of a volume unit of the sterilized product m is described by the distribution of the probabilities q(m), (2) inactivation of the population from m of microorganisms is approximated by Bernoulli test scheme, and (3) contamination of unit of the sterilized product is independent. The possibility of approximation q(m) by Poisson distribution is demonstrated. The diagrams are presented permitting to evaluate the dose which provides the defined reliability of sterilization of food for chicken-gnotobionts

  11. Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

    Science.gov (United States)

    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.

  12. A probabilistic model for US nuclear power construction times

    International Nuclear Information System (INIS)

    Shash, A.A.H.

    1988-01-01

    Construction time for nuclear power plants is an important element in planning for resources to meet future load demands. Analysis of actual versus estimated construction times for past US nuclear power plants indicates that utilities have continuously underestimated their power plants' construction durations. The analysis also indicates that the actual average construction time has been increasing upward, and the actual durations of power plants permitted to construct in the same year varied substantially. This study presents two probabilistic models for nuclear power construction time for use by the nuclear industry as estimating tool. The study also presents a detailed explanation of the factors that are responsible for increasing and varying nuclear power construction times. Observations on 91 complete nuclear units were involved in three interdependent analyses in the process of explanation and derivation of the probabilistic models. The historical data was first utilized in the data envelopment analysis (DEA) for the purpose of obtaining frontier index measures for project management achievement in building nuclear power plants

  13. Probabilistic risk assessment modeling of digital instrumentation and control systems using two dynamic methodologies

    Energy Technology Data Exchange (ETDEWEB)

    Aldemir, T., E-mail: aldemir.1@osu.ed [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Guarro, S. [ASCA, Inc., 1720 S. Catalina Avenue, Suite 220, Redondo Beach, CA 90277-5501 (United States); Mandelli, D. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Kirschenbaum, J. [Ohio State University, Department of Computer Science and Engineering, Columbus, OH 43210 (United States); Mangan, L.A. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Bucci, P. [Ohio State University, Department of Computer Science and Engineering, Columbus, OH 43210 (United States); Yau, M. [ASCA, Inc., 1720 S. Catalina Avenue, Suite 220, Redondo Beach, CA 90277-5501 (United States); Ekici, E. [Ohio State University, Department of Electrical and Computer Engineering, Columbus, OH 43210 (United States); Miller, D.W.; Sun, X. [Ohio State University, Nuclear Engineering Program, Columbus, OH 43210 (United States); Arndt, S.A. [U.S. Nuclear Regulatory Commission, Washington, DC 20555-0001 (United States)

    2010-10-15

    The Markov/cell-to-cell mapping technique (CCMT) and the dynamic flowgraph methodology (DFM) are two system logic modeling methodologies that have been proposed to address the dynamic characteristics of digital instrumentation and control (I and C) systems and provide risk-analytical capabilities that supplement those provided by traditional probabilistic risk assessment (PRA) techniques for nuclear power plants. Both methodologies utilize a discrete state, multi-valued logic representation of the digital I and C system. For probabilistic quantification purposes, both techniques require the estimation of the probabilities of basic system failure modes, including digital I and C software failure modes, that appear in the prime implicants identified as contributors to a given system event of interest. As in any other system modeling process, the accuracy and predictive value of the models produced by the two techniques, depend not only on the intrinsic features of the modeling paradigm, but also and to a considerable extent on information and knowledge available to the analyst, concerning the system behavior and operation rules under normal and off-nominal conditions, and the associated controlled/monitored process dynamics. The application of the two methodologies is illustrated using a digital feedwater control system (DFWCS) similar to that of an operating pressurized water reactor. This application was carried out to demonstrate how the use of either technique, or both, can facilitate the updating of an existing nuclear power plant PRA model following an upgrade of the instrumentation and control system from analog to digital. Because of scope limitations, the focus of the demonstration of the methodologies was intentionally limited to aspects of digital I and C system behavior for which probabilistic data was on hand or could be generated within the existing project bounds of time and resources. The data used in the probabilistic quantification portion of the

  14. Probabilistic disaggregation model with application to natural hazard risk assessment of portfolios

    DEFF Research Database (Denmark)

    Custer, Rocco; Nishijima, Kazuyoshi

    In natural hazard risk assessment, a resolution mismatch between hazard data and aggregated exposure data is often observed. A possible solution to this issue is the disaggregation of exposure data to match the spatial resolution of hazard data. Disaggregation models available in literature...... disaggregation model that considers the uncertainty in the disaggregation, taking basis in the scaled Dirichlet distribution. The proposed probabilistic disaggregation model is applied to a portfolio of residential buildings in the Canton Bern, Switzerland, subject to flood risk. Thereby, the model is verified...... are usually deterministic and make use of auxiliary indicator, such as land cover, to spatially distribute exposures. As the dependence between auxiliary indicator and disaggregated number of exposures is generally imperfect, uncertainty arises in disaggregation. This paper therefore proposes a probabilistic...

  15. A Practical Probabilistic Graphical Modeling Tool for Weighing ...

    Science.gov (United States)

    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 ecological risk determinations. Probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure wasdeveloped for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors. We provide a flexible Bayesian network structure for weighing and integrating lines of evidence for ecological risk determinations

  16. An Empirical Study of Efficiency and Accuracy of Probabilistic Graphical Models

    DEFF Research Database (Denmark)

    Nielsen, Jens Dalgaard; Jaeger, Manfred

    2006-01-01

    In this paper we compare Na\\ii ve Bayes (NB) models, general Bayes Net (BN) models and Probabilistic Decision Graph (PDG) models w.r.t. accuracy and efficiency. As the basis for our analysis we use graphs of size vs. likelihood that show the theoretical capabilities of the models. We also measure...

  17. Probabilistic logics and probabilistic networks

    CERN Document Server

    Haenni, Rolf; Wheeler, Gregory; Williamson, Jon; Andrews, Jill

    2014-01-01

    Probabilistic Logic and Probabilistic Networks presents a groundbreaking framework within which various approaches to probabilistic logic naturally fit. Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.

  18. A study of probabilistic fatigue crack propagation models in Mg Al Zn alloys under different specimen thickness conditions by using the residual of a random variable

    International Nuclear Information System (INIS)

    Choi, Seon Soon

    2012-01-01

    The primary aim of this paper was to evaluate several probabilistic fatigue crack propagation models using the residual of a random variable, and to present the model fit for probabilistic fatigue behavior in Mg Al Zn alloys. The proposed probabilistic models are the probabilistic Paris Erdogan model, probabilistic Walker model, probabilistic Forman model, and probabilistic modified Forman models. These models were prepared by applying a random variable to the empirical fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models for describing fatigue crack propagation models with these names. The best models vor describing fatigue crack propagation behavior in Mg Al Zn alloys were generally the probabilistic Paris Erdogan and probabilistic Walker models. The probabilistic Forman model was a good model only for a specimen with a thickness of 9.45mm

  19. Pipe fracture evaluations for leak-rate detection: Probabilistic models

    International Nuclear Information System (INIS)

    Rahman, S.; Wilkowski, G.; Ghadiali, N.

    1993-01-01

    This is the second in series of three papers generated from studies on nuclear pipe fracture evaluations for leak-rate detection. This paper focuses on the development of novel probabilistic models for stochastic performance evaluation of degraded nuclear piping systems. It was accomplished here in three distinct stages. First, a statistical analysis was conducted to characterize various input variables for thermo-hydraulic analysis and elastic-plastic fracture mechanics, such as material properties of pipe, crack morphology variables, and location of cracks found in nuclear piping. Second, a new stochastic model was developed to evaluate performance of degraded piping systems. It is based on accurate deterministic models for thermo-hydraulic and fracture mechanics analyses described in the first paper, statistical characterization of various input variables, and state-of-the-art methods of modem structural reliability theory. From this model. the conditional probability of failure as a function of leak-rate detection capability of the piping systems can be predicted. Third, a numerical example was presented to illustrate the proposed model for piping reliability analyses. Results clearly showed that the model provides satisfactory estimates of conditional failure probability with much less computational effort when compared with those obtained from Monte Carlo simulation. The probabilistic model developed in this paper will be applied to various piping in boiling water reactor and pressurized water reactor plants for leak-rate detection applications

  20. Quantification of Wave Model Uncertainties Used for Probabilistic Reliability Assessments of Wave Energy Converters

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

    2015-01-01

    Wave models used for site assessments are subjected to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Four different wave models are considered, and validation...... data are collected from published scientific research. The bias and the root-mean-square error, as well as the scatter index, are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example, this paper presents how the quantified...... uncertainties can be implemented in probabilistic reliability assessments....

  1. Determination of Wave Model Uncertainties used for Probabilistic Reliability Assessments of Wave Energy Devices

    DEFF Research Database (Denmark)

    Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

    2014-01-01

    Wave models used for site assessments are subject to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Considered are four different wave models and validation...... data is collected from published scientific research. The bias, the root-mean-square error as well as the scatter index are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example it is shown how the estimated uncertainties can...... be implemented in probabilistic reliability assessments....

  2. Parameter Estimation for Thurstone Choice Models

    Energy Technology Data Exchange (ETDEWEB)

    Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-04-24

    We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

  3. Towards port sustainability through probabilistic models: Bayesian networks

    Directory of Open Access Journals (Sweden)

    B. Molina

    2018-04-01

    Full Text Available It is necessary that a manager of an infrastructure knows relations between variables. Using Bayesian networks, variables can be classified, predicted and diagnosed, being able to estimate posterior probability of the unknown ones based on known ones. The proposed methodology has generated a database with port variables, which have been classified as economic, social, environmental and institutional, as addressed in of smart ports studies made in all Spanish Port System. Network has been developed using an acyclic directed graph, which have let us know relationships in terms of parents and sons. In probabilistic terms, it can be concluded from the constructed network that the most decisive variables for port sustainability are those that are part of the institutional dimension. It has been concluded that Bayesian networks allow modeling uncertainty probabilistically even when the number of variables is high as it occurs in port planning and exploitation.

  4. Probabilistic systems coalgebraically: A survey

    Science.gov (United States)

    Sokolova, Ana

    2011-01-01

    We survey the work on both discrete and continuous-space probabilistic systems as coalgebras, starting with how probabilistic systems are modeled as coalgebras and followed by a discussion of their bisimilarity and behavioral equivalence, mentioning results that follow from the coalgebraic treatment of probabilistic systems. It is interesting to note that, for different reasons, for both discrete and continuous probabilistic systems it may be more convenient to work with behavioral equivalence than with bisimilarity. PMID:21998490

  5. Probabilistic Programming (Invited Talk)

    OpenAIRE

    Yang, Hongseok

    2017-01-01

    Probabilistic programming refers to the idea of using standard programming constructs for specifying probabilistic models from machine learning and statistics, and employing generic inference algorithms for answering various queries on these models, such as posterior inference and estimation of model evidence. Although this idea itself is not new and was, in fact, explored by several programming-language and statistics researchers in the early 2000, it is only in the last few years that proba...

  6. Probabilistic Modelling of Fatigue Life of Composite Laminates Using Bayesian Inference

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Kiureghian, Armen Der

    2014-01-01

    A probabilistic model for estimating the fatigue life of laminated composite plates subjected to constant-amplitude or variable-amplitude loading is developed. The model is based on lamina-level input data, making it possible to predict fatigue properties for a wide range of laminate configuratio...

  7. Global/local methods for probabilistic structural analysis

    Science.gov (United States)

    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.

  8. Consideration of aging in probabilistic safety assessment

    International Nuclear Information System (INIS)

    Titina, B.; Cepin, M.

    2007-01-01

    Probabilistic safety assessment is a standardised tool for assessment of safety of nuclear power plants. It is a complement to the safety analyses. Standard probabilistic models of safety equipment assume component failure rate as a constant. Ageing of systems, structures and components can theoretically be included in new age-dependent probabilistic safety assessment, which generally causes the failure rate to be a function of age. New age-dependent probabilistic safety assessment models, which offer explicit calculation of the ageing effects, are developed. Several groups of components are considered which require their unique models: e.g. operating components e.g. stand-by components. The developed models on the component level are inserted into the models of the probabilistic safety assessment in order that the ageing effects are evaluated for complete systems. The preliminary results show that the lack of necessary data for consideration of ageing causes highly uncertain models and consequently the results. (author)

  9. Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

    NARCIS (Netherlands)

    Engström, Kerstin; Olin, Stefan; Rounsevell, Mark D A; Brogaard, Sara; Van Vuuren, Detlef P.; Alexander, Peter; Murray-Rust, Dave; Arneth, Almut

    2016-01-01

    We present a modelling framework to simulate probabilistic futures of global cropland areas that are conditional on the SSP (shared socio-economic pathway) scenarios. Simulations are based on the Parsimonious Land Use Model (PLUM) linked with the global dynamic vegetation model LPJ-GUESS

  10. Multiple sequential failure model: A probabilistic approach to quantifying human error dependency

    International Nuclear Information System (INIS)

    Samanta

    1985-01-01

    This paper rpesents a probabilistic approach to quantifying human error dependency when multiple tasks are performed. Dependent human failures are dominant contributors to risks from nuclear power plants. An overview of the Multiple Sequential Failure (MSF) model developed and its use in probabilistic risk assessments (PRAs) depending on the available data are discussed. A small-scale psychological experiment was conducted on the nature of human dependency and the interpretation of the experimental data by the MSF model show remarkable accommodation of the dependent failure data. The model, which provides an unique method for quantification of dependent failures in human reliability analysis, can be used in conjunction with any of the general methods currently used for performing the human reliability aspect in PRAs

  11. Can model weighting improve probabilistic projections of climate change?

    Energy Technology Data Exchange (ETDEWEB)

    Raeisaenen, Jouni; Ylhaeisi, Jussi S. [Department of Physics, P.O. Box 48, University of Helsinki (Finland)

    2012-10-15

    Recently, Raeisaenen and co-authors proposed a weighting scheme in which the relationship between observable climate and climate change within a multi-model ensemble determines to what extent agreement with observations affects model weights in climate change projection. Within the Third Coupled Model Intercomparison Project (CMIP3) dataset, this scheme slightly improved the cross-validated accuracy of deterministic projections of temperature change. Here the same scheme is applied to probabilistic temperature change projection, under the strong limiting assumption that the CMIP3 ensemble spans the actual modeling uncertainty. Cross-validation suggests that probabilistic temperature change projections may also be improved by this weighting scheme. However, the improvement relative to uniform weighting is smaller in the tail-sensitive logarithmic score than in the continuous ranked probability score. The impact of the weighting on projection of real-world twenty-first century temperature change is modest in most parts of the world. However, in some areas mainly over the high-latitude oceans, the mean of the distribution is substantially changed and/or the distribution is considerably narrowed. The weights of individual models vary strongly with location, so that a model that receives nearly zero weight in some area may still get a large weight elsewhere. Although the details of this variation are method-specific, it suggests that the relative strengths of different models may be difficult to harness by weighting schemes that use spatially uniform model weights. (orig.)

  12. Probabilistic model of random uncertainties in structural dynamics for mis-tuned bladed disks; Modele probabiliste des incertitudes en dynamique des structures pour le desaccordage des roues aubagees

    Energy Technology Data Exchange (ETDEWEB)

    Capiez-Lernout, E.; Soize, Ch. [Universite de Marne la Vallee, Lab. de Mecanique, 77 (France)

    2003-10-01

    The mis-tuning of blades is frequently the cause of spatial localizations for the dynamic forced response in turbomachinery industry. The random character of mis-tuning requires the construction of probabilistic models of random uncertainties. A usual parametric probabilistic description considers the mis-tuning through the Young modulus of each blade. This model consists in mis-tuning blade eigenfrequencies, assuming the blade modal shapes unchanged. Recently a new approach known as a non-parametric model of random uncertainties has been introduced for modelling random uncertainties in elasto-dynamics. This paper proposes the construction of a non-parametric model which is coherent with all the uncertainties which characterize mis-tuning. As mis-tuning is a phenomenon which is independent from one blade to another one, the structure is considered as an assemblage of substructures. The mean reduced matrix model required by the non-parametric approach is thus constructed by dynamic sub-structuring. A comparative approach is also needed to study the influence of the non-parametric approach for a usual parametric model adapted to mis-tuning. A numerical example is presented. (authors)

  13. Developing Pavement Distress Deterioration Models for Pavement Management System Using Markovian Probabilistic Process

    Directory of Open Access Journals (Sweden)

    Promothes Saha

    2017-01-01

    Full Text Available In the state of Colorado, the Colorado Department of Transportation (CDOT utilizes their pavement management system (PMS to manage approximately 9,100 miles of interstate, highways, and low-volume roads. Three types of deterioration models are currently being used in the existing PMS: site-specific, family, and expert opinion curves. These curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to traffic and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for five distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated that longitudinal, fatigue, and rut indices had very slow deterioration over time, whereas transverse and ride indices showed faster deterioration. The developed deterioration models had the coefficient of determination (R2 above 0.84. As probabilistic models provide more accurate results, it is recommended that these models be used as the family curves in the CDOT PMS for low-volume roads.

  14. Modeling the dynamics of choice.

    Science.gov (United States)

    Baum, William M; Davison, Michael

    2009-06-01

    A simple linear-operator model both describes and predicts the dynamics of choice that may underlie the matching relation. We measured inter-food choice within components of a schedule that presented seven different pairs of concurrent variable-interval schedules for 12 food deliveries each with no signals indicating which pair was in force. This measure of local choice was accurately described and predicted as obtained reinforcer sequences shifted it to favor one alternative or the other. The effect of a changeover delay was reflected in one parameter, the asymptote, whereas the effect of a difference in overall rate of food delivery was reflected in the other parameter, rate of approach to the asymptote. The model takes choice as a primary dependent variable, not derived by comparison between alternatives-an approach that agrees with the molar view of behaviour.

  15. Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.

    Science.gov (United States)

    Tan, Ping; Tan, Guan-Zheng; Cai, Zi-Xing; Sa, Wei-Ping; Zou, Yi-Qun

    2017-01-01

    Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.

  16. Probabilistic safety assessment

    International Nuclear Information System (INIS)

    Hoertner, H.; Schuetz, B.

    1982-09-01

    For the purpose of assessing applicability and informativeness on risk-analysis methods in licencing procedures under atomic law, the choice of instruments for probabilistic analysis, the problems in and experience gained in their application, and the discussion of safety goals with respect to such instruments are of paramount significance. Naturally, such a complex field can only be dealt with step by step, making contribution relative to specific problems. The report on hand shows the essentials of a 'stocktaking' of systems relability studies in the licencing procedure under atomic law and of an American report (NUREG-0739) on 'Quantitative Safety Goals'. (orig.) [de

  17. Hybrid Compensatory-Noncompensatory Choice Sets in Semicompensatory Models

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Bekhor, Shlomo; Shiftan, Yoram

    2013-01-01

    Semicompensatory models represent a choice process consisting of an elimination-based choice set formation on satisfaction of criterion thresholds and a utility-based choice. Current semicompensatory models assume a purely noncompensatory choice set formation and therefore do not support multinom...

  18. Probabilistic Modeling of Graded Timber Material Properties

    DEFF Research Database (Denmark)

    Faber, M. H.; Köhler, J.; Sørensen, John Dalsgaard

    2004-01-01

    The probabilistic modeling of timber material characteristics is considered with special emphasis to the modeling of the effect of different quality control and selection procedures used as means for quality grading in the production line. It is shown how statistical models may be established...... on the basis of the same type of information which is normally collected as a part of the quality control procedures and furthermore, how the efficiency of different control procedures may be quantified and compared. The tail behavior of the probability distributions of timber material characteristics plays...... such that they may readily be applied in structural reliability analysis and their format appears to be appropriate for codification purposes of quality control and selection for grading procedures....

  19. Modeling and analysis of cell membrane systems with probabilistic model checking

    Science.gov (United States)

    2011-01-01

    Background Recently there has been a growing interest in the application of Probabilistic Model Checking (PMC) for the formal specification of biological systems. PMC is able to exhaustively explore all states of a stochastic model and can provide valuable insight into its behavior which are more difficult to see using only traditional methods for system analysis such as deterministic and stochastic simulation. In this work we propose a stochastic modeling for the description and analysis of sodium-potassium exchange pump. The sodium-potassium pump is a membrane transport system presents in all animal cell and capable of moving sodium and potassium ions against their concentration gradient. Results We present a quantitative formal specification of the pump mechanism in the PRISM language, taking into consideration a discrete chemistry approach and the Law of Mass Action aspects. We also present an analysis of the system using quantitative properties in order to verify the pump reversibility and understand the pump behavior using trend labels for the transition rates of the pump reactions. Conclusions Probabilistic model checking can be used along with other well established approaches such as simulation and differential equations to better understand pump behavior. Using PMC we can determine if specific events happen such as the potassium outside the cell ends in all model traces. We can also have a more detailed perspective on its behavior such as determining its reversibility and why its normal operation becomes slow over time. This knowledge can be used to direct experimental research and make it more efficient, leading to faster and more accurate scientific discoveries. PMID:22369714

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

    Science.gov (United States)

    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.

  1. A Novel TRM Calculation Method by Probabilistic Concept

    Science.gov (United States)

    Audomvongseree, Kulyos; Yokoyama, Akihiko; Verma, Suresh Chand; Nakachi, Yoshiki

    In a new competitive environment, it becomes possible for the third party to access a transmission facility. From this structure, to efficiently manage the utilization of the transmission network, a new definition about Available Transfer Capability (ATC) has been proposed. According to the North American ElectricReliability Council (NERC)’s definition, ATC depends on several parameters, i. e. Total Transfer Capability (TTC), Transmission Reliability Margin (TRM), and Capacity Benefit Margin (CBM). This paper is focused on the calculation of TRM which is one of the security margin reserved for any uncertainty of system conditions. The TRM calculation by probabilistic method is proposed in this paper. Based on the modeling of load forecast error and error in transmission line limitation, various cases of transmission transfer capability and its related probabilistic nature can be calculated. By consideration of the proposed concept of risk analysis, the appropriate required amount of TRM can be obtained. The objective of this research is to provide realistic information on the actual ability of the network which may be an alternative choice for system operators to make an appropriate decision in the competitive market. The advantages of the proposed method are illustrated by application to the IEEJ-WEST10 model system.

  2. Bayesian uncertainty analyses of probabilistic risk models

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1989-01-01

    Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

  3. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  4. Probabilistic models for steel corrosion loss and pitting of marine infrastructure

    International Nuclear Information System (INIS)

    Melchers, R.E.; Jeffrey, R.J.

    2008-01-01

    With the increasing emphasis on attempting to retain in service ageing infrastructure models for the description and prediction of corrosion losses and for maximum pit depth are of increasing interest. In most cases assessment and prediction will be done in a probabilistic risk assessment framework and this then requires probabilistic corrosion models. Recently, novel models for corrosion loss and maximum pit depth under marine immersion conditions have been developed. The models show that both corrosion loss and pit depth progress in a non-linear fashion with increased exposure time and do so in a non-monotonic manner as a result of the controlling corrosion process changing from oxidation to being influenced by bacterial action. For engineers the importance of this lies in the fact that conventional 'corrosion rates' have no validity, particularly for the long-term corrosion effects as relevant to deteriorated infrastructure. The models are consistent with corrosion science principles as well as current understanding of the considerable influence of bacterial processes on corrosion loss and pitting. The considerable practical implications of this are described

  5. Modeling Intercity Mode Choice and Airport Choice in the United States

    OpenAIRE

    Ashiabor, Senanu Y.

    2007-01-01

    The aim of this study was to develop a framework to model travel choice behavior in order to estimate intercity travel demand at nation-level in the United States. Nested and mixed logit models were developed to study national-level intercity transportation in the United States. A separate General Aviation airport choice model to estimates General Aviation person-trips and number of aircraft operations though more than 3000 airports was also developed. The combination of the General Aviati...

  6. Meta-analysis of choice set generation effects on route choice model estimates and predictions

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo

    2012-01-01

    are applied for model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach allows synthesizing the effect of judgments......Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior...... modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation...

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

    Science.gov (United States)

    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.

  8. A Probabilistic Graphical Model to Detect Chromosomal Domains

    Science.gov (United States)

    Heermann, Dieter; Hofmann, Andreas; Weber, Eva

    To understand the nature of a cell, one needs to understand the structure of its genome. For this purpose, experimental techniques such as Hi-C detecting chromosomal contacts are used to probe the three-dimensional genomic structure. These experiments yield topological information, consistently showing a hierarchical subdivision of the genome into self-interacting domains across many organisms. Current methods for detecting these domains using the Hi-C contact matrix, i.e. a doubly-stochastic matrix, are mostly based on the assumption that the domains are distinct, thus non-overlapping. For overcoming this simplification and for being able to unravel a possible nested domain structure, we developed a probabilistic graphical model that makes no a priori assumptions on the domain structure. Within this approach, the Hi-C contact matrix is analyzed using an Ising like probabilistic graphical model whose coupling constant is proportional to each lattice point (entry in the contact matrix). The results show clear boundaries between identified domains and the background. These domain boundaries are dependent on the coupling constant, so that one matrix yields several clusters of different sizes, which show the self-interaction of the genome on different scales. This work was supported by a Grant from the International Human Frontier Science Program Organization (RGP0014/2014).

  9. Understanding onsets of rainfall in Southern Africa using temporal probabilistic modelling

    CSIR Research Space (South Africa)

    Cheruiyot, D

    2010-12-01

    Full Text Available This research investigates an alternative approach to automatically evolve the hidden temporal distribution of onset of rainfall directly from multivariate time series (MTS) data in the absence of domain experts. Temporal probabilistic modelling...

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

    Science.gov (United States)

    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....

  11. Branching bisimulation congruence for probabilistic systems

    NARCIS (Netherlands)

    Trcka, N.; Georgievska, S.; Aldini, A.; Baier, C.

    2008-01-01

    The notion of branching bisimulation for the alternating model of probabilistic systems is not a congruence with respect to parallel composition. In this paper we first define another branching bisimulation in the more general model allowing consecutive probabilistic transitions, and we prove that

  12. Variable selection and model choice in geoadditive regression models.

    Science.gov (United States)

    Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

    2009-06-01

    Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

  13. Probabilistic programming in Python using PyMC3

    Directory of Open Access Journals (Sweden)

    John Salvatier

    2016-04-01

    Full Text Available Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.

  14. Probabilistic machine learning and artificial intelligence.

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-28

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

  15. Probabilistic machine learning and artificial intelligence

    Science.gov (United States)

    Ghahramani, Zoubin

    2015-05-01

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

  16. Reasoning with probabilistic and deterministic graphical models exact algorithms

    CERN Document Server

    Dechter, Rina

    2013-01-01

    Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well

  17. Process and Context in Choice Models

    DEFF Research Database (Denmark)

    Ben-Akiva, Moshe; Palma, André de; McFadden, Daniel

    2012-01-01

    . The extended choice framework includes more behavioral richness through the explicit representation of the planning process preceding an action and its dynamics and the effects of context (family, friends, and market) on the process leading to a choice, as well as the inclusion of new types of subjective data...... in choice models. We discuss the key issues involved in applying the extended framework, focusing on richer data requirements, theories, and models, and present three partial demonstrations of the proposed framework. Future research challenges include the development of more comprehensive empirical tests...

  18. Probabilistic safety assessment model in consideration of human factors based on object-oriented bayesian networks

    International Nuclear Information System (INIS)

    Zhou Zhongbao; Zhou Jinglun; Sun Quan

    2007-01-01

    Effect of Human factors on system safety is increasingly serious, which is often ignored in traditional probabilistic safety assessment methods however. A new probabilistic safety assessment model based on object-oriented Bayesian networks is proposed in this paper. Human factors are integrated into the existed event sequence diagrams. Then the classes of the object-oriented Bayesian networks are constructed which are converted to latent Bayesian networks for inference. Finally, the inference results are integrated into event sequence diagrams for probabilistic safety assessment. The new method is applied to the accident of loss of coolant in a nuclear power plant. the results show that the model is not only applicable to real-time situation assessment, but also applicable to situation assessment based certain amount of information. The modeling complexity is kept down and the new method is appropriate to large complex systems due to the thoughts of object-oriented. (authors)

  19. Modelling the evolution and consequences of mate choice

    OpenAIRE

    Tazzyman, S. J.

    2010-01-01

    This thesis considers the evolution and the consequences of mate choice across a variety of taxa, using game theoretic, population genetic, and quantitative genetic modelling techniques. Part I is about the evolution of mate choice. In chapter 2, a population genetic model shows that mate choice is even beneficial in self-fertilising species such as Saccharomyces yeast. In chapter 3, a game theoretic model shows that female choice will be strongly dependent upon whether the benefi...

  20. MODELING PROBABILISTIC CONFLICT OF TECHNOLOGICAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    D. B. Desyatov

    2015-01-01

    Full Text Available Recently for the study of conflict increasingly used method of mathematical optical modeling. Its importance stems from the fact that experimental research such conflicts rather time-consuming and complex. However, existing approaches to the study of conflict do not take into account the stochastic nature of the systems, suffers from conceptual incompleteness. There is a need to develop models, algorithms and principles, in order to assess the conflict, to choose conflict resolution to ensure that not the worst of conditions. For stochastic technological systems as a utility function, we consider the probability of achieving a given objective. We assume that some system S1 is in conflict with the system S2, (SR2R К SR1R, if q(SR1R,SR2Rprobabilistic conflict of the first kind (А К1 B, if P(A/Bprobabilistic conflict of the second kind (А К2 B, if P(A/B

  1. Sensitivity of probabilistic MCO water content estimates to key assumptions

    International Nuclear Information System (INIS)

    DUNCAN, D.R.

    1999-01-01

    Sensitivity of probabilistic multi-canister overpack (MCO) water content estimates to key assumptions is evaluated with emphasis on the largest non-cladding film-contributors, water borne by particulates adhering to damage sites, and water borne by canister particulate. Calculations considered different choices of damage state degree of independence, different choices of percentile for reference high inputs, three types of input probability density function (pdfs): triangular, log-normal, and Weibull, and the number of scrap baskets in an MCO

  2. Glucocorticoid Regulation of Food-Choice Behavior in Humans: Evidence from Cushing's Syndrome.

    Science.gov (United States)

    Moeller, Scott J; Couto, Lizette; Cohen, Vanessa; Lalazar, Yelena; Makotkine, Iouri; Williams, Nia; Yehuda, Rachel; Goldstein, Rita Z; Geer, Eliza B

    2016-01-01

    The mechanisms by which glucocorticoids regulate food intake and resulting body mass in humans are not well-understood. One potential mechanism could involve modulation of reward processing, but human stress models examining effects of glucocorticoids on behavior contain important confounds. Here, we studied individuals with Cushing's syndrome, a rare endocrine disorder characterized by chronic excess endogenous glucocorticoids. Twenty-three patients with Cushing's syndrome (13 with active disease; 10 with disease in remission) and 15 controls with a comparably high body mass index (BMI) completed two simulated food-choice tasks (one with "explicit" task contingencies and one with "probabilistic" task contingencies), during which they indicated their objective preference for viewing high calorie food images vs. standardized pleasant, unpleasant, and neutral images. All participants also completed measures of food craving, and approximately half of the participants provided 24-h urine samples for assessment of cortisol and cortisone concentrations. Results showed that on the explicit task (but not the probabilistic task), participants with active Cushing's syndrome made fewer food-related choices than participants with Cushing's syndrome in remission, who in turn made fewer food-related choices than overweight controls. Corroborating this group effect, higher urine cortisone was negatively correlated with food-related choice in the subsample of all participants for whom these data were available. On the probabilistic task, despite a lack of group differences, higher food-related choice correlated with higher state and trait food craving in active Cushing's patients. Taken together, relative to overweight controls, Cushing's patients, particularly those with active disease, displayed a reduced vigor of responding for food rewards that was presumably attributable to glucocorticoid abnormalities. Beyond Cushing's, these results may have relevance for elucidating

  3. A probabilistic model for component-based shape synthesis

    KAUST Repository

    Kalogerakis, Evangelos

    2012-07-01

    We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis. © 2012 ACM 0730-0301/2012/08-ART55.

  4. Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure

    International Nuclear Information System (INIS)

    Praks, Pavel; Kopustinskas, Vytis; Masera, Marcelo

    2015-01-01

    The paper presents a probabilistic model to study security of supply in a gas network. The model is based on Monte-Carlo simulations with graph theory, and is implemented in the software tool ProGasNet. The software allows studying gas networks in various aspects including identification of weakest links and nodes, vulnerability analysis, bottleneck analysis, evaluation of new infrastructure etc. In this paper ProGasNet is applied to a benchmark network based on a real EU gas transmission network of several countries with the purpose of evaluating the security of supply effects of new infrastructure, either under construction, recently completed or under planning. The probabilistic model enables quantitative evaluations by comparing the reliability of gas supply in each consuming node of the network. - Highlights: • A Monte-Carlo algorithm for stochastic flow networks is presented. • Network elements can fail according to a given probabilistic model. • Priority supply pattern of gas transmission networks is assumed. • A real-world EU gas transmission network is presented and analyzed. • A risk ratio is used for security of supply quantification of a new infrastructure.

  5. Chiefly Symmetric: Results on the Scalability of Probabilistic Model Checking for Operating-System Code

    Directory of Open Access Journals (Sweden)

    Marcus Völp

    2012-11-01

    Full Text Available Reliability in terms of functional properties from the safety-liveness spectrum is an indispensable requirement of low-level operating-system (OS code. However, with evermore complex and thus less predictable hardware, quantitative and probabilistic guarantees become more and more important. Probabilistic model checking is one technique to automatically obtain these guarantees. First experiences with the automated quantitative analysis of low-level operating-system code confirm the expectation that the naive probabilistic model checking approach rapidly reaches its limits when increasing the numbers of processes. This paper reports on our work-in-progress to tackle the state explosion problem for low-level OS-code caused by the exponential blow-up of the model size when the number of processes grows. We studied the symmetry reduction approach and carried out our experiments with a simple test-and-test-and-set lock case study as a representative example for a wide range of protocols with natural inter-process dependencies and long-run properties. We quickly see a state-space explosion for scenarios where inter-process dependencies are insignificant. However, once inter-process dependencies dominate the picture models with hundred and more processes can be constructed and analysed.

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

    Science.gov (United States)

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

    2017-11-15

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

  7. Choice experiments versus revealed choice models : a before-after study of consumer spatial shopping behavior

    NARCIS (Netherlands)

    Timmermans, H.J.P.; Borgers, A.W.J.; Waerden, van der P.J.H.J.

    1992-01-01

    The purpose of this article is to compare a set of multinomial logit models derived from revealed choice data and a decompositional choice model derived from experimental data in terms of predictive success in the context of consumer spatial shopping behavior. Data on consumer shopping choice

  8. Probabilistic inversion for chicken processing lines

    International Nuclear Information System (INIS)

    Cooke, Roger M.; Nauta, Maarten; Havelaar, Arie H.; Fels, Ine van der

    2006-01-01

    We discuss an application of probabilistic inversion techniques to a model of campylobacter transmission in chicken processing lines. Such techniques are indicated when we wish to quantify a model which is new and perhaps unfamiliar to the expert community. In this case there are no measurements for estimating model parameters, and experts are typically unable to give a considered judgment. In such cases, experts are asked to quantify their uncertainty regarding variables which can be predicted by the model. The experts' distributions (after combination) are then pulled back onto the parameter space of the model, a process termed 'probabilistic inversion'. This study illustrates two such techniques, iterative proportional fitting (IPF) and PARmeter fitting for uncertain models (PARFUM). In addition, we illustrate how expert judgement on predicted observable quantities in combination with probabilistic inversion may be used for model validation and/or model criticism

  9. Probabilistic topic modeling for the analysis and classification of genomic sequences

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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  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.

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

    Science.gov (United States)

    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.

  12. Unified Probabilistic Models for Face Recognition from a Single Example Image per Person

    Institute of Scientific and Technical Information of China (English)

    Pin Liao; Li Shen

    2004-01-01

    This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.

  13. Competing probabilistic models for catch-effort relationships in wildlife censuses

    Energy Technology Data Exchange (ETDEWEB)

    Skalski, J.R.; Robson, D.S.; Matsuzaki, C.L.

    1983-01-01

    Two probabilistic models are presented for describing the chance that an animal is captured during a wildlife census, as a function of trapping effort. The models in turn are used to propose relationships between sampling intensity and catch-per-unit-effort (C.P.U.E.) that were field tested on small mammal populations. Capture data suggests a model of diminshing C.P.U.E. with increasing levels of trapping intensity. The catch-effort model is used to illustrate optimization procedures in the design of mark-recapture experiments for censusing wild populations. 14 references, 2 tables.

  14. Up-gradient transport in a probabilistic transport model

    DEFF Research Database (Denmark)

    Gavnholt, J.; Juul Rasmussen, J.; Garcia, O.E.

    2005-01-01

    The transport of particles or heat against the driving gradient is studied by employing a probabilistic transport model with a characteristic particle step length that depends on the local concentration or heat gradient. When this gradient is larger than a prescribed critical value, the standard....... These results supplement recent works by van Milligen [Phys. Plasmas 11, 3787 (2004)], which applied Levy distributed step sizes in the case of supercritical gradients to obtain the up-gradient transport. (c) 2005 American Institute of Physics....

  15. Hybrid discrete choice models: Gained insights versus increasing effort

    Energy Technology Data Exchange (ETDEWEB)

    Mariel, Petr, E-mail: petr.mariel@ehu.es [UPV/EHU, Economía Aplicada III, Avda. Lehendakari Aguire, 83, 48015 Bilbao (Spain); Meyerhoff, Jürgen [Institute for Landscape Architecture and Environmental Planning, Technical University of Berlin, D-10623 Berlin, Germany and The Kiel Institute for the World Economy, Duesternbrooker Weg 120, 24105 Kiel (Germany)

    2016-10-15

    Hybrid choice models expand the standard models in discrete choice modelling by incorporating psychological factors as latent variables. They could therefore provide further insights into choice processes and underlying taste heterogeneity but the costs of estimating these models often significantly increase. This paper aims at comparing the results from a hybrid choice model and a classical random parameter logit. Point of departure for this analysis is whether researchers and practitioners should add hybrid choice models to their suite of models routinely estimated. Our comparison reveals, in line with the few prior studies, that hybrid models gain in efficiency by the inclusion of additional information. The use of one of the two proposed approaches, however, depends on the objective of the analysis. If disentangling preference heterogeneity is most important, hybrid model seems to be preferable. If the focus is on predictive power, a standard random parameter logit model might be the better choice. Finally, we give recommendations for an adequate use of hybrid choice models based on known principles of elementary scientific inference. - Highlights: • The paper compares performance of a Hybrid Choice Model (HCM) and a classical Random Parameter Logit (RPL) model. • The HCM indeed provides insights regarding preference heterogeneity not gained from the RPL. • The RPL has similar predictive power as the HCM in our data. • The costs of estimating HCM seem to be justified when learning more on taste heterogeneity is a major study objective.

  16. Hybrid discrete choice models: Gained insights versus increasing effort

    International Nuclear Information System (INIS)

    Mariel, Petr; Meyerhoff, Jürgen

    2016-01-01

    Hybrid choice models expand the standard models in discrete choice modelling by incorporating psychological factors as latent variables. They could therefore provide further insights into choice processes and underlying taste heterogeneity but the costs of estimating these models often significantly increase. This paper aims at comparing the results from a hybrid choice model and a classical random parameter logit. Point of departure for this analysis is whether researchers and practitioners should add hybrid choice models to their suite of models routinely estimated. Our comparison reveals, in line with the few prior studies, that hybrid models gain in efficiency by the inclusion of additional information. The use of one of the two proposed approaches, however, depends on the objective of the analysis. If disentangling preference heterogeneity is most important, hybrid model seems to be preferable. If the focus is on predictive power, a standard random parameter logit model might be the better choice. Finally, we give recommendations for an adequate use of hybrid choice models based on known principles of elementary scientific inference. - Highlights: • The paper compares performance of a Hybrid Choice Model (HCM) and a classical Random Parameter Logit (RPL) model. • The HCM indeed provides insights regarding preference heterogeneity not gained from the RPL. • The RPL has similar predictive power as the HCM in our data. • The costs of estimating HCM seem to be justified when learning more on taste heterogeneity is a major study objective.

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

    Science.gov (United States)

    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

  18. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Dejan Pecevski

    2011-12-01

    Full Text Available 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.

  19. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

    Science.gov (United States)

    Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

    2011-12-01

    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.

  20. Probabilistic Structural Analysis Program

    Science.gov (United States)

    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.

  1. Probabilistic modeling of crack networks in thermal fatigue

    International Nuclear Information System (INIS)

    Malesys, N.

    2007-11-01

    Thermal superficial crack networks have been detected in mixing zone of cooling system in nuclear power plants. Numerous experimental works have already been led to characterize initiation and propagation of these cracks. The random aspect of initiation led to propose a probabilistic model for the formation and propagation of crack networks in thermal fatigue. In a first part, uniaxial mechanical test were performed on smooth and slightly notched specimens in order to characterize the initiation of multiple cracks, their arrest due to obscuration and the coalescence phenomenon by recovery of amplification stress zones. In a second time, the probabilistic model was established under two assumptions: the continuous cracks initiation on surface, described by a Poisson point process law with threshold, and the shielding phenomenon which prohibits the initiation or the propagation of a crack if this one is in the relaxation stress zone of another existing crack. The crack propagation is assumed to follow a Paris' law based on the computation of stress intensity factors at the top and the bottom of crack. The evolution of multiaxial cracks on the surface can be followed thanks to three quantities: the shielding probability, comparable to a damage variable of the structure, the initiated crack density, representing the total number of cracks per unit surface which can be compared to experimental observations, and the propagating crack density, representing the number per unit surface of active cracks in the network. The crack sizes distribution is also computed by the model allowing an easier comparison with experimental results. (author)

  2. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  3. A practical test for the choice of mixing distribution in discrete choice models

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; Bierlaire, Michel

    2007-01-01

    The choice of a specific distribution for random parameters of discrete choice models is a critical issue in transportation analysis. Indeed, various pieces of research have demonstrated that an inappropriate choice of the distribution may lead to serious bias in model forecast and in the estimated...... means of random parameters. In this paper, we propose a practical test, based on seminonparametric techniques. The test is analyzed both on synthetic and real data, and is shown to be simple and powerful. (c) 2007 Elsevier Ltd. All rights reserved....

  4. Probabilistic Failure Analysis of Bone Using a Finite Element Model of Mineral-Collagen Composites

    OpenAIRE

    Dong, X. Neil; Guda, Teja; Millwater, Harry R.; Wang, Xiaodu

    2008-01-01

    Microdamage accumulation is a major pathway for energy dissipation during the post-yield deformation of bone. In this study, a two-dimensional probabilistic finite element model of a mineral-collagen composite was developed to investigate the influence of the tissue and ultrastructural properties of bone on the evolution of microdamage from an initial defect in tension. The probabilistic failure analyses indicated that the microdamage progression would be along the plane of the initial defect...

  5. Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

    Directory of Open Access Journals (Sweden)

    Tolga Kaya

    2010-11-01

    Full Text Available The purpose of this study is to compare the performances of Artificial Neural Networks (ANN and Multinomial Probit (MNP approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

  6. Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling

    Directory of Open Access Journals (Sweden)

    Moritz eBoos

    2016-05-01

    Full Text Available Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling 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 behaviour. 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 modelling 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 modelling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  7. A logic for inductive probabilistic reasoning

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2005-01-01

    Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from '70% of As are Bs" and "a is an A" infer...... that a is a B with probability 0.7. Direct inference is generalized by Jeffrey's rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have...... to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework...

  8. The Dependent Poisson Race Model and Modeling Dependence in Conjoint Choice Experiments

    Science.gov (United States)

    Ruan, Shiling; MacEachern, Steven N.; Otter, Thomas; Dean, Angela M.

    2008-01-01

    Conjoint choice experiments are used widely in marketing to study consumer preferences amongst alternative products. We develop a class of choice models, belonging to the class of Poisson race models, that describe a "random utility" which lends itself to a process-based description of choice. The models incorporate a dependence structure which…

  9. Suboptimal choice, reward-predictive signals, and temporal information.

    Science.gov (United States)

    Cunningham, Paul J; Shahan, Timothy A

    2018-01-01

    Suboptimal choice refers to preference for an alternative offering a low probability of food (suboptimal alternative) over an alternative offering a higher probability of food (optimal alternative). Numerous studies have found that stimuli signaling probabilistic food play a critical role in the development and maintenance of suboptimal choice. However, there is still much debate about how to characterize how these stimuli influence suboptimal choice. There is substantial evidence that the temporal information conveyed by a food-predictive signal governs its function as both a Pavlovian conditioned stimulus and as an instrumental conditioned reinforcer. Thus, we explore the possibility that food-predictive signals influence suboptimal choice via the temporal information they convey. Application of this temporal information-theoretic approach to suboptimal choice provides a formal, quantitative framework that describes how food-predictive signals influence suboptimal choice in a manner consistent with related phenomena in Pavlovian conditioning and conditioned reinforcement. Our reanalysis of previous data on suboptimal choice suggests that, generally speaking, preference in the suboptimal choice procedure tracks relative temporal information conveyed by food-predictive signals for the suboptimal and optimal alternatives. The model suggests that suboptimal choice develops when the food-predictive signal for the suboptimal alternative conveys more temporal information than that for the optimal alternative. Finally, incorporating a role for competition between temporal information provided by food-predictive signals and relative primary reinforcement rate provides a reasonable account of existing data on suboptimal choice. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  10. Probabilistic modelling of the high-pressure arc cathode spot displacement dynamic

    CERN Document Server

    Coulombe, S

    2003-01-01

    A probabilistic modelling approach for the study of the cathode spot displacement dynamic in high-pressure arc systems is developed in an attempt to interpret the observed voltage fluctuations. The general framework of the model allows to define simple, probabilistic displacement rules, the so-called cathode spot dynamic rules, for various possible surface states (un-arced metal, arced, contaminated) and to study the resulting dynamic of the cathode spot displacements over one or several arc passages. The displacements of the type-A cathode spot (macro-spot) in a magnetically rotating arc using concentric electrodes made up of either clean or contaminated metal surfaces is considered. Experimental observations for this system revealed a 1/f sup - sup t sup i sup l sup d sup e sup 1 signature in the frequency power spectrum (FPS) of the arc voltage for anchoring arc conditions on the cathode (e.g. clean metal surface), while it shows a 'white noise' signature for conditions favouring a smooth movement (e.g. ox...

  11. Probabilistic Reversible Automata and Quantum Automata

    OpenAIRE

    Golovkins, Marats; Kravtsev, Maksim

    2002-01-01

    To study relationship between quantum finite automata and probabilistic finite automata, we introduce a notion of probabilistic reversible automata (PRA, or doubly stochastic automata). We find that there is a strong relationship between different possible models of PRA and corresponding models of quantum finite automata. We also propose a classification of reversible finite 1-way automata.

  12. Probabilistic, multi-variate flood damage modelling using random forests and Bayesian networks

    Science.gov (United States)

    Kreibich, Heidi; Schröter, Kai

    2015-04-01

    Decisions on flood risk management and adaptation are increasingly based on risk analyses. Such analyses are associated with considerable uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention recently, they are hardly applied in flood damage assessments. Most of the damage models usually applied in standard practice have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. This presentation will show approaches for probabilistic, multi-variate flood damage modelling on the micro- and meso-scale and discuss their potential and limitations. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., Merz, B. (2014): How useful are complex flood damage models? - Water Resources Research, 50, 4, p. 3378-3395.

  13. A day in the city : using conjoint choice experiments to model urban tourists' choice of activity packages

    NARCIS (Netherlands)

    Dellaert, B.G.C.; Borgers, A.W.J.; Timmermans, H.J.P.

    1995-01-01

    This paper introduces and tests a conjoint choice experiment approach to modeling urban tourists' choice of activity packages. The joint logit model is introduced as a tool to model choices between combinations of activities and an experimental design approach is proposed that includes attributes

  14. The management of subsurface uncertainty using probabilistic modeling of life cycle production forecasts and cash flows

    International Nuclear Information System (INIS)

    Olatunbosun, O. O.

    1998-01-01

    The subject pertains to the implementation of the full range of subsurface uncertainties in life cycle probabilistic forecasting and its extension to project cash flows using the methodology of probabilities. A new tool has been developed in the probabilistic application of Crystal-Ball which can model reservoir volumetrics, life cycle production forecasts and project cash flows in a single environment. The tool is modular such that the volumetrics and cash flow modules are optional. Production forecasts are often generated by applying a decline equation to single best estimate values of input parameters such as initial potential, decline rate, abandonment rate etc -or sometimes by results of reservoir simulation. This new tool provides a means of implementing the full range of uncertainties and interdependencies of the input parameters into the production forecasts by defining the input parameters as probability density functions, PDFs and performing several iterations to generate an expectation curve forecast. Abandonment rate is implemented in each iteration via a link to an OPEX model. The expectation curve forecast is input into a cash flow model to generate a probabilistic NPV. Base case and sensitivity runs from reservoir simulation can likewise form the basis for a probabilistic production forecast from which a probabilistic cash flow can be generated. A good illustration of the application of this tool is in the modelling of the production forecast for a well that encounters its target reservoirs in OUT/ODT situation and thus has significant uncertainties. The uncertainty in presence and size (if present) of gas cap and dependency between ultimate recovery and initial potential amongst other uncertainties can be easily implemented in the production forecast with this tool. From the expectation curve forecast, a probabilistic NPV can be easily generated. Possible applications of this tool include: i. estimation of range of actual recoverable volumes based

  15. A PROBABILISTIC DEMAND APPLICATION IN THE AMERICAN CRACKER MARKET

    Directory of Open Access Journals (Sweden)

    Rutherford Cd. Johnson

    2016-07-01

    Full Text Available Knowledge of the distribution of consumer buying strategies by producers may permit improved marketing strategies and improved ability to respond to volatile market conditions. In order to investigate potential ways of gaining such knowledge, this study extends the work of Kahneman, Russell, and Thaler through the application of a probabilistic demand framework using Choice Wave theory, based on the Schrödinger Wave Equation in quantum mechanics. Probabilistic variability of response to health information and its potential influence on buying strategies is also investigated, extending the work of Clement, Johnson, Hu, Pagoulatos, and Debertin. In the present study, the domestic cracker market within fourteen U.S. metropolitan areas is segmented, using the Choice Wave Probabilistic Demand approach, into two statistically independent “Consumer Types” of seven metropolitan areas each. The two Consumer Types are shown to have statistically different elasticities than each other and from the combined market as a whole. This approach may provide not only improved marketing strategies through improved awareness of consumer preferences and buying strategies, especially in the volatile agricultural sector, but also may be useful in aiding producers of store brand/private label products in finding desirable markets in which to compete against national brands. The results also suggest that supply/producer-side strategies should take into account the ways in which information, whether under the direct control of the producer or not, may influence and change consumer buying strategies.

  16. Probabilistic delay differential equation modeling of event-related potentials.

    Science.gov (United States)

    Ostwald, Dirk; Starke, Ludger

    2016-08-01

    "Dynamic causal models" (DCMs) are a promising approach in the analysis of functional neuroimaging data due to their biophysical interpretability and their consolidation of functional-segregative and functional-integrative propositions. In this theoretical note we are concerned with the DCM framework for electroencephalographically recorded event-related potentials (ERP-DCM). Intuitively, ERP-DCM combines deterministic dynamical neural mass models with dipole-based EEG forward models to describe the event-related scalp potential time-series over the entire electrode space. Since its inception, ERP-DCM has been successfully employed to capture the neural underpinnings of a wide range of neurocognitive phenomena. However, in spite of its empirical popularity, the technical literature on ERP-DCM remains somewhat patchy. A number of previous communications have detailed certain aspects of the approach, but no unified and coherent documentation exists. With this technical note, we aim to close this gap and to increase the technical accessibility of ERP-DCM. Specifically, this note makes the following novel contributions: firstly, we provide a unified and coherent review of the mathematical machinery of the latent and forward models constituting ERP-DCM by formulating the approach as a probabilistic latent delay differential equation model. Secondly, we emphasize the probabilistic nature of the model and its variational Bayesian inversion scheme by explicitly deriving the variational free energy function in terms of both the likelihood expectation and variance parameters. Thirdly, we detail and validate the estimation of the model with a special focus on the explicit form of the variational free energy function and introduce a conventional nonlinear optimization scheme for its maximization. Finally, we identify and discuss a number of computational issues which may be addressed in the future development of the approach. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Risk Management Technologies With Logic and Probabilistic Models

    CERN Document Server

    Solozhentsev, E D

    2012-01-01

    This book presents intellectual, innovative, information technologies (I3-technologies) based on logical and probabilistic (LP) risk models. The technologies presented here consider such models for structurally complex systems and processes with logical links and with random events in economics and technology.  The volume describes the following components of risk management technologies: LP-calculus; classes of LP-models of risk and efficiency; procedures for different classes; special software for different classes; examples of applications; methods for the estimation of probabilities of events based on expert information. Also described are a variety of training courses in these topics. The classes of risk models treated here are: LP-modeling, LP-classification, LP-efficiency, and LP-forecasting. Particular attention is paid to LP-models of risk of failure to resolve difficult economic and technical problems. Amongst the  discussed  procedures of I3-technologies  are the construction of  LP-models,...

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

    KAUST Repository

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

    2011-01-01

    . 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.Methods: This paper describes inductive learning methods to train protein-independent probabilistic models of H

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

  1. Probabilistic graphical models to deal with age estimation of living persons.

    Science.gov (United States)

    Sironi, Emanuele; Gallidabino, Matteo; Weyermann, Céline; Taroni, Franco

    2016-03-01

    Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

  2. Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents

    International Nuclear Information System (INIS)

    Chang, Y.H.J.; Mosleh, A.

    2007-01-01

    This is the last in a series of five papers that discuss the Information Decision and Action in Crew (IDAC) context for human reliability analysis (HRA) and example application. The model is developed to probabilistically predict the responses of the control room operating crew in nuclear power plants during an accident, for use in probabilistic risk assessments (PRA). The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper describes a dynamic PRA computer simulation program, accident dynamics simulator (ADS), developed in part to implement the IDAC model. This paper also provides a detailed example of implementing a simpler version of IDAC, compared with the IDAC model discussed in the first four papers of this series, to demonstrate the practicality of integrating a detailed cognitive HRA model within a dynamic PRA framework

  3. Approach to modeling of human performance for purposes of probabilistic risk assessment

    International Nuclear Information System (INIS)

    Swain, A.D.

    1983-01-01

    This paper describes the general approach taken in NUREG/CR-1278 to model human performance in sufficienct detail to permit probabilistic risk assessments of nuclear power plant operations. To show the basis for the more specific models in the above NUREG, a simplified model of the human component in man-machine systems is presented, the role of performance shaping factors is discussed, and special problems in modeling the cognitive aspect of behavior are described

  4. A Probabilistic Model of the LMAC Protocol for Concurrent Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Esparza, Luz Judith R; Zeng, Kebin; Nielsen, Bo Friis

    2011-01-01

    We present a probabilistic model for the network setup phase of the Lightweight Medium Access Protocol (LMAC) for concurrent Wireless Sensor Networks. In the network setup phase, time slots are allocated to the individual sensors through resolution of successive collisions. The setup phase...

  5. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1991-06-01

    The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. 3 refs., 1 fig

  6. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1991-01-01

    The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. (Author)

  7. A probabilistic model for estimating the waiting time until the simultaneous collapse of two contingencies

    International Nuclear Information System (INIS)

    Barnett, C.S.

    1992-01-01

    The double contingency principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative and intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive for a search for a quantitative and probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies, as functions of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess the effectiveness of the DCP. (Author)

  8. Probabilistic Mu-Calculus

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Mardare, Radu Iulian; Xue, Bingtian

    2016-01-01

    We introduce a version of the probabilistic µ-calculus (PMC) built on top of a probabilistic modal logic that allows encoding n-ary inequational conditions on transition probabilities. PMC extends previously studied calculi and we prove that, despite its expressiveness, it enjoys a series of good...... metaproperties. Firstly, we prove the decidability of satisfiability checking by establishing the small model property. An algorithm for deciding the satisfiability problem is developed. As a second major result, we provide a complete axiomatization for the alternation-free fragment of PMC. The completeness proof...

  9. Software for Probabilistic Risk Reduction

    Science.gov (United States)

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

    2004-01-01

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

  10. Hold it! The influence of lingering rewards on choice diversification and persistence.

    Science.gov (United States)

    Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R

    2017-11-01

    Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice-outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented a response to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. Human X-chromosome inactivation pattern distributions fit a model of genetically influenced choice better than models of completely random choice

    Science.gov (United States)

    Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C

    2013-01-01

    In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377

  12. Comparison of Vehicle Choice Models

    Energy Technology Data Exchange (ETDEWEB)

    Stephens, Thomas S. [Argonne National Lab. (ANL), Argonne, IL (United States); Levinson, Rebecca S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brooker, Aaron [National Renewable Energy Lab. (NREL), Golden, CO (United States); Liu, Changzheng [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lin, Zhenhong [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Birky, Alicia [Energetics Incorporated, Columbia, MD (United States); Kontou, Eleftheria [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2017-10-01

    Five consumer vehicle choice models that give projections of future sales shares of light-duty vehicles were compared by running each model using the same inputs, where possible, for two scenarios. The five models compared — LVCFlex, MA3T, LAVE-Trans, ParaChoice, and ADOPT — have been used in support of the Energy Efficiency and Renewable Energy (EERE) Vehicle Technologies Office in analyses of future light-duty vehicle markets under different assumptions about future vehicle technologies and market conditions. The models give projections of sales shares by powertrain technology. Projections made using common, but not identical, inputs showed qualitative agreement, with the exception of ADOPT. ADOPT estimated somewhat lower advanced vehicle shares, mostly composed of hybrid electric vehicles. Other models projected large shares of multiple advanced vehicle powertrains. Projections of models differed in significant ways, including how different technologies penetrated cars and light trucks. Since the models are constructed differently and take different inputs, not all inputs were identical, but were the same or very similar where possible.

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

    Science.gov (United States)

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

    2003-10-01

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

  14. Probabilistic linguistics

    NARCIS (Netherlands)

    Bod, R.; Heine, B.; Narrog, H.

    2010-01-01

    Probabilistic linguistics takes all linguistic evidence as positive evidence and lets statistics decide. It allows for accurate modelling of gradient phenomena in production and perception, and suggests that rule-like behaviour is no more than a side effect of maximizing probability. This chapter

  15. Precise Quantitative Analysis of Probabilistic Business Process Model and Notation Workflows

    DEFF Research Database (Denmark)

    Herbert, Luke Thomas; Sharp, Robin

    2013-01-01

    We present a framework for modeling and analysis of real-world business workflows. We present a formalized core subset of the business process modeling and notation (BPMN) and then proceed to extend this language with probabilistic nondeterministic branching and general-purpose reward annotations...... the entire BPMN language, allow for more complex annotations and ultimately to automatically synthesize workflows by composing predefined subprocesses, in order to achieve a configuration that is optimal for parameters of interest....

  16. Modelling Choice of Information Sources

    Directory of Open Access Journals (Sweden)

    Agha Faisal Habib Pathan

    2013-04-01

    Full Text Available This paper addresses the significance of traveller information sources including mono-modal and multimodal websites for travel decisions. The research follows a decision paradigm developed earlier, involving an information acquisition process for travel choices, and identifies the abstract characteristics of new information sources that deserve further investigation (e.g. by incorporating these in models and studying their significance in model estimation. A Stated Preference experiment is developed and the utility functions are formulated by expanding the travellers' choice set to include different combinations of sources of information. In order to study the underlying choice mechanisms, the resulting variables are examined in models based on different behavioural strategies, including utility maximisation and minimising the regret associated with the foregone alternatives. This research confirmed that RRM (Random Regret Minimisation Theory can fruitfully be used and can provide important insights for behavioural studies. The study also analyses the properties of travel planning websites and establishes a link between travel choices and the content, provenance, design, presence of advertisements, and presentation of information. The results indicate that travellers give particular credence to governmentowned sources and put more importance on their own previous experiences than on any other single source of information. Information from multimodal websites is more influential than that on train-only websites. This in turn is more influential than information from friends, while information from coachonly websites is the least influential. A website with less search time, specific information on users' own criteria, and real time information is regarded as most attractive

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

    Science.gov (United States)

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

    2007-12-01

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

  18. Probabilistic Design and Analysis Framework

    Science.gov (United States)

    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.

  19. Modeling Stochastic Route Choice Behaviors with Equivalent Impedance

    Directory of Open Access Journals (Sweden)

    Jun Li

    2015-01-01

    Full Text Available A Logit-based route choice model is proposed to address the overlapping and scaling problems in the traditional multinomial Logit model. The nonoverlapping links are defined as a subnetwork, and its equivalent impedance is explicitly calculated in order to simply network analyzing. The overlapping links are repeatedly merged into subnetworks with Logit-based equivalent travel costs. The choice set at each intersection comprises only the virtual equivalent route without overlapping. In order to capture heterogeneity in perception errors of different sizes of networks, different scale parameters are assigned to subnetworks and they are linked to the topological relationships to avoid estimation burden. The proposed model provides an alternative method to model the stochastic route choice behaviors without the overlapping and scaling problems, and it still maintains the simple and closed-form expression from the MNL model. A link-based loading algorithm based on Dial’s algorithm is proposed to obviate route enumeration and it is suitable to be applied on large-scale networks. Finally a comparison between the proposed model and other route choice models is given by numerical examples.

  20. Arbitrage and Hedging in a non probabilistic framework

    OpenAIRE

    Alvarez, Alexander; Ferrando, Sebastian; Olivares, Pablo

    2011-01-01

    The paper studies the concepts of hedging and arbitrage in a non probabilistic framework. It provides conditions for non probabilistic arbitrage based on the topological structure of the trajectory space and makes connections with the usual notion of arbitrage. Several examples illustrate the non probabilistic arbitrage as well perfect replication of options under continuous and discontinuous trajectories, the results can then be applied in probabilistic models path by path. The approach is r...

  1. Machine learning, computer vision, and probabilistic models in jet physics

    CERN Multimedia

    CERN. Geneva; NACHMAN, Ben

    2015-01-01

    In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image processing for the first time, improving the performance to identify highly boosted W bosons with respect to state-of-the-art methods, and providing a new way to visualize the discriminant features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods. Second, we will present Fuzzy jets: a new paradigm for jet clustering using machine learning methods. Fuzzy jets view jet clustering as an unsupervised learning task and incorporate a probabilistic assignment of particles to jets to learn new features of the jet structure. In particular, we wi...

  2. Deliverable D74.2. Probabilistic analysis methods for support structures

    DEFF Research Database (Denmark)

    Gintautas, Tomas

    2018-01-01

    Relevant Description: Report describing the probabilistic analysis for offshore substructures and results attained. This includes comparison with experimental data and with conventional design. Specific targets: 1) Estimate current reliability level of support structures 2) Development of basis...... for probabilistic calculations and evaluation of reliability for offshore support structures (substructures) 3) Development of a probabilistic model for stiffness and strength of soil parameters and for modeling geotechnical load bearing capacity 4) Comparison between probabilistic analysis and deterministic...

  3. Use of probabilistic relational model (PRM) for dependability analysis of complex systems

    OpenAIRE

    Medina-Oliva , Gabriela; Weber , Philippe; Levrat , Eric; Iung , Benoît

    2010-01-01

    International audience; This paper proposes a methodology to develop a aided decision-making tool for assessing the dependability and performances (i.e. reliability) of an industrial system. This tool is built on a model based on a new formalism, called the probabilistic relational model (PRM) which is adapted to deal with large and complex systems. The model is formalized from functional, dysfunctional and informational studies of the technical industrial systems. An application of this meth...

  4. Probabilistic Modeling of Intracranial Pressure Effects on Optic Nerve Biomechanics

    Science.gov (United States)

    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.

  5. Some thoughts on the future of probabilistic structural design of nuclear components

    International Nuclear Information System (INIS)

    Stancampiano, P.A.

    1978-01-01

    This paper presents some views on the future role of probabilistic methods in the structural design of nuclear components. The existing deterministic design approach is discussed and compared to the probabilistic approach. Some of the objections to both deterministic and probabilistic design are listed. Extensive research and development activities are required to mature the probabilistic approach suficiently to make it cost-effective and competitive with current deterministic design practices. The required research activities deal with probabilistic methods development, more realistic casual failure mode models development, and statistical data models development. A quasi-probabilistic structural design approach is recommended which accounts for the random error in the design models. (Auth.)

  6. A random regret minimization model of travel choice

    NARCIS (Netherlands)

    Chorus, C.G.; Arentze, T.A.; Timmermans, H.J.P.

    2008-01-01

    Abstract This paper presents an alternative to Random Utility-Maximization models of travel choice. Our Random Regret-Minimization model is rooted in Regret Theory and provides several useful features for travel demand analysis. Firstly, it allows for the possibility that choices between travel

  7. Comparison of Vehicle Choice Models

    Energy Technology Data Exchange (ETDEWEB)

    Stephens, Thomas S. [Argonne National Lab. (ANL), Argonne, IL (United States); Levinson, Rebecca S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brooker, Aaron [National Renewable Energy Lab. (NREL), Golden, CO (United States); Liu, Changzheng [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lin, Zhenhong [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Birky, Alicia [Energetics Incorporated, Columbia, MD (United States); Kontou, Eleftheria [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2017-10-31

    Five consumer vehicle choice models that give projections of future sales shares of light-duty vehicles were compared by running each model using the same inputs, where possible, for two scenarios. The five models compared — LVCFlex, MA3T, LAVE-Trans, ParaChoice, and ADOPT — have been used in support of the Energy Efficiency and Renewable Energy (EERE) Vehicle Technologies Office in analyses of future light-duty vehicle markets under different assumptions about future vehicle technologies and market conditions. The models give projections of sales shares by powertrain technology. Projections made using common, but not identical, inputs showed qualitative agreement, with the exception of ADOPT. ADOPT estimated somewhat lower advanced vehicle shares, mostly composed of hybrid electric vehicles. Other models projected large shares of multiple advanced vehicle powertrains. Projections of models differed in significant ways, including how different technologies penetrated cars and light trucks. Since the models are constructed differently and take different inputs, not all inputs were identical, but were the same or very similar where possible. Projections by all models were in close agreement only in the first few years. Although the projections from LVCFlex, MA3T, LAVE-Trans, and ParaChoice were in qualitative agreement, there were significant differences in sales shares given by the different models for individual powertrain types, particularly in later years (2030 and later). For example, projected sales shares of conventional spark-ignition vehicles in 2030 for a given scenario ranged from 35% to 74%. Reasons for such differences are discussed, recognizing that these models were not developed to give quantitatively accurate predictions of future sales shares, but to represent vehicles markets realistically and capture the connections between sales and important influences. Model features were also compared at a high level, and suggestions for further comparison

  8. ParaChoice Model.

    Energy Technology Data Exchange (ETDEWEB)

    Heimer, Brandon Walter [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Levinson, Rebecca Sobel [Sandia National Lab. (SNL-CA), Livermore, CA (United States); West, Todd H. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

    2017-12-01

    Analysis with the ParaChoice model addresses three barriers from the VTO Multi-Year Program Plan: availability of alternative fuels and electric charging station infrastructure, availability of AFVs and electric drive vehicles, and consumer reluctance to purchase new technologies. In this fiscal year, we first examined the relationship between the availability of alternative fuels and station infrastructure. Specifically, we studied how electric vehicle charging infrastructure affects the ability of EVs to compete with vehicles that rely on mature, conventional petroleum-based fuels. Second, we studied how the availability of less costly AFVs promotes their representation in the LDV fleet. Third, we used ParaChoice trade space analyses to help inform which consumers are reluctant to purchase new technologies. Last, we began analysis of impacts of alternative energy technologies on Class 8 trucks to isolate those that may most efficaciously advance HDV efficiency and petroleum use reduction goals.

  9. Probabilistic Modeling of Aircraft Trajectories for Dynamic Separation Volumes

    Science.gov (United States)

    Lewis, Timothy A.

    2016-01-01

    With a proliferation of new and unconventional vehicles and operations expected in the future, the ab initio airspace design will require new approaches to trajectory prediction for separation assurance and other air traffic management functions. This paper presents an approach to probabilistic modeling of the trajectory of an aircraft when its intent is unknown. The approach uses a set of feature functions to constrain a maximum entropy probability distribution based on a set of observed aircraft trajectories. This model can be used to sample new aircraft trajectories to form an ensemble reflecting the variability in an aircraft's intent. The model learning process ensures that the variability in this ensemble reflects the behavior observed in the original data set. Computational examples are presented.

  10. Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models

    DEFF Research Database (Denmark)

    David, M.; Ramahatana, F.; Trombe, Pierre-Julien

    2016-01-01

    Forecasting of the solar irradiance is a key feature in order to increase the penetration rate of solar energy into the energy grids. Indeed, the anticipation of the fluctuations of the solar renewables allows a better management of the production means of electricity and a better operation...... sky index show some similarities with that of financial time series. The aim of this paper is to assess the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance. In addition, a recursive...... regarding the statistical distribution of the error, the reliability of the probabilistic forecasts stands in the same order of magnitude as other works done in the field of solar forecasting....

  11. Economic choices reveal probability distortion in macaque monkeys.

    Science.gov (United States)

    Stauffer, William R; Lak, Armin; Bossaerts, Peter; Schultz, Wolfram

    2015-02-18

    Economic choices are largely determined by two principal elements, reward value (utility) and probability. Although nonlinear utility functions have been acknowledged for centuries, nonlinear probability weighting (probability distortion) was only recently recognized as a ubiquitous aspect of real-world choice behavior. Even when outcome probabilities are known and acknowledged, human decision makers often overweight low probability outcomes and underweight high probability outcomes. Whereas recent studies measured utility functions and their corresponding neural correlates in monkeys, it is not known whether monkeys distort probability in a manner similar to humans. Therefore, we investigated economic choices in macaque monkeys for evidence of probability distortion. We trained two monkeys to predict reward from probabilistic gambles with constant outcome values (0.5 ml or nothing). The probability of winning was conveyed using explicit visual cues (sector stimuli). Choices between the gambles revealed that the monkeys used the explicit probability information to make meaningful decisions. Using these cues, we measured probability distortion from choices between the gambles and safe rewards. Parametric modeling of the choices revealed classic probability weighting functions with inverted-S shape. Therefore, the animals overweighted low probability rewards and underweighted high probability rewards. Empirical investigation of the behavior verified that the choices were best explained by a combination of nonlinear value and nonlinear probability distortion. Together, these results suggest that probability distortion may reflect evolutionarily preserved neuronal processing. Copyright © 2015 Stauffer et al.

  12. The selection of probabilistic safety assessment techniques for non-reactor nuclear facilities

    International Nuclear Information System (INIS)

    Vail, J.

    1992-01-01

    Historically, the probabilistic safety assessment (PSA) methodology of choice is the well known event tree/fault tree inductive technique. For reactor facilities is has stood the test of time. Some non-reactor nuclear facilities have found inductive methodologies difficult to apply. The stand-alone fault tree deductive technique has been used effectively to analyze risk in nuclear chemical processing facilities and waste handling facilities. The selection between the two choices suggest benefits from use of the deductive method for non-reactor facilities

  13. Comparison of Microscopic Drivers' Probabilistic Lane-changing Models With Real Traffic Microscopic Data

    Directory of Open Access Journals (Sweden)

    Seyyed Mohammad Sadat Hoseini

    2011-07-01

    Full Text Available The difficulties of microscopic-level simulation models to accurately reproduce real traffic phenomena stem not only from the complexity of calibration and validation operations, but also from the structural inadequacies of the sub-models themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty in gathering accurate field data. This paper studies the traffic behaviour of individual drivers utilizing vehicle trajectory data extracted from digital images collected from freeways in Iran. These data are used to evaluate the four proposed microscopic traffic models. One of the models is based on the traffic regulations in Iran and the three others are probabilistic models that use a decision factor for calculating the probability of choosing a position on the freeway by a driver. The decision factors for three probabilistic models are increasing speed, decreasing risk of collision, and increasing speed combined with decreasing risk of collision. The models are simulated by a cellular automata simulator and compared with the real data. It is shown that the model based on driving regulations is not valid, but that other models appear useful for predicting the driver’s behaviour on freeway segments in Iran during noncongested conditions.

  14. Probabilistic Mobility Models for Mobile and Wireless Networks

    DEFF Research Database (Denmark)

    Song, Lei; Godskesen, Jens Christian

    2010-01-01

    In this paper we present a probabilistic broadcast calculus for mobile and wireless networks whose connections are unreliable. In our calculus broadcasted messages can be lost with a certain probability, and due to mobility the connection probabilities may change. If a network broadcasts a message...... from a location it will evolve to a network distribution depending on whether nodes at other locations receive the message or not. Mobility of locations is not arbitrary but guarded by a probabilistic mobility function (PMF) and we also define the notion of a weak bisimulation given a PMF...

  15. Probabilistic brains: knowns and unknowns

    Science.gov (United States)

    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

  16. Complexity effects in choice experiments-based models

    NARCIS (Netherlands)

    Dellaert, B.G.C.; Donkers, B.; van Soest, A.H.O.

    2012-01-01

    Many firms rely on choice experiment–based models to evaluate future marketing actions under various market conditions. This research investigates choice complexity (i.e., number of alternatives, number of attributes, and utility similarity between the most attractive alternatives) and individual

  17. Probabilistic Design of Wind Turbines

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Toft, H.S.

    2010-01-01

    Probabilistic design of wind turbines requires definition of the structural elements to be included in the probabilistic basis: e.g., blades, tower, foundation; identification of important failure modes; careful stochastic modeling of the uncertain parameters; recommendations for target reliability....... It is described how uncertainties in wind turbine design related to computational models, statistical data from test specimens, results from a few full-scale tests and from prototype wind turbines can be accounted for using the Maximum Likelihood Method and a Bayesian approach. Assessment of the optimal...... reliability level by cost-benefit optimization is illustrated by an offshore wind turbine example. Uncertainty modeling is illustrated by an example where physical, statistical and model uncertainties are estimated....

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

    Energy Technology Data Exchange (ETDEWEB)

    Ferson, Scott [Applied Biomathematics, Setauket, NY (United States); Nelsen, Roger B. [Lewis & Clark College, Portland OR (United States); Hajagos, Janos [Applied Biomathematics, Setauket, NY (United States); Berleant, Daniel J. [Iowa State Univ., Ames, IA (United States); Zhang, Jianzhong [Iowa State Univ., Ames, IA (United States); Tucker, W. Troy [Applied Biomathematics, Setauket, NY (United States); Ginzburg, Lev R. [Applied Biomathematics, Setauket, NY (United States); Oberkampf, William L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    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.

  19. Route Choice Model Based on Game Theory for Commuters

    Directory of Open Access Journals (Sweden)

    Licai Yang

    2016-06-01

    Full Text Available The traffic behaviours of commuters may cause traffic congestion during peak hours. Advanced Traffic Information System can provide dynamic information to travellers. Due to the lack of timeliness and comprehensiveness, the provided information cannot satisfy the travellers’ needs. Since the assumptions of traditional route choice model based on Expected Utility Theory conflict with the actual situation, a route choice model based on Game Theory is proposed to provide reliable route choice to commuters in actual situation in this paper. The proposed model treats the alternative routes as game players and utilizes the precision of predicted information and familiarity of traffic condition to build a game. The optimal route can be generated considering Nash Equilibrium by solving the route choice game. Simulations and experimental analysis show that the proposed model can describe the commuters’ routine route choice decisionexactly and the provided route is reliable.

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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...

  2. A nested recursive logit model for route choice analysis

    DEFF Research Database (Denmark)

    Mai, Tien; Frejinger, Emma; Fosgerau, Mogens

    2015-01-01

    choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination....... The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible......We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link...

  3. A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

    DEFF Research Database (Denmark)

    Have, Christian Theil; Mørk, Søren

    We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... and are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames...

  4. Applying Probabilistic Decision Models to Clinical Trial Design

    Science.gov (United States)

    Smith, Wade P; Phillips, Mark H

    2018-01-01

    Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance.

  5. Bisimulations meet PCTL equivalences for probabilistic automata

    DEFF Research Database (Denmark)

    Song, Lei; Zhang, Lijun; Godskesen, Jens Chr.

    2013-01-01

    Probabilistic automata (PAs) have been successfully applied in formal verification of concurrent and stochastic systems. Efficient model checking algorithms have been studied, where the most often used logics for expressing properties are based on probabilistic computation tree logic (PCTL) and its...

  6. 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

    Science.gov (United States)

    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.

  7. Joint Residence-Workplace Location Choice Model Based on Household Decision Behavior

    Directory of Open Access Journals (Sweden)

    Pengpeng Jiao

    2015-01-01

    Full Text Available Residence location and workplace are the two most important urban land-use types, and there exist strong interdependences between them. Existing researches often assume that one choice dimension is correlated to the other. Using the mixed logit framework, three groups of choice models are developed to illustrate such choice dependencies. First, for all households, this paper presents a basic methodology of the residence location and workplace choice without decision sequence based on the assumption that the two choice behaviors are independent of each other. Second, the paper clusters all households into two groups, choosing residence or workplace first, and formulates the residence location and workplace choice models under the constraint of decision sequence. Third, this paper combines the residence location and workplace together as the choice alternative and puts forward the joint choice model. A questionnaire survey is implemented in Beijing city to collect the data of 1994 households. Estimation results indicate that the joint choice model fits the data significantly better, and the elasticity effects analyses show that the joint choice model reflects the influences of relevant factors to the choice probability well and leads to the job-housing balance.

  8. Consumer choice of theme parks : a conjoint choice model of seasonality effects and variety seeking behavior

    NARCIS (Netherlands)

    Kemperman, A.D.A.M.; Borgers, A.W.J.; Oppewal, H.; Timmermans, H.J.P.

    2000-01-01

    Most existing mathematical models of tourist choice behavior assume that individuals' preferences for choice alternatives remain invariant over time. Although the assumption of invariant preference functions may be reasonable in some choice contexts, this study examines the hypothesis that

  9. PROBABILISTIC MODEL FOR AIRPORT RUNWAY SAFETY AREAS

    Directory of Open Access Journals (Sweden)

    Stanislav SZABO

    2017-06-01

    Full Text Available The Laboratory of Aviation Safety and Security at CTU in Prague has recently started a project aimed at runway protection zones. The probability of exceeding by a certain distance from the runway in common incident/accident scenarios (take-off/landing overrun/veer-off, landing undershoot is being identified relative to the runway for any airport. As a result, the size and position of safety areas around runways are defined for the chosen probability. The basis for probability calculation is a probabilistic model using statistics from more than 1400 real-world cases where jet airplanes have been involved over the last few decades. Other scientific studies have contributed to understanding the issue and supported the model’s application to different conditions.

  10. Reinforcement learning models of risky choice and the promotion of risk-taking by losses disguised as wins in rats.

    Science.gov (United States)

    Marshall, Andrew T; Kirkpatrick, Kimberly

    2017-07-01

    Risky decisions are inherently characterized by the potential to receive gains or incur losses, and these outcomes have distinct effects on subsequent decision-making. One important factor is that individuals engage in loss-chasing, in which the reception of a loss is followed by relatively increased risk-taking. Unfortunately, the mechanisms of loss-chasing are poorly understood, despite the potential importance for understanding pathological choice behavior. The goal of the present experiment was to illuminate the mechanisms governing individual differences in loss-chasing and risky-choice behaviors. Rats chose between a low-uncertainty outcome that always delivered a variable amount of reward and a high-uncertainty outcome that probabilistically delivered reward. Loss-processing and loss-chasing were assessed in the context of losses disguised as wins (LDWs), which are loss outcomes that are presented along with gain-related stimuli. LDWs have been suggested to interfere with adaptive decision-making in humans and thus potentially increase loss-making. Here, the rats presented with LDWs were riskier, in that they made more choices for the high-uncertainty outcome. A series of nonlinear models were fit to individual rats' data to elucidate the possible psychological mechanisms that best account for individual differences in high-uncertainty choices and loss-chasing behaviors. The models suggested that the rats presented with LDWs were more prone to showing a stay bias following high-uncertainty outcomes compared to rats not presented with LDWs. These results collectively suggest that LDWs acquire conditioned reinforcement properties that encourage continued risk-taking and increase loss-chasing following previous high-risk decisions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  11. River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

    Directory of Open Access Journals (Sweden)

    H. Sanikhani

    2016-02-01

    . Different combinations of recorded data were used as the input pattern to streamflow forecasting. Results and Discussion: Application of the used approaches in ensemble form (in order to choice the optimized parameters improved the model accuracy and robustness in prediction. Different statistical criteria including correlation coefficient (R, root mean squared error (RMSE and Nash–Sutcliffe efficiency coefficient (E were used for evaluating the performance of models. The ranges of parameter values to be covered in the ensemble prediction have been identified by some preliminary tests on the calibration set. Since very small values of k have been found to produce unacceptable results due to the presence of noise, the minimum value is fixed at 100 and trial values are taken up to 10000 (k = 100, 200, 300,500, 1000, 2000, 5000, 10000. The values of mare chosen between 1 and 20 and delay time values γ are tested in the range [1,5]. With increasing the discharge values, the width of confidence band increased and the maximum confidence band is related to maximum river flows. In Dizaj station, for ensemble numbers in the range of 50-100, the variation of RMSE is linear. The variation of RMSE in Mashin station is linear for ensemble members in the range of 100-150. It seems the numbers of ensemble members equals to 100 is suitable for pattern construction. The performance of NNPE model was acceptable for two stations. The number of points excluded 95% confidence interval were equal to 108 and 96 for Dizaj and Mashin stations, respectively. The results showed that the performance of model was better in prediction of minimum and median discharge in comparing maximum values. Conclusion: The results confirmed the performance and reliability of applied methods. The results indicated the better performance and lower uncertainty of ensemble method based on nearest neighbor in comparison with probabilistic nonlinear ensemble method. Nash–Sutcliffe model efficiency coefficient (E for

  12. Analysis of uncertainties in a probabilistic seismic hazard estimation, example for France

    International Nuclear Information System (INIS)

    Beauval, C.

    2003-12-01

    This thesis proposes a new methodology that allows to pinpoint the key parameters that control probabilistic seismic hazard assessment (PSHA) and at the same time to quantify the impact of these parameters uncertainties on hazard estimates. Cornell-McGuire's method is used here. First, uncertainties on magnitude and location determinations are modeled and quantified: resulting variability on hazard estimates ranges between 5% and 25% (=COV), depending on the site and the return period. An impact study is then performed, in order to determine the hierarchy between the impacts on hazard of the choices of four other parameters: intensity-magnitude correlation, minimum and maximum magnitudes, the truncation of the attenuation relationship. The results at 34 Hz (PGA) indicate that the maximum magnitude is the less influent parameter (from 100 to 10000 years); whereas the intensity-magnitude correlation and the truncation of ground motion predictions (>2σ) are the controlling parameters at all return periods (up to 30% decrease each at 10000 years). An increase in the minimum magnitude contributing to the hazard, from 3.5 to 4.5, can also produce non-negligible impacts at small return periods (up to 20% decrease of hazard results at 475 years). Finally, the overall variability on hazard estimates due to the combined choices of the four parameters can reach up to 30% (COV, at 34 Hz). For lower frequencies (<5 Hz), the overall variability increases and maximum magnitude becomes a controlling parameter. Therefore, variability of estimates due to catalog uncertainties and to the choices of these four parameters must be taken into account in all probabilistic seismic hazard studies in France. To reduce variability in hazard estimates, future research should concentrate on the elaboration of an appropriate intensity- magnitude correlation, as well as on a more realistic way of taking into account ground motion dispersion. (author)

  13. Probabilistic Learning by Rodent Grid Cells.

    Science.gov (United States)

    Cheung, Allen

    2016-10-01

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

  14. Convex models and probabilistic approach of nonlinear fatigue failure

    International Nuclear Information System (INIS)

    Qiu Zhiping; Lin Qiang; Wang Xiaojun

    2008-01-01

    This paper is concerned with the nonlinear fatigue failure problem with uncertainties in the structural systems. In the present study, in order to solve the nonlinear problem by convex models, the theory of ellipsoidal algebra with the help of the thought of interval analysis is applied. In terms of the inclusion monotonic property of ellipsoidal functions, the nonlinear fatigue failure problem with uncertainties can be solved. A numerical example of 25-bar truss structures is given to illustrate the efficiency of the presented method in comparison with the probabilistic approach

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

    Science.gov (United States)

    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.

  16. Probabilistic Model for Integrated Assessment of the Behavior at the T.D.P. Version 2

    International Nuclear Information System (INIS)

    Hurtado, A.; Eguilior, S.; Recreo, F

    2015-01-01

    This report documents the completion of the first phase of the implementation of the methodology ABACO2G (Bayes Application to Geological Storage of CO2) and the final version of the ABACO2G probabilistic model for the injection phase before its future validation in the experimental field of the Technology Development Plant in Hontom (Burgos). The model, which is based on the determination of the probabilistic risk component of a geological storage of CO2 using the formalism of Bayesian networks and Monte Carlo probability yields quantitative probability functions of the total system CO2 storage and of each one of their subsystems (storage subsystem and the primary seal; secondary containment subsystem and dispersion subsystem or tertiary one); the implementation of the stochastic time evolution of the CO2 plume during the injection period, the stochastic time evolution of the drying front, the probabilistic evolution of the pressure front, decoupled from the CO2 plume progress front, and the implementation of submodels and leakage probability functions through major leakage risk elements (fractures / faults and wells / deep boreholes) which together define the space of events to estimate the risks associated with the CO2 geological storage system. The activities included in this report have been to replace the previous qualitative estimation submodels of former ABACO2G version developed during Phase I of the project ALM-10-017, by analytical, semi-analytical or numerical submodels for the main elements of risk (wells and fractures), to obtain an integrated probabilistic model of a CO2 storage complex in carbonate formations that meets the needs of the integrated behavior evaluation of the Technology Development Plant in Hontomín

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

    Science.gov (United States)

    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.

  18. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

    Science.gov (United States)

    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...

  19. Physical microscopic free-choice model in the framework of a Darwinian approach to quantum mechanics

    Energy Technology Data Exchange (ETDEWEB)

    Baladron, Carlos [Departamento de Fisica Teorica, Atomica y Optica, Universidad de Valladolid, E-47011, Valladolid (Spain)

    2017-06-15

    A compatibilistic model of free choice for a fundamental particle is built within a general framework that explores the possibility that quantum mechanics be the emergent result of generalised Darwinian evolution acting on the abstract landscape of possible physical theories. The central element in this approach is a probabilistic classical Turing machine -basically an information processor plus a randomiser- methodologically associated with every fundamental particle. In this scheme every system acts not under a general law, but as a consequence of the command of a particular, evolved algorithm. This evolved programme enables the particle to algorithmically anticipate possible future world configurations in information space, and as a consequence, without altering the natural forward causal order in physical space, to incorporate elements to the decision making procedure that are neither purely random nor strictly in the past, but in a possible future. (copyright 2016 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  20. Probabilistic model for fatigue crack growth and fracture of welded joints in civil engineering structures

    NARCIS (Netherlands)

    Maljaars, J.; Steenbergen, H.M.G.M.; Vrouwenvelder, A.C.W.M.

    2012-01-01

    This paper presents a probabilistic assessment model for linear elastic fracture mechanics (LEFM). The model allows the determination of the failure probability of a structure subjected to fatigue loading. The distributions of the random variables for civil engineering structures are provided, and

  1. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  2. A deterministic-probabilistic model for contaminant transport. User manual

    Energy Technology Data Exchange (ETDEWEB)

    Schwartz, F W; Crowe, A

    1980-08-01

    This manual describes a deterministic-probabilistic contaminant transport (DPCT) computer model designed to simulate mass transfer by ground-water movement in a vertical section of the earth's crust. The model can account for convection, dispersion, radioactive decay, and cation exchange for a single component. A velocity is calculated from the convective transport of the ground water for each reference particle in the modeled region; dispersion is accounted for in the particle motion by adding a readorn component to the deterministic motion. The model is sufficiently general to enable the user to specify virtually any type of water table or geologic configuration, and a variety of boundary conditions. A major emphasis in the model development has been placed on making the model simple to use, and information provided in the User Manual will permit changes to the computer code to be made relatively easily for those that might be required for specific applications. (author)

  3. Surrogate reservoir models for CSI well probabilistic production forecast

    Directory of Open Access Journals (Sweden)

    Saúl Buitrago

    2017-09-01

    Full Text Available The aim of this work is to present the construction and use of Surrogate Reservoir Models capable of accurately predicting cumulative oil production for every well stimulated with cyclic steam injection at any given time in a heavy oil reservoir in Mexico considering uncertain variables. The central composite experimental design technique was selected to capture the maximum amount of information from the model response with a minimum number of reservoir models simulations. Four input uncertain variables (the dead oil viscosity with temperature, the reservoir pressure, the reservoir permeability and oil sand thickness hydraulically connected to the well were selected as the ones with more impact on the initial hot oil production rate according to an analytical production prediction model. Twenty five runs were designed and performed with the STARS simulator for each well type on the reservoir model. The results show that the use of Surrogate Reservoir Models is a fast viable alternative to perform probabilistic production forecasting of the reservoir.

  4. Statistical analysis of probabilistic models of software product lines with quantitative constraints

    DEFF Research Database (Denmark)

    Beek, M.H. ter; Legay, A.; Lluch Lafuente, Alberto

    2015-01-01

    We investigate the suitability of statistical model checking for the analysis of probabilistic models of software product lines with complex quantitative constraints and advanced feature installation options. Such models are specified in the feature-oriented language QFLan, a rich process algebra...... of certain behaviour to the expected average cost of products. This is supported by a Maude implementation of QFLan, integrated with the SMT solver Z3 and the distributed statistical model checker MultiVeStA. Our approach is illustrated with a bikes product line case study....

  5. Process for computing geometric perturbations for probabilistic analysis

    Science.gov (United States)

    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.

  6. Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

    Science.gov (United States)

    Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

    2018-01-01

    Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

  7. A Probabilistic Asteroid Impact Risk Model

    Science.gov (United States)

    Mathias, Donovan L.; Wheeler, Lorien F.; Dotson, Jessie L.

    2016-01-01

    Asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data has little effect on the metrics of interest.

  8. A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

    Science.gov (United States)

    Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

    2018-05-01

    Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

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

    Science.gov (United States)

    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...

  10. Selective attention increases choice certainty in human decision making.

    Science.gov (United States)

    Zizlsperger, Leopold; Sauvigny, Thomas; Haarmeier, Thomas

    2012-01-01

    Choice certainty is a probabilistic estimate of past performance and expected outcome. In perceptual decisions the degree of confidence correlates closely with choice accuracy and reaction times, suggesting an intimate relationship to objective performance. Here we show that spatial and feature-based attention increase human subjects' certainty more than accuracy in visual motion discrimination tasks. Our findings demonstrate for the first time a dissociation of choice accuracy and certainty with a significantly stronger influence of voluntary top-down attention on subjective performance measures than on objective performance. These results reveal a so far unknown mechanism of the selection process implemented by attention and suggest a unique biological valence of choice certainty beyond a faithful reflection of the decision process.

  11. A Probabilistic Model to Evaluate the Optimal Density of Stations Measuring Snowfall.

    Science.gov (United States)

    Schneebeli, Martin; Laternser, Martin

    2004-05-01

    Daily new snow measurements are very important for avalanche forecasting and tourism. A dense network of manual or automatic stations measuring snowfall is necessary to have spatially reliable data. Snow stations in Switzerland were built at partially subjective locations. A probabilistic model based on the frequency and spatial extent of areas covered by heavy snowfalls was developed to quantify the probability that snowfall events are measured by the stations. Area probability relations were calculated for different thresholds of daily accumulated snowfall. A probabilistic model, including autocorrelation, was used to calculate the optimal spacing of stations based on simulated triangular grids and to compare the capture probability of different networks and snowfall thresholds. The Swiss operational snow-stations network captured snowfall events with high probability, but the distribution of the stations could be optimized. The spatial variability increased with higher thresholds of daily accumulated snowfall, and the capture probability decreased with increasing thresholds. The method can be used for other areas where the area probability relation for threshold values of snow or rain can be calculated.

  12. Modelling life trajectories and mode choice using Bayesian belief networks

    NARCIS (Netherlands)

    Verhoeven, M.

    2010-01-01

    Traditionally, transport mode choice was primarily examined as a stand alone problem. Given a purpose and destination, the choice of transport mode was modelled as a function of the various attributes of the transport mode alternatives. Later, transport mode choice decisions were modelled as part of

  13. Probabilistic versus deterministic hazard assessment in liquefaction susceptible zones

    Science.gov (United States)

    Daminelli, Rosastella; Gerosa, Daniele; Marcellini, Alberto; Tento, Alberto

    2015-04-01

    Probabilistic seismic hazard assessment (PSHA), usually adopted in the framework of seismic codes redaction, is based on Poissonian description of the temporal occurrence, negative exponential distribution of magnitude and attenuation relationship with log-normal distribution of PGA or response spectrum. The main positive aspect of this approach stems into the fact that is presently a standard for the majority of countries, but there are weak points in particular regarding the physical description of the earthquake phenomenon. Factors like site effects, source characteristics like duration of the strong motion and directivity that could significantly influence the expected motion at the site are not taken into account by PSHA. Deterministic models can better evaluate the ground motion at a site from a physical point of view, but its prediction reliability depends on the degree of knowledge of the source, wave propagation and soil parameters. We compare these two approaches in selected sites affected by the May 2012 Emilia-Romagna and Lombardia earthquake, that caused widespread liquefaction phenomena unusually for magnitude less than 6. We focus on sites liquefiable because of their soil mechanical parameters and water table level. Our analysis shows that the choice between deterministic and probabilistic hazard analysis is strongly dependent on site conditions. The looser the soil and the higher the liquefaction potential, the more suitable is the deterministic approach. Source characteristics, in particular the duration of strong ground motion, have long since recognized as relevant to induce liquefaction; unfortunately a quantitative prediction of these parameters appears very unlikely, dramatically reducing the possibility of their adoption in hazard assessment. Last but not least, the economic factors are relevant in the choice of the approach. The case history of 2012 Emilia-Romagna and Lombardia earthquake, with an officially estimated cost of 6 billions

  14. Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

    Directory of Open Access Journals (Sweden)

    Chuan Ding

    2017-01-01

    Full Text Available Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling, the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

  15. Consumer Behavior in the Choice of Mode of Transport: A Case Study in the Toledo-Madrid Corridor

    Science.gov (United States)

    Muro-Rodríguez, Ana I.; Perez-Jiménez, Israel R.; Gutiérrez-Broncano, Santiago

    2017-01-01

    Within the context of the consumption of goods or services the decisions made by individuals involve the choice between a set of discrete alternatives, such as the choice of mode of transport. The methodology for analyzing the consumer behavior are the models of discrete choice based on the Theory of Random Utility. These models are based on the definition of preferences through a utility function that is maximized. These models also denominated of disaggregated demand derived from the decision of a set of individuals, who are formalized by the application of probabilistic models. The objective of this study is to determine the behavior of the consumer in the choice of a service, namely of transport services and in a short-distance corridor, such as Toledo-Madrid. The Toledo-Madrid corridor is characterized by being short distance, with high speed train available within the choice options to get the airport, along with the bus and the car. And where offers of HST and aircraft services can be proposed as complementary modes. By applying disaggregated transport models with revealed preference survey data and declared preferences, one can determine the most important variables involved in the choice and determine the arrangements for payment of individuals. These payment provisions may condition the use of certain transport policies to promote the use of efficient transportation. PMID:28676776

  16. Consumer Behavior in the Choice of Mode of Transport: A Case Study in the Toledo-Madrid Corridor

    Directory of Open Access Journals (Sweden)

    Ana I. Muro-Rodríguez

    2017-06-01

    Full Text Available Within the context of the consumption of goods or services the decisions made by individuals involve the choice between a set of discrete alternatives, such as the choice of mode of transport. The methodology for analyzing the consumer behavior are the models of discrete choice based on the Theory of Random Utility. These models are based on the definition of preferences through a utility function that is maximized. These models also denominated of disaggregated demand derived from the decision of a set of individuals, who are formalized by the application of probabilistic models. The objective of this study is to determine the behavior of the consumer in the choice of a service, namely of transport services and in a short-distance corridor, such as Toledo-Madrid. The Toledo-Madrid corridor is characterized by being short distance, with high speed train available within the choice options to get the airport, along with the bus and the car. And where offers of HST and aircraft services can be proposed as complementary modes. By applying disaggregated transport models with revealed preference survey data and declared preferences, one can determine the most important variables involved in the choice and determine the arrangements for payment of individuals. These payment provisions may condition the use of certain transport policies to promote the use of efficient transportation.

  17. Consumer Behavior in the Choice of Mode of Transport: A Case Study in the Toledo-Madrid Corridor.

    Science.gov (United States)

    Muro-Rodríguez, Ana I; Perez-Jiménez, Israel R; Gutiérrez-Broncano, Santiago

    2017-01-01

    Within the context of the consumption of goods or services the decisions made by individuals involve the choice between a set of discrete alternatives, such as the choice of mode of transport. The methodology for analyzing the consumer behavior are the models of discrete choice based on the Theory of Random Utility. These models are based on the definition of preferences through a utility function that is maximized. These models also denominated of disaggregated demand derived from the decision of a set of individuals, who are formalized by the application of probabilistic models. The objective of this study is to determine the behavior of the consumer in the choice of a service, namely of transport services and in a short-distance corridor, such as Toledo-Madrid. The Toledo-Madrid corridor is characterized by being short distance, with high speed train available within the choice options to get the airport, along with the bus and the car. And where offers of HST and aircraft services can be proposed as complementary modes. By applying disaggregated transport models with revealed preference survey data and declared preferences, one can determine the most important variables involved in the choice and determine the arrangements for payment of individuals. These payment provisions may condition the use of certain transport policies to promote the use of efficient transportation.

  18. On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power

    International Nuclear Information System (INIS)

    Gallego-Castillo, Cristobal; Bessa, Ricardo; Cavalcante, Laura; Lopez-Garcia, Oscar

    2016-01-01

    Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold crossvalidation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead. - Highlights: • New online quantile regression model based on the Reproducing Kernel Hilbert Space. • First application to operational probabilistic wind power forecasting. • Modest improvements of CRPS for prediction horizons between 6 and 20 h ahead. • Noticeable improvements in terms of Calibration due to online learning.

  19. Think twice before you book? Modelling the choice of public vs private dentist in a choice experiment.

    Science.gov (United States)

    Kiiskinen, Urpo; Suominen-Taipale, Anna Liisa; Cairns, John

    2010-06-01

    This study concerns the choice of primary dental service provider by consumers. If the health service delivery system allows individuals to choose between public-care providers or if complementary private services are available, it is typically assumed that utilisation is a three-stage decision process. The patient first makes a decision to seek care, and then chooses the service provider. The final stage, involving decisions over the amount and form of treatment, is not considered here. The paper reports a discrete choice experiment (DCE) designed to evaluate attributes affecting individuals' choice of dental-care provider. The feasibility of the DCE approach in modelling consumers' choice in the context of non-acute need for dental care is assessed. The aim is to test whether a separate two-stage logit, a multinomial logit, or a nested logit best fits the choice process of consumers. A nested logit model of indirect utility functions is estimated and inclusive value (IV) constraints are tested for modelling implications. The results show that non-trading behaviour has an impact on the choice of appropriate modelling technique, but is to some extent dependent on the choice of scenarios offered. It is concluded that for traders multinomial logit is appropriate, whereas for non-traders and on average the nested logit is the method supported by the analyses. The consistent finding in all subgroup analyses is that the traditional two-stage decision process is found to be implausible in the context of consumer's choice of dental-care provider.

  20. Misclassification in binary choice models

    Czech Academy of Sciences Publication Activity Database

    Meyer, B. D.; Mittag, Nikolas

    2017-01-01

    Roč. 200, č. 2 (2017), s. 295-311 ISSN 0304-4076 Institutional support: RVO:67985998 Keywords : measurement error * binary choice models * program take-up Subject RIV: AH - Economics OBOR OECD: Economic Theory Impact factor: 1.633, year: 2016

  1. Probabilistic risk assessment and its role in plant modifications

    International Nuclear Information System (INIS)

    Diederich, A.R.; McElroy, W.F.

    1986-01-01

    Electric Utilities today have a tool available to improve management's ability to evaluate nuclear power plant modifications (MODS). Probabilistic Risk Assessment (PRA), is a tool of choice since it can be applied to a specific situation such as MOD request review, bringing the perspectives of reliability, financial risk and consequences to the public in addition to the more rigid requirements like those associated with Quality Assurance or licensing criteria. The techniques used in the PRA process revolve about the creation and manipulation of Fault Trees and Event Trees, which are used to quantify the event sequences and reliability of plant systems in a logical framework. It is through these methods that chains of sequences, or events, are understood. The degree to which plant systems are modelled in the PRA can vary depending on resources and purpose. Philadelphia Elecrtric Company's PRA modelled ten (10) major systems but this number may increase during the application and updating process

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

    Science.gov (United States)

    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.

  3. Street Choice Logit Model for Visitors in Shopping Districts

    Science.gov (United States)

    Kawada, Ko; Yamada, Takashi; Kishimoto, Tatsuya

    2014-01-01

    In this study, we propose two models for predicting people’s activity. The first model is the pedestrian distribution prediction (or postdiction) model by multiple regression analysis using space syntax indices of urban fabric and people distribution data obtained from a field survey. The second model is a street choice model for visitors using multinomial logit model. We performed a questionnaire survey on the field to investigate the strolling routes of 46 visitors and obtained a total of 1211 street choices in their routes. We proposed a utility function, sum of weighted space syntax indices, and other indices, and estimated the parameters for weights on the basis of maximum likelihood. These models consider both street networks, distance from destination, direction of the street choice and other spatial compositions (numbers of pedestrians, cars, shops, and elevation). The first model explains the characteristics of the street where many people tend to walk or stay. The second model explains the mechanism underlying the street choice of visitors and clarifies the differences in the weights of street choice parameters among the various attributes, such as gender, existence of destinations, number of people, etc. For all the attributes considered, the influences of DISTANCE and DIRECTION are strong. On the other hand, the influences of Int.V, SHOPS, CARS, ELEVATION, and WIDTH are different for each attribute. People with defined destinations tend to choose streets that “have more shops, and are wider and lower”. In contrast, people with undefined destinations tend to choose streets of high Int.V. The choice of males is affected by Int.V, SHOPS, WIDTH (positive) and CARS (negative). Females prefer streets that have many shops, and couples tend to choose downhill streets. The behavior of individual persons is affected by all variables. The behavior of people visiting in groups is affected by SHOP and WIDTH (positive). PMID:25379274

  4. Street Choice Logit Model for Visitors in Shopping Districts

    Directory of Open Access Journals (Sweden)

    Ko Kawada

    2014-07-01

    Full Text Available In this study, we propose two models for predicting people’s activity. The first model is the pedestrian distribution prediction (or postdiction model by multiple regression analysis using space syntax indices of urban fabric and people distribution data obtained from a field survey. The second model is a street choice model for visitors using multinomial logit model. We performed a questionnaire survey on the field to investigate the strolling routes of 46 visitors and obtained a total of 1211 street choices in their routes. We proposed a utility function, sum of weighted space syntax indices, and other indices, and estimated the parameters for weights on the basis of maximum likelihood. These models consider both street networks, distance from destination, direction of the street choice and other spatial compositions (numbers of pedestrians, cars, shops, and elevation. The first model explains the characteristics of the street where many people tend to walk or stay. The second model explains the mechanism underlying the street choice of visitors and clarifies the differences in the weights of street choice parameters among the various attributes, such as gender, existence of destinations, number of people, etc. For all the attributes considered, the influences of DISTANCE and DIRECTION are strong. On the other hand, the influences of Int.V, SHOPS, CARS, ELEVATION, and WIDTH are different for each attribute. People with defined destinations tend to choose streets that “have more shops, and are wider and lower”. In contrast, people with undefined destinations tend to choose streets of high Int.V. The choice of males is affected by Int.V, SHOPS, WIDTH (positive and CARS (negative. Females prefer streets that have many shops, and couples tend to choose downhill streets. The behavior of individual persons is affected by all variables. The behavior of people visiting in groups is affected by SHOP and WIDTH (positive.

  5. A probabilistic model of the electron transport in films of nanocrystals arranged in a cubic lattice

    Energy Technology Data Exchange (ETDEWEB)

    Kriegel, Ilka [Department of Nanochemistry, Istituto Italiano di Tecnologia (IIT), via Morego, 30, 16163 Genova (Italy); Scotognella, Francesco, E-mail: francesco.scotognella@polimi.it [Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Center for Nano Science and Technology@PoliMi, Istituto Italiano di Tecnologia, Via Giovanni Pascoli, 70/3, 20133 Milan (Italy)

    2016-08-01

    The fabrication of nanocrystal (NC) films, starting from colloidal dispersion, is a very attractive topic in condensed matter physics community. NC films can be employed for transistors, light emitting diodes, lasers, and solar cells. For this reason the understanding of the film conductivity is of major importance. In this paper we describe a probabilistic model that allows the prediction of the conductivity of NC films, in this case of a cubic lattice of Lead Selenide or Cadmium Selenide NCs. The model is based on the hopping probability between NCs. The results are compared to experimental data reported in literature. - Highlights: • Colloidal nanocrystal (NC) film conductivity is a topic of major importance. • We present a probabilistic model to predict the electron conductivity in NC films. • The model is based on the hopping probability between NCs. • We found a good agreement between the model and data reported in literature.

  6. Probabilistic inversion in priority setting of emerging zoonoses.

    NARCIS (Netherlands)

    Kurowicka, D.; Bucura, C.; Cooke, R.; Havelaar, A.H.

    2010-01-01

    This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi-criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a

  7. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    2014-02-14

    Feb 14, 2014 ... Application of probabilistic precipitation forecasts from a deterministic model ... aim of this paper is to investigate the increase in the lead-time of flash flood warnings of the SAFFG using probabilistic precipitation forecasts ... The procedure is applied to a real flash flood event and the ensemble-based.

  8. Using Probabilistic Models to Appraise and Decide on Sovereign Disaster Risk Financing and Insurance

    OpenAIRE

    Ley-Borrás, Roberto; Fox, Benjamin D.

    2015-01-01

    This paper presents an overview of the structure of probabilistic catastrophe risk models, discusses their importance for appraising sovereign disaster risk financing and insurance instruments and strategy, and puts forward a model and a process for improving decision making on the linked disaster risk management strategy and sovereign disaster risk financing and insurance strategy. The pa...

  9. Harvest choice and timber supply models for forest forecasting

    Science.gov (United States)

    Maksym Polyakov; David N Wear

    2010-01-01

    Timber supply has traditionally been modeled using aggregate data, whereas individual harvest choices have been shown to be sensitive to the vintage and condition of forest capital stocks. In this article, we build aggregate supply models for four roundwood products in a seven-state region of the US South directly from stand-level harvest choice models applied to...

  10. Probabilistic structural analysis of aerospace components using NESSUS

    Science.gov (United States)

    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.

  11. Local Choices: Rationality and the Contextuality of Decision-Making

    Science.gov (United States)

    Vlaev, Ivo

    2018-01-01

    Rational explanation is ubiquitous in psychology and social sciences, ranging from rational analysis, expectancy-value theories, ideal observer models, mental logic to probabilistic frameworks, rational choice theory, and informal “folk psychological” explanation. However, rational explanation appears to be challenged by apparently systematic irrationality observed in psychological experiments, especially in the field of judgement and decision-making (JDM). Here, it is proposed that the experimental results require not that rational explanation should be rejected, but that rational explanation is local, i.e., within a context. Thus, rational models need to be supplemented with a theory of contextual shifts. We review evidence in JDM that patterns of choices are often consistent within contexts, but unstable between contexts. We also demonstrate that for a limited, though reasonably broad, class of decision-making domains, recent theoretical models can be viewed as providing theories of contextual shifts. It is argued that one particular significant source of global inconsistency arises from a cognitive inability to represent absolute magnitudes, whether for perceptual variables, utilities, payoffs, or probabilities. This overall argument provides a fresh perspective on the scope and limits of human rationality. PMID:29301289

  12. Local Choices: Rationality and the Contextuality of Decision-Making.

    Science.gov (United States)

    Vlaev, Ivo

    2018-01-02

    Rational explanation is ubiquitous in psychology and social sciences, ranging from rational analysis, expectancy-value theories, ideal observer models, mental logic to probabilistic frameworks, rational choice theory, and informal "folk psychological" explanation. However, rational explanation appears to be challenged by apparently systematic irrationality observed in psychological experiments, especially in the field of judgement and decision-making (JDM). Here, it is proposed that the experimental results require not that rational explanation should be rejected, but that rational explanation is local , i.e., within a context. Thus, rational models need to be supplemented with a theory of contextual shifts. We review evidence in JDM that patterns of choices are often consistent within contexts, but unstable between contexts. We also demonstrate that for a limited, though reasonably broad, class of decision-making domains, recent theoretical models can be viewed as providing theories of contextual shifts. It is argued that one particular significant source of global inconsistency arises from a cognitive inability to represent absolute magnitudes, whether for perceptual variables, utilities, payoffs, or probabilities. This overall argument provides a fresh perspective on the scope and limits of human rationality.

  13. Probabilistic calculation of dose commitment from uranium mill tailings

    International Nuclear Information System (INIS)

    1983-10-01

    The report discusses in a general way considerations of uncertainty in relation to probabilistic modelling. An example of a probabilistic calculation applied to the behaviour of uranium mill tailings is given

  14. Probabilistic forward model for electroencephalography source analysis

    International Nuclear Information System (INIS)

    Plis, Sergey M; George, John S; Jun, Sung C; Ranken, Doug M; Volegov, Petr L; Schmidt, David M

    2007-01-01

    Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates

  15. Financial and Real Sector Leading Indicators of Recessions in Brazil Using Probabilistic Models

    Directory of Open Access Journals (Sweden)

    Fernando Nascimento de Oliveira

    Full Text Available We examine the usefulness of various financial and real sector variables to forecast recessions in Brazil between one and eight quarters ahead. We estimate probabilistic models of recession and select models based on their outof-sample forecasts, using the Receiver Operating Characteristic (ROC function. We find that the predictive out-of-sample ability of several models vary depending on the numbers of quarters ahead to forecast and on the number of regressors used in the model specification. The models selected seem to be relevant to give early warnings of recessions in Brazil.

  16. Integration of anatomical and external response mappings explains crossing effects in tactile localization: A probabilistic modeling approach.

    Science.gov (United States)

    Badde, Stephanie; Heed, Tobias; Röder, Brigitte

    2016-04-01

    To act upon a tactile stimulus its original skin-based, anatomical spatial code has to be transformed into an external, posture-dependent reference frame, a process known as tactile remapping. When the limbs are crossed, anatomical and external location codes are in conflict, leading to a decline in tactile localization accuracy. It is unknown whether this impairment originates from the integration of the resulting external localization response with the original, anatomical one or from a failure of tactile remapping in crossed postures. We fitted probabilistic models based on these diverging accounts to the data from three tactile localization experiments. Hand crossing disturbed tactile left-right location choices in all experiments. Furthermore, the size of these crossing effects was modulated by stimulus configuration and task instructions. The best model accounted for these results by integration of the external response mapping with the original, anatomical one, while applying identical integration weights for uncrossed and crossed postures. Thus, the model explained the data without assuming failures of remapping. Moreover, performance differences across tasks were accounted for by non-individual parameter adjustments, indicating that individual participants' task adaptation results from one common functional mechanism. These results suggest that remapping is an automatic and accurate process, and that the observed localization impairments in touch result from a cognitively controlled integration process that combines anatomically and externally coded responses.

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

    Science.gov (United States)

    Chiu, Weihsueh A; Slob, Wout

    2015-12-01

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

  18. Probabilistic insurance

    OpenAIRE

    Wakker, P.P.; Thaler, R.H.; Tversky, A.

    1997-01-01

    textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these preferences are intuitively appealing they are difficult to reconcile with expected utility theory. Under highly plausible assumptions about the utility function, willingness to pay for probabilistic i...

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

    Energy Technology Data Exchange (ETDEWEB)

    Soltanzadeh, I. [Tehran Univ. (Iran, Islamic Republic of). Inst. of Geophysics; Azadi, M.; Vakili, G.A. [Atmospheric Science and Meteorological Research Center (ASMERC), Teheran (Iran, Islamic Republic of)

    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. (orig.)

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

    Directory of Open Access Journals (Sweden)

    I. Soltanzadeh

    2011-07-01

    Full Text Available 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.

  1. Growing hierarchical probabilistic self-organizing graphs.

    Science.gov (United States)

    López-Rubio, Ezequiel; Palomo, Esteban José

    2011-07-01

    Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

  2. Exclusive queueing model including the choice of service windows

    Science.gov (United States)

    Tanaka, Masahiro; Yanagisawa, Daichi; Nishinari, Katsuhiro

    2018-01-01

    In a queueing system involving multiple service windows, choice behavior is a significant concern. This paper incorporates the choice of service windows into a queueing model with a floor represented by discrete cells. We contrived a logit-based choice algorithm for agents considering the numbers of agents and the distances to all service windows. Simulations were conducted with various parameters of agent choice preference for these two elements and for different floor configurations, including the floor length and the number of service windows. We investigated the model from the viewpoint of transit times and entrance block rates. The influences of the parameters on these factors were surveyed in detail and we determined that there are optimum floor lengths that minimize the transit times. In addition, we observed that the transit times were determined almost entirely by the entrance block rates. The results of the presented model are relevant to understanding queueing systems including the choice of service windows and can be employed to optimize facility design and floor management.

  3. Probabilistic simple sticker systems

    Science.gov (United States)

    Selvarajoo, Mathuri; Heng, Fong Wan; Sarmin, Nor Haniza; Turaev, Sherzod

    2017-04-01

    A model for DNA computing using the recombination behavior of DNA molecules, known as a sticker system, was introduced by by L. Kari, G. Paun, G. Rozenberg, A. Salomaa, and S. Yu in the paper entitled DNA computing, sticker systems and universality from the journal of Acta Informatica vol. 35, pp. 401-420 in the year 1998. A sticker system uses the Watson-Crick complementary feature of DNA molecules: starting from the incomplete double stranded sequences, and iteratively using sticking operations until a complete double stranded sequence is obtained. It is known that sticker systems with finite sets of axioms and sticker rules generate only regular languages. Hence, different types of restrictions have been considered to increase the computational power of sticker systems. Recently, a variant of restricted sticker systems, called probabilistic sticker systems, has been introduced [4]. In this variant, the probabilities are initially associated with the axioms, and the probability of a generated string is computed by multiplying the probabilities of all occurrences of the initial strings in the computation of the string. Strings for the language are selected according to some probabilistic requirements. In this paper, we study fundamental properties of probabilistic simple sticker systems. We prove that the probabilistic enhancement increases the computational power of simple sticker systems.

  4. Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

    Directory of Open Access Journals (Sweden)

    Shengchun Yang

    2016-01-01

    Full Text Available Demand response (DR programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL, plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

  5. Evaluation of probabilistic forecasts with the scoringRules package

    Science.gov (United States)

    Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian

    2017-04-01

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

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

    Science.gov (United States)

    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.

  7. Experiencing a probabilistic approach to clarify and disclose uncertainties when setting occupational exposure limits.

    Science.gov (United States)

    Vernez, David; Fraize-Frontier, Sandrine; Vincent, Raymond; Binet, Stéphane; Rousselle, Christophe

    2018-03-15

    Assessment factors (AFs) are commonly used for deriving reference concentrations for chemicals. These factors take into account variabilities as well as uncertainties in the dataset, such as inter-species and intra-species variabilities or exposure duration extrapolation or extrapolation from the lowest-observed-adverse-effect level (LOAEL) to the noobserved- adverse-effect level (NOAEL). In a deterministic approach, the value of an AF is the result of a debate among experts and, often a conservative value is used as a default choice. A probabilistic framework to better take into account uncertainties and/or variability when setting occupational exposure limits (OELs) is presented and discussed in this paper. Each AF is considered as a random variable with a probabilistic distribution. A short literature was conducted before setting default distributions ranges and shapes for each AF commonly used. A random sampling, using Monte Carlo techniques, is then used for propagating the identified uncertainties and computing the final OEL distribution. Starting from the broad default distributions obtained, experts narrow it to its most likely range, according to the scientific knowledge available for a specific chemical. Introducing distribution rather than single deterministic values allows disclosing and clarifying variability and/or uncertainties inherent to the OEL construction process. This probabilistic approach yields quantitative insight into both the possible range and the relative likelihood of values for model outputs. It thereby provides a better support in decision-making and improves transparency. This work is available in Open Access model and licensed under a CC BY-NC 3.0 PL license.

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

    Science.gov (United States)

    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.

  9. Probabilistic Graphical Models for the Analysis and Synthesis of Musical Audio

    Science.gov (United States)

    Hoffmann, Matthew Douglas

    Content-based Music Information Retrieval (MIR) systems seek to automatically extract meaningful information from musical audio signals. This thesis applies new and existing generative probabilistic models to several content-based MIR tasks: timbral similarity estimation, semantic annotation and retrieval, and latent source discovery and separation. In order to estimate how similar two songs sound to one another, we employ a Hierarchical Dirichlet Process (HDP) mixture model to discover a shared representation of the distribution of timbres in each song. Comparing songs under this shared representation yields better query-by-example retrieval quality and scalability than previous approaches. To predict what tags are likely to apply to a song (e.g., "rap," "happy," or "driving music"), we develop the Codeword Bernoulli Average (CBA) model, a simple and fast mixture-of-experts model. Despite its simplicity, CBA performs at least as well as state-of-the-art approaches at automatically annotating songs and finding to what songs in a database a given tag most applies. Finally, we address the problem of latent source discovery and separation by developing two Bayesian nonparametric models, the Shift-Invariant HDP and Gamma Process NMF. These models allow us to discover what sounds (e.g. bass drums, guitar chords, etc.) are present in a song or set of songs and to isolate or suppress individual source. These models' ability to decide how many latent sources are necessary to model the data is particularly valuable in this application, since it is impossible to guess a priori how many sounds will appear in a given song or set of songs. Once they have been fit to data, probabilistic models can also be used to drive the synthesis of new musical audio, both for creative purposes and to qualitatively diagnose what information a model does and does not capture. We also adapt the SIHDP model to create new versions of input audio with arbitrary sample sets, for example, to create

  10. Review of the Brunswick Steam Electric Plant Probabilistic Risk Assessment

    International Nuclear Information System (INIS)

    Sattison, M.B.; Davis, P.R.; Satterwhite, D.G.; Gilmore, W.E.; Gregg, R.E.

    1989-11-01

    A review of the Brunswick Steam Electric Plant probabilistic risk Assessment was conducted with the objective of confirming the safety perspectives brought to light by the probabilistic risk assessment. The scope of the review included the entire Level I probabilistic risk assessment including external events. This is consistent with the scope of the probabilistic risk assessment. The review included an assessment of the assumptions, methods, models, and data used in the study. 47 refs., 14 figs., 15 tabs

  11. 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.

    Science.gov (United States)

    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.

  12. Probabilistic Fatigue Model for Reinforced Concrete Onshore Wind Turbine Foundations

    DEFF Research Database (Denmark)

    Marquez-Dominguez, Sergio; Sørensen, John Dalsgaard

    2013-01-01

    Reinforced Concrete Slab Foundation (RCSF) is the most common onshore wind turbine foundation type installed by the wind industry around the world. Fatigue cracks in a RCSF are an important issue to be considered by the designers. Causes and consequences of the cracks due to fatigue damage in RCSFs...... are discussed in this paper. A probabilistic fatigue model for a RCSF is established which makes a rational treatment of the uncertainties involved in the complex interaction between fatigue cyclic loads and reinforced concrete. Design and limit state equations are established considering concrete shear...

  13. On probabilistic forecasting of wind power time-series

    DEFF Research Database (Denmark)

    Pinson, Pierre

    power dynamics. In both cases, the model parameters are adaptively and recursively estimated, time-adaptativity being the result of exponential forgetting of past observations. The probabilistic forecasting methodology is applied at the Horns Rev wind farm in Denmark, for 10-minute ahead probabilistic...... forecasting of wind power generation. Probabilistic forecasts generated from the proposed methodology clearly have higher skill than those obtained from a classical Gaussian assumption about wind power predictive densities. Corresponding point forecasts also exhibit significantly lower error criteria....

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

    International Nuclear Information System (INIS)

    Bouzat, S.; Farengo, R.

    2005-01-01

    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

  15. Probabilistic numerical discrimination in mice.

    Science.gov (United States)

    Berkay, Dilara; Çavdaroğlu, Bilgehan; Balcı, Fuat

    2016-03-01

    Previous studies showed that both human and non-human animals can discriminate between different quantities (i.e., time intervals, numerosities) with a limited level of precision due to their endogenous/representational uncertainty. In addition, other studies have shown that subjects can modulate their temporal categorization responses adaptively by incorporating information gathered regarding probabilistic contingencies into their time-based decisions. Despite the psychophysical similarities between the interval timing and nonverbal counting functions, the sensitivity of count-based decisions to probabilistic information remains an unanswered question. In the current study, we investigated whether exogenous probabilistic information can be integrated into numerosity-based judgments by mice. In the task employed in this study, reward was presented either after few (i.e., 10) or many (i.e., 20) lever presses, the last of which had to be emitted on the lever associated with the corresponding trial type. In order to investigate the effect of probabilistic information on performance in this task, we manipulated the relative frequency of different trial types across different experimental conditions. We evaluated the behavioral performance of the animals under models that differed in terms of their assumptions regarding the cost of responding (e.g., logarithmically increasing vs. no response cost). Our results showed for the first time that mice could adaptively modulate their count-based decisions based on the experienced probabilistic contingencies in directions predicted by optimality.

  16. Comparing Categorical and Probabilistic Fingerprint Evidence.

    Science.gov (United States)

    Garrett, Brandon; Mitchell, Gregory; Scurich, Nicholas

    2018-04-23

    Fingerprint examiners traditionally express conclusions in categorical terms, opining that impressions do or do not originate from the same source. Recently, probabilistic conclusions have been proposed, with examiners estimating the probability of a match between recovered and known prints. This study presented a nationally representative sample of jury-eligible adults with a hypothetical robbery case in which an examiner opined on the likelihood that a defendant's fingerprints matched latent fingerprints in categorical or probabilistic terms. We studied model language developed by the U.S. Defense Forensic Science Center to summarize results of statistical analysis of the similarity between prints. Participant ratings of the likelihood the defendant left prints at the crime scene and committed the crime were similar when exposed to categorical and strong probabilistic match evidence. Participants reduced these likelihoods when exposed to the weaker probabilistic evidence, but did not otherwise discriminate among the prints assigned different match probabilities. © 2018 American Academy of Forensic Sciences.

  17. Development and Application of a Probabilistic Risk-Benefit Assessment Model for Infant Feeding Integrating Microbiological, Nutritional, and Chemical Components.

    Science.gov (United States)

    Boué, Géraldine; Cummins, Enda; Guillou, Sandrine; Antignac, Jean-Philippe; Le Bizec, Bruno; Membré, Jeanne-Marie

    2017-12-01

    A probabilistic and interdisciplinary risk-benefit assessment (RBA) model integrating microbiological, nutritional, and chemical components was developed for infant milk, with the objective of predicting the health impact of different scenarios of consumption. Infant feeding is a particular concern of interest in RBA as breast milk and powder infant formula have both been associated with risks and benefits related to chemicals, bacteria, and nutrients, hence the model considers these three facets. Cronobacter sakazakii, dioxin-like polychlorinated biphenyls (dl-PCB), and docosahexaenoic acid (DHA) were three risk/benefit factors selected as key issues in microbiology, chemistry, and nutrition, respectively. The present model was probabilistic with variability and uncertainty separated using a second-order Monte Carlo simulation process. In this study, advantages and limitations of undertaking probabilistic and interdisciplinary RBA are discussed. In particular, the probabilistic technique was found to be powerful in dealing with missing data and to translate assumptions into quantitative inputs while taking uncertainty into account. In addition, separation of variability and uncertainty strengthened the interpretation of the model outputs by enabling better consideration and distinction of natural heterogeneity from lack of knowledge. Interdisciplinary RBA is necessary to give more structured conclusions and avoid contradictory messages to policymakers and also to consumers, leading to more decisive food recommendations. This assessment provides a conceptual development of the RBA methodology and is a robust basis on which to build upon. © 2017 Society for Risk Analysis.

  18. Discrete choice modeling of season choice for Minnesota turkey hunters

    Science.gov (United States)

    Schroeder, Susan A.; Fulton, David C.; Cornicelli, Louis; Merchant, Steven S.

    2018-01-01

    Recreational turkey hunting exemplifies the interdisciplinary nature of modern wildlife management. Turkey populations in Minnesota have reached social or biological carrying capacities in many areas, and changes to turkey hunting regulations have been proposed by stakeholders and wildlife managers. This study employed discrete stated choice modeling to enhance understanding of turkey hunter preferences about regulatory alternatives. We distributed mail surveys to 2,500 resident turkey hunters. Results suggest that, compared to season structure and lotteries, additional permits and level of potential interference from other hunters most influenced hunter preferences for regulatory alternatives. Low hunter interference was preferred to moderate or high interference. A second permit issued only to unsuccessful hunters was preferred to no second permit or permits for all hunters. Results suggest that utility is not strictly defined by harvest or an individual's material gain but can involve preference for other outcomes that on the surface do not materially benefit an individual. Discrete stated choice modeling offers wildlife managers an effective way to assess constituent preferences related to new regulations before implementing them. 

  19. Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software.

    Science.gov (United States)

    Lancsar, Emily; Fiebig, Denzil G; Hole, Arne Risa

    2017-07-01

    We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a 'way in' for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics.

  20. A probabilistic model for component-based shape synthesis

    KAUST Repository

    Kalogerakis, Evangelos; Chaudhuri, Siddhartha; Koller, Daphne; Koltun, Vladlen

    2012-01-01

    represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation

  1. Closing the gap between behavior and models in route choice: The role of spatiotemporal constraints and latent traits in choice set formation

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Prato, Carlo Giacomo

    2012-01-01

    not account for individual-related spatiotemporal constraints. This paper reduces the gap by proposing a route choice model incorporating spatiotemporal constraints and latent traits. The proposed approach combines stochastic route generation with a latent variable semi-compensatory model representing......A considerable gap exists between the behavioral paradigm of choice set formation in route choice and its representation in route choice modeling. While travelers form their viable choice set by retaining routes that satisfy spatiotemporal constraints, existing route generation techniques do...

  2. Misclassification in binary choice models

    Czech Academy of Sciences Publication Activity Database

    Meyer, B. D.; Mittag, Nikolas

    2017-01-01

    Roč. 200, č. 2 (2017), s. 295-311 ISSN 0304-4076 R&D Projects: GA ČR(CZ) GJ16-07603Y Institutional support: Progres-Q24 Keywords : measurement error * binary choice models * program take-up Subject RIV: AH - Economics OBOR OECD: Economic Theory Impact factor: 1.633, year: 2016

  3. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    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.

  4. Probabilistic modelling of the damage of geological barriers of the nuclear waste deep storage - ENDOSTON project, final report

    International Nuclear Information System (INIS)

    2010-01-01

    As the corrosion of metallic casings of radioactive waste storage packages releases hydrogen under pressure, and as the overpressure disturbs the stress fields, the authors report the development of methodologies and numerical simulation tools aimed at a better understanding of the mechanisms of development and propagation of crack networks in the geological barrier due to this overpressure. They present a probabilistic model of the formation of crack networks in rocks, with the probabilistic post-processing of a finite element calculation. They describe the modelling of crack propagation and damage in quasi-brittle materials. They present the ENDO-HETEROGENE model for the formation and propagation of cracks in heterogeneous media, describe the integration of the model into the Aster code, and report the model validation (calculation of the stress intensity factor, grid dependence). They finally report a test case of the ENDO-HETEROGENE model

  5. Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.

    Science.gov (United States)

    Marcum, Christopher Steven; Goldring, Megan R; McBride, Colleen M; Persky, Susan

    2018-02-17

    Meal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events. The current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention. Relational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation. Micro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased. Modeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.

  6. A probabilistic topic model for clinical risk stratification from electronic health records.

    Science.gov (United States)

    Huang, Zhengxing; Dong, Wei; Duan, Huilong

    2015-12-01

    Risk stratification aims to provide physicians with the accurate assessment of a patient's clinical risk such that an individualized prevention or management strategy can be developed and delivered. Existing risk stratification techniques mainly focus on predicting the overall risk of an individual patient in a supervised manner, and, at the cohort level, often offer little insight beyond a flat score-based segmentation from the labeled clinical dataset. To this end, in this paper, we propose a new approach for risk stratification by exploring a large volume of electronic health records (EHRs) in an unsupervised fashion. Along this line, this paper proposes a novel probabilistic topic modeling framework called probabilistic risk stratification model (PRSM) based on Latent Dirichlet Allocation (LDA). The proposed PRSM recognizes a patient clinical state as a probabilistic combination of latent sub-profiles, and generates sub-profile-specific risk tiers of patients from their EHRs in a fully unsupervised fashion. The achieved stratification results can be easily recognized as high-, medium- and low-risk, respectively. In addition, we present an extension of PRSM, called weakly supervised PRSM (WS-PRSM) by incorporating minimum prior information into the model, in order to improve the risk stratification accuracy, and to make our models highly portable to risk stratification tasks of various diseases. We verify the effectiveness of the proposed approach on a clinical dataset containing 3463 coronary heart disease (CHD) patient instances. Both PRSM and WS-PRSM were compared with two established supervised risk stratification algorithms, i.e., logistic regression and support vector machine, and showed the effectiveness of our models in risk stratification of CHD in terms of the Area Under the receiver operating characteristic Curve (AUC) analysis. As well, in comparison with PRSM, WS-PRSM has over 2% performance gain, on the experimental dataset, demonstrating that

  7. Probabilistic modeling of caprock leakage from seismic reflection data

    DEFF Research Database (Denmark)

    Zunino, Andrea; Hansen, Thomas Mejer; Bergjofd-Kitterød, Ingjerd

    We illustrate a methodology which helps to perform a leakage risk analysis for a CO2 reservoir based on a consistent, probabilistic approach to geophysical and geostatistical inversion. Generally, risk assessments of storage complexes are based on geological models and simulations of CO2 movement...... within the storage complexes. The geological models are built on top of geophysical data such as seismic surveys, geological information and well logs from the reservoir or nearby regions. The risk assessment of CO2 storage requires a careful analysis which accounts for all sources of uncertainty....... However, at present, no well-defined and consistent method for mapping the true uncertainty related to the geophysical data and how that uncertainty affects the overall risk assessment for the potential storage site is available. To properly quantify the uncertainties and to avoid unrealistic...

  8. Probabilistic Approaches to Video Retrieval

    NARCIS (Netherlands)

    Ianeva, Tzvetanka; Boldareva, L.; Westerveld, T.H.W.; Cornacchia, Roberto; Hiemstra, Djoerd; de Vries, A.P.

    Our experiments for TRECVID 2004 further investigate the applicability of the so-called “Generative Probabilistic Models to video retrieval��?. TRECVID 2003 results demonstrated that mixture models computed from video shot sequences improve the precision of “query by examples��? results when

  9. Transitions in a probabilistic interface growth model

    International Nuclear Information System (INIS)

    Alves, S G; Moreira, J G

    2011-01-01

    We study a generalization of the Wolf–Villain (WV) interface growth model based on a probabilistic growth rule. In the WV model, particles are randomly deposited onto a substrate and subsequently move to a position nearby where the binding is strongest. We introduce a growth probability which is proportional to a power of the number n i of bindings of the site i: p i ∝n i ν . Through extensive simulations, in (1 + 1) dimensions, we find three behaviors depending on the ν value: (i) if ν is small, a crossover from the Mullins–Herring to the Edwards–Wilkinson (EW) universality class; (ii) for intermediate values of ν, a crossover from the EW to the Kardar–Parisi–Zhang (KPZ) universality class; and, finally, (iii) for large ν values, the system is always in the KPZ class. In (2 + 1) dimensions, we obtain three different behaviors: (i) a crossover from the Villain–Lai–Das Sarma to the EW universality class for small ν values; (ii) the EW class is always present for intermediate ν values; and (iii) a deviation from the EW class is observed for large ν values

  10. Tractable approximations for probabilistic models: The adaptive Thouless-Anderson-Palmer mean field approach

    DEFF Research Database (Denmark)

    Opper, Manfred; Winther, Ole

    2001-01-01

    We develop an advanced mean held method for approximating averages in probabilistic data models that is based on the Thouless-Anderson-Palmer (TAP) approach of disorder physics. In contrast to conventional TAP. where the knowledge of the distribution of couplings between the random variables...... is required. our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is so far restricted to models with nonglassy behavior? by replica calculations for a wide class of models as well as by simulations for a real data set....

  11. Probabilistic models of population evolution scaling limits, genealogies and interactions

    CERN Document Server

    Pardoux, Étienne

    2016-01-01

    This expository book presents the mathematical description of evolutionary models of populations subject to interactions (e.g. competition) within the population. The author includes both models of finite populations, and limiting models as the size of the population tends to infinity. The size of the population is described as a random function of time and of the initial population (the ancestors at time 0). The genealogical tree of such a population is given. Most models imply that the population is bound to go extinct in finite time. It is explained when the interaction is strong enough so that the extinction time remains finite, when the ancestral population at time 0 goes to infinity. The material could be used for teaching stochastic processes, together with their applications. Étienne Pardoux is Professor at Aix-Marseille University, working in the field of Stochastic Analysis, stochastic partial differential equations, and probabilistic models in evolutionary biology and population genetics. He obtai...

  12. Choices and changes: Eccles' Expectancy-Value model and upper-secondary school students' longitudinal reflections about their choice of a STEM education

    Science.gov (United States)

    Lykkegaard, Eva; Ulriksen, Lars

    2016-03-01

    During the past 30 years, Eccles' comprehensive social-psychological Expectancy-Value Model of Motivated Behavioural Choices (EV-MBC model) has been proven suitable for studying educational choices related to Science, Technology, Engineering and/or Mathematics (STEM). The reflections of 15 students in their last year in upper-secondary school concerning their choice of tertiary education were examined using quantitative EV-MBC surveys and repeated qualitative interviews. This article presents the analyses of three cases in detail. The analytical focus was whether the factors indicated in the EV-MBC model could be used to detect significant changes in the students' educational choice processes. An important finding was that the quantitative EV-MBC surveys and the qualitative interviews gave quite different results concerning the students' considerations about the choice of tertiary education, and that significant changes in the students' reflections were not captured by the factors of the EV-MBC model. This questions the validity of the EV-MBC surveys. Moreover, the quantitative factors from the EV-MBC model did not sufficiently explain students' dynamical educational choice processes where students in parallel considered several different potential educational trajectories. We therefore call for further studies of the EV-MBC model's use in describing longitudinal choice processes and especially in investigating significant changes.

  13. Probabilistic Structural Analysis of SSME Turbopump Blades: Probabilistic Geometry Effects

    Science.gov (United States)

    Nagpal, V. K.

    1985-01-01

    A probabilistic study was initiated to evaluate the precisions of the geometric and material properties tolerances on the structural response of turbopump blades. To complete this study, a number of important probabilistic variables were identified which are conceived to affect the structural response of the blade. In addition, a methodology was developed to statistically quantify the influence of these probabilistic variables in an optimized way. The identified variables include random geometric and material properties perturbations, different loadings and a probabilistic combination of these loadings. Influences of these probabilistic variables are planned to be quantified by evaluating the blade structural response. Studies of the geometric perturbations were conducted for a flat plate geometry as well as for a space shuttle main engine blade geometry using a special purpose code which uses the finite element approach. Analyses indicate that the variances of the perturbations about given mean values have significant influence on the response.

  14. Probabilistic modeling of dietary intake of substances - The risk management question governs the method

    NARCIS (Netherlands)

    Pieters MN; Ossendorp BC; Bakker MI; Slob W; SIR

    2005-01-01

    In this report the discussion on the use of probabilistic modeling in relation to pesticide use in food crops is analyzed. Due to different policy questions the current discussion is complex and considers safety of an MRL as well as probability of a health risk. The question regarding the use of

  15. A Probabilistic Framework for Security Scenarios with Dependent Actions

    NARCIS (Netherlands)

    Kordy, Barbara; Pouly, Marc; Schweizer, Patrick; Albert, Elvira; Sekereinsk, Emil

    2014-01-01

    This work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform

  16. A state-based probabilistic model for tumor respiratory motion prediction

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  17. Human-Guided Learning for Probabilistic Logic Models

    Directory of Open Access Journals (Sweden)

    Phillip Odom

    2018-06-01

    Full Text Available Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final structure of the model.

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

    Science.gov (United States)

    Slob, Wout

    2015-01-01

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

  19. Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Kieran R Campbell

    2017-03-01

    Full Text Available 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.

  20. Development of discrete choice model considering internal reference points and their effects in travel mode choice context

    Science.gov (United States)

    Sarif; Kurauchi, Shinya; Yoshii, Toshio

    2017-06-01

    In the conventional travel behavior models such as logit and probit, decision makers are assumed to conduct the absolute evaluations on the attributes of the choice alternatives. On the other hand, many researchers in cognitive psychology and marketing science have been suggesting that the perceptions of attributes are characterized by the benchmark called “reference points” and the relative evaluations based on them are often employed in various choice situations. Therefore, this study developed a travel behavior model based on the mental accounting theory in which the internal reference points are explicitly considered. A questionnaire survey about the shopping trip to the CBD in Matsuyama city was conducted, and then the roles of reference points in travel mode choice contexts were investigated. The result showed that the goodness-of-fit of the developed model was higher than that of the conventional model, indicating that the internal reference points might play the major roles in the choice of travel mode. Also shown was that the respondents seem to utilize various reference points: some tend to adopt the lowest fuel price they have experienced, others employ fare price they feel in perceptions of the travel cost.

  1. On Probabilistic Alpha-Fuzzy Fixed Points and Related Convergence Results in Probabilistic Metric and Menger Spaces under Some Pompeiu-Hausdorff-Like Probabilistic Contractive Conditions

    OpenAIRE

    De la Sen, M.

    2015-01-01

    In the framework of complete probabilistic metric spaces and, in particular, in probabilistic Menger spaces, this paper investigates some relevant properties of convergence of sequences to probabilistic α-fuzzy fixed points under some types of probabilistic contractive conditions.

  2. Probabilistic model for untargeted peak detection in LC-MS using Bayesian statistics

    NARCIS (Netherlands)

    Woldegebriel, M.; Vivó-Truyols, G.

    2015-01-01

    We introduce a novel Bayesian probabilistic peak detection algorithm for liquid chromatography mass spectroscopy (LC-MS). The final probabilistic result allows the user to make a final decision about which points in a 2 chromatogram are affected by a chromatographic peak and which ones are only

  3. Probabilistic image processing by means of the Bethe approximation for the Q-Ising model

    International Nuclear Information System (INIS)

    Tanaka, Kazuyuki; Inoue, Jun-ichi; Titterington, D M

    2003-01-01

    The framework of Bayesian image restoration for multi-valued images by means of the Q-Ising model with nearest-neighbour interactions is presented. Hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. A practical algorithm is described for multi-valued image restoration based on the Bethe approximation. The algorithm corresponds to loopy belief propagation in artificial intelligence. We conclude that, in real world grey-level images, the Q-Ising model can give us good results

  4. Probabilistic Insurance

    NARCIS (Netherlands)

    Wakker, P.P.; Thaler, R.H.; Tversky, A.

    1997-01-01

    Probabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in premium to compensate for a 1% default risk. These observations cannot be

  5. Probabilistic Insurance

    NARCIS (Netherlands)

    P.P. Wakker (Peter); R.H. Thaler (Richard); A. Tversky (Amos)

    1997-01-01

    textabstractProbabilistic insurance is an insurance policy involving a small probability that the consumer will not be reimbursed. Survey data suggest that people dislike probabilistic insurance and demand more than a 20% reduction in the premium to compensate for a 1% default risk. While these

  6. A Simulation-Based Dynamic Stochastic Route Choice Model for Evacuation

    Directory of Open Access Journals (Sweden)

    Xing Zhao

    2012-01-01

    Full Text Available This paper establishes a dynamic stochastic route choice model for evacuation to simulate the propagation process of traffic flow and estimate the stochastic route choice under evacuation situations. The model contains a lane-group-based cell transmission model (CTM which sets different traffic capacities for links with different turning movements to flow out in an evacuation situation, an actual impedance model which is to obtain the impedance of each route in time units at each time interval and a stochastic route choice model according to the probit-based stochastic user equilibrium. In this model, vehicles loading at each origin at each time interval are assumed to choose an evacuation route under determinate road network, signal design, and OD demand. As a case study, the proposed model is validated on the network nearby Nanjing Olympic Center after the opening ceremony of the 10th National Games of the People's Republic of China. The traffic volumes and clearing time at five exit points of the evacuation zone are calculated by the model to compare with survey data. The results show that this model can appropriately simulate the dynamic route choice and evolution process of the traffic flow on the network in an evacuation situation.

  7. Identifiability of tree-child phylogenetic networks under a probabilistic recombination-mutation model of evolution.

    Science.gov (United States)

    Francis, Andrew; Moulton, Vincent

    2018-06-07

    Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

    KAUST Repository

    Chikalov, Igor

    2011-02-15

    Background: 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.Methods: 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 ?. We model dependence of the output variable on the predictors by a regression tree.Results: 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.Conclusions: 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. 2011 Chikalov et al; licensee BioMed Central Ltd.

  9. Probabilistic Modeling of Seismic Risk Based Design for a Dual System Structure

    OpenAIRE

    Sidi, Indra Djati

    2017-01-01

    The dual system structure concept has gained popularity in the construction of high-rise buildings over the last decades. Meanwhile, earthquake engineering design provisions for buildings have moved from the uniform hazard concept to the uniform risk concept upon recognizing the uncertainties involved in the earthquake resistance of concrete structures. In this study, a probabilistic model for the evaluation of such risk is proposed for a dual system structure consisting of shear walls or cor...

  10. Airport choice model in multiple airport regions

    Directory of Open Access Journals (Sweden)

    Claudia Muñoz

    2017-02-01

    Full Text Available Purpose: This study aims to analyze travel choices made by air transportation users in multi airport regions because it is a crucial component when planning passenger redistribution policies. The purpose of this study is to find a utility function which makes it possible to know the variables that influence users’ choice of the airports on routes to the main cities in the Colombian territory. Design/methodology/approach: This research generates a Multinomial Logit Model (MNL, which is based on the theory of maximizing utility, and it is based on the data obtained on revealed and stated preference surveys applied to users who reside in the metropolitan area of Aburrá Valley (Colombia. This zone is the only one in the Colombian territory which has two neighboring airports for domestic flights. The airports included in the modeling process were Enrique Olaya Herrera (EOH Airport and José María Córdova (JMC Airport. Several structure models were tested, and the MNL proved to be the most significant revealing the common variables that affect passenger airport choice include the airfare, the price to travel the airport, and the time to get to the airport. Findings and Originality/value: The airport choice model which was calibrated corresponds to a valid powerful tool used to calculate the probability of each analyzed airport of being chosen for domestic flights in the Colombian territory. This is done bearing in mind specific characteristic of each of the attributes contained in the utility function. In addition, these probabilities will be used to calculate future market shares of the two airports considered in this study, and this will be done generating a support tool for airport and airline marketing policies.

  11. Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization

    International Nuclear Information System (INIS)

    Mohaghegh, Zahra; Kazemi, Reza; Mosleh, Ali

    2009-01-01

    This paper is a result of a research with the primary purpose of extending Probabilistic Risk Assessment (PRA) modeling frameworks to include the effects of organizational factors as the deeper, more fundamental causes of accidents and incidents. There have been significant improvements in the sophistication of quantitative methods of safety and risk assessment, but the progress on techniques most suitable for organizational safety risk frameworks has been limited. The focus of this paper is on the choice of 'representational schemes' and 'techniques.' A methodology for selecting appropriate candidate techniques and their integration in the form of a 'hybrid' approach is proposed. Then an example is given through an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT) in order to demonstrate the feasibility and value of hybrid techniques. The proposed hybrid approach integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool for complex socio-technical systems. An application of the hybrid technique is provided in the aviation safety domain, focusing on airline maintenance systems. The example demonstrates how the hybrid method can be used to analyze the dynamic effects of organizational factors on system risk

  12. PBDE exposure from food in Ireland: optimising data exploitation in probabilistic exposure modelling.

    Science.gov (United States)

    Trudel, David; Tlustos, Christina; Von Goetz, Natalie; Scheringer, Martin; Hungerbühler, Konrad

    2011-01-01

    Polybrominated diphenyl ethers (PBDEs) are a class of brominated flame retardants added to plastics, polyurethane foam, electronics, textiles, and other products. These products release PBDEs into the indoor and outdoor environment, thus causing human exposure through food and dust. This study models PBDE dose distributions from ingestion of food for Irish adults on congener basis by using two probabilistic and one semi-deterministic method. One of the probabilistic methods was newly developed and is based on summary statistics of food consumption combined with a model generating realistic daily energy supply from food. Median (intermediate) doses of total PBDEs are in the range of 0.4-0.6 ng/kg(bw)/day for Irish adults. The 97.5th percentiles of total PBDE doses lie in a range of 1.7-2.2 ng/kg(bw)/day, which is comparable to doses derived for Belgian and Dutch adults. BDE-47 and BDE-99 were identified as the congeners contributing most to estimated intakes, accounting for more than half of the total doses. The most influential food groups contributing to this intake are lean fish and salmon which together account for about 22-25% of the total doses.

  13. DYNAMIC SOFTWARE TESTING MODELS WITH PROBABILISTIC PARAMETERS FOR FAULT DETECTION AND ERLANG DISTRIBUTION FOR FAULT RESOLUTION DURATION

    Directory of Open Access Journals (Sweden)

    A. D. Khomonenko

    2016-07-01

    Full Text Available Subject of Research.Software reliability and test planning models are studied taking into account the probabilistic nature of error detection and discovering. Modeling of software testing enables to plan the resources and final quality at early stages of project execution. Methods. Two dynamic models of processes (strategies are suggested for software testing, using error detection probability for each software module. The Erlang distribution is used for arbitrary distribution approximation of fault resolution duration. The exponential distribution is used for approximation of fault resolution discovering. For each strategy, modified labeled graphs are built, along with differential equation systems and their numerical solutions. The latter makes it possible to compute probabilistic characteristics of the test processes and states: probability states, distribution functions for fault detection and elimination, mathematical expectations of random variables, amount of detected or fixed errors. Evaluation of Results. Probabilistic characteristics for software development projects were calculated using suggested models. The strategies have been compared by their quality indexes. Required debugging time to achieve the specified quality goals was calculated. The calculation results are used for time and resources planning for new projects. Practical Relevance. The proposed models give the possibility to use the reliability estimates for each individual module. The Erlang approximation removes restrictions on the use of arbitrary time distribution for fault resolution duration. It improves the accuracy of software test process modeling and helps to take into account the viability (power of the tests. With the use of these models we can search for ways to improve software reliability by generating tests which detect errors with the highest probability.

  14. COMPONENT SUPPLY MODEL FOR REPAIR ACTIVITIES NETWORK UNDER CONDITIONS OF PROBABILISTIC INDEFINITENESS.

    Directory of Open Access Journals (Sweden)

    Victor Yurievich Stroganov

    2017-02-01

    Full Text Available This article contains the systematization of the major production functions of repair activities network and the list of planning and control functions, which are described in the form of business processes (BP. Simulation model for analysis of the delivery effectiveness of components under conditions of probabilistic uncertainty was proposed. It has been shown that a significant portion of the total number of business processes is represented by the management and planning of the parts and components movement. Questions of construction of experimental design techniques on the simulation model in the conditions of non-stationarity were considered.

  15. Value-based choice: An integrative, neuroscience-informed model of health goals.

    Science.gov (United States)

    Berkman, Elliot T

    2018-01-01

    Traditional models of health behaviour focus on the roles of cognitive, personality and social-cognitive constructs (e.g. executive function, grit, self-efficacy), and give less attention to the process by which these constructs interact in the moment that a health-relevant choice is made. Health psychology needs a process-focused account of how various factors are integrated to produce the decisions that determine health behaviour. I present an integrative value-based choice model of health behaviour, which characterises the mechanism by which a variety of factors come together to determine behaviour. This model imports knowledge from research on behavioural economics and neuroscience about how choices are made to the study of health behaviour, and uses that knowledge to generate novel predictions about how to change health behaviour. I describe anomalies in value-based choice that can be exploited for health promotion, and review neuroimaging evidence about the involvement of midline dopamine structures in tracking and integrating value-related information during choice. I highlight how this knowledge can bring insights to health psychology using illustrative case of healthy eating. Value-based choice is a viable model for health behaviour and opens new avenues for mechanism-focused intervention.

  16. bayesPop: Probabilistic Population Projections

    Directory of Open Access Journals (Sweden)

    Hana Ševčíková

    2016-12-01

    Full Text Available 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.

  17. bayesPop: Probabilistic Population Projections

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  20. Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization

    NARCIS (Netherlands)

    Voet, van der H.; Slob, W.

    2007-01-01

    A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a

  1. Behavioural Models for Route Choice of Passengers in Multimodal Public Transport Networks

    DEFF Research Database (Denmark)

    Anderson, Marie Karen

    in the estimation of route choice models of public transport users based upon observed choices. Public transport route choice models have not benefitted from the same technological enhancements as car models because of the necessity (i) to collect additional information concerning lines and transfers, and (ii...... modes, public transport modes, lines, transfers, egress modes) is large. This thesis proposes a doubly stochastic approach for generating alternative routes that are relevant to travellers, since the method allows accounting for both perceived costs of the network elements and heterogeneity......The subject of this thesis is behavioural models for route choice of passengers in multimodal public transport networks. While research in sustainable transport has dedicated much attention toward the determinants of choice between car and sustainable travel options, it has devoted less attention...

  2. Probabilistic fatigue life of balsa cored sandwich composites subjected to transverse shear

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Berggreen, Christian

    2015-01-01

    A probabilistic fatigue life model for end-grain balsa cored sandwich composites subjectedto transverse shear is proposed. The model is calibrated to measured three-pointbending constant-amplitude fatigue test data using the maximum likelihood method. Some possible applications of the probabilistic...

  3. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    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

  4. Probabilistic Networks

    DEFF Research Database (Denmark)

    Jensen, Finn Verner; Lauritzen, Steffen Lilholt

    2001-01-01

    This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs.......This article describes the basic ideas and algorithms behind specification and inference in probabilistic networks based on directed acyclic graphs, undirected graphs, and chain graphs....

  5. Probabilistic modeling of the flows and environmental risks of nano-silica

    International Nuclear Information System (INIS)

    Wang, Yan; Kalinina, Anna; Sun, Tianyin; Nowack, Bernd

    2016-01-01

    Nano-silica, the engineered nanomaterial with one of the largest production volumes, has a wide range of applications in consumer products and industry. This study aimed to quantify the exposure of nano-silica to the environment and to assess its risk to surface waters. Concentrations were calculated for four environmental (air, soil, surface water, sediments) and two technical compartments (wastewater, solid waste) for the EU and Switzerland using probabilistic material flow modeling. The corresponding median concentration in surface water is predicted to be 0.12 μg/l in the EU (0.053–3.3 μg/l, 15/85% quantiles). The concentrations in sediments in the complete sedimentation scenario were found to be the largest among all environmental compartments, with a median annual increase of 0.43 mg/kg·y in the EU (0.19–12 mg/kg·y, 15/85% quantiles). Moreover, probabilistic species sensitivity distributions (PSSD) were computed and the risk of nano-silica in surface waters was quantified by comparing the predicted environmental concentration (PEC) with the predicted no-effect concentration (PNEC) distribution, which was derived from the cumulative PSSD. This assessment suggests that nano-silica currently poses no risk to aquatic organisms in surface waters. Further investigations are needed to assess the risk of nano-silica in other environmental compartments, which is currently not possible due to a lack of ecotoxicological data. - Highlights: • We quantify the exposure of nano-silica to technical systems and the environment. • The median concentration in surface waters is predicted to be 0.12 μg/L in the EU. • Probabilistic species sensitivity distributions were computed for surface waters. • The risk assessment suggests that nano-silica poses no risk to aquatic organisms.

  6. Discrete choice models with multiplicative error terms

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; Bierlaire, Michel

    2009-01-01

    The conditional indirect utility of many random utility maximization (RUM) discrete choice models is specified as a sum of an index V depending on observables and an independent random term ε. In general, the universe of RUM consistent models is much larger, even fixing some specification of V due...

  7. Dependencies, human interactions and uncertainties in probabilistic safety assessment

    International Nuclear Information System (INIS)

    Hirschberg, S.

    1990-01-01

    In the context of Probabilistic Safety Assessment (PSA), three areas were investigated in a 4-year Nordic programme: dependencies with special emphasis on common cause failures, human interactions and uncertainty aspects. The approach was centered around comparative analyses in form of Benchmark/Reference Studies and retrospective reviews. Weak points in available PSAs were identified and recommendations were made aiming at improving consistency of the PSAs. The sensitivity of PSA-results to basic assumptions was demonstrated and the sensitivity to data assignment and to choices of methods for analysis of selected topics was investigated. (author)

  8. a Probabilistic Embedding Clustering Method for Urban Structure Detection

    Science.gov (United States)

    Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

    2017-09-01

    Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  9. A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

    Directory of Open Access Journals (Sweden)

    X. Lin

    2017-09-01

    Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

  10. Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices

    OpenAIRE

    Hiroyuki Kasahara; Katsumi Shimotsu

    2006-01-01

    In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...

  11. Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions

    Directory of Open Access Journals (Sweden)

    Steven T Piantadosi

    2015-04-01

    Full Text Available Economists often model choices as if decision-makers assign each option a scalar value variable, known as utility, and then select the option with the highest utility. It remains unclear whether as-if utility models describe real mental and neural steps in choice. Although choices alone cannot prove the existence of a utility stage in choice, utility transformations are often taken to provide the most parsimonious or psychologically plausible explanation for choice data. Here, we show that it is possible to mathematically transform a large set of common utility-stage two-option choice models (specifically ones in which dimensions are linearly separable into a psychologically plausible heuristic model (specifically, a dimensional prioritization heuristic that has no utility computation stage. We then show that under a range of plausible assumptions, both classes of model predict similar neural responses. These results highlight the difficulties in using neuroeconomic data to infer the existence of a value stage in choice.

  12. Review of models for use in probabilistic assessments of the disposal of high level radioactive wastes on or beneath the seabed

    International Nuclear Information System (INIS)

    Hooper, A.G.; Gibson, A.E.

    1985-08-01

    The report describes a review of computer models applicable to probabilistic assessments of the ocean disposal of vitrified high level waste. Computer models were identified and each evaluated against a common set of criteria. The review covers each stage of the chain of radionuclide transport: canister deterioration and leaching, migration through the bottom sediments, dispersion in the ocean, suspended sediment effects, biological aspects and uptake by man. The models identified are generally at a relatively early stage of development and documentation was often only of a general nature. These overall systems models and sub-models considered to be most suitable for probabilistic assessments were identified and areas of further development identified. (author)

  13. A Stochastic Lagrangian Basis for a Probabilistic Parameterization of Moisture Condensation in Eulerian Models

    OpenAIRE

    Tsang, Yue-Kin; Vallis, Geoffrey K.

    2018-01-01

    In this paper we describe the construction of an efficient probabilistic parameterization that could be used in a coarse-resolution numerical model in which the variation of moisture is not properly resolved. An Eulerian model using a coarse-grained field on a grid cannot properly resolve regions of saturation---in which condensation occurs---that are smaller than the grid boxes. Thus, in the absence of a parameterization scheme, either the grid box must become saturated or condensation will ...

  14. Convolution product construction of interactions in probabilistic physical models

    International Nuclear Information System (INIS)

    Ratsimbarison, H.M.; Raboanary, R.

    2007-01-01

    This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.

  15. A probabilistic model-based soft sensor to monitor lactic acid bacteria fermentations

    DEFF Research Database (Denmark)

    Spann, Robert; Roca, Christophe; Kold, David

    2018-01-01

    A probabilistic soft sensor based on a mechanistic model was designed to monitor S. thermophilus fermentations, and validated with experimental lab-scale data. It considered uncertainties in the initial conditions, on-line measurements, and model parameters by performing Monte Carlo simulations...... the model parameters that were then used as input to the mechanistic model. The soft sensor predicted both the current state variables, as well as the future course of the fermentation, e.g. with a relative mean error of the biomass concentration of 8 %. This successful implementation of a process...... within the monitoring system. It predicted, therefore, the probability distributions of the unmeasured states, such as biomass, lactose, and lactic acid concentrations. To this end, a mechanistic model was developed first, and a statistical parameter estimation was performed in order to assess parameter...

  16. Probabilistic assessment of dry transport with burnup credit

    International Nuclear Information System (INIS)

    Lake, W.H.

    2003-01-01

    The general concept of probabilistic analysis and its application to the use of burnup credit in spent fuel transport is explored. Discussion of the probabilistic analysis method is presented. The concepts of risk and its perception are introduced, and models are suggested for performing probability and risk estimates. The general probabilistic models are used for evaluating the application of burnup credit for dry spent nuclear fuel transport. Two basic cases are considered. The first addresses the question of the relative likelihood of exceeding an established criticality safety limit with and without burnup credit. The second examines the effect of using burnup credit on the overall risk for dry spent fuel transport. Using reasoned arguments and related failure probability and consequence data analysis is performed to estimate the risks of using burnup credit for dry transport of spent nuclear fuel. (author)

  17. On Probabilistic Automata in Continuous Time

    DEFF Research Database (Denmark)

    Eisentraut, Christian; Hermanns, Holger; Zhang, Lijun

    2010-01-01

    We develop a compositional behavioural model that integrates a variation of probabilistic automata into a conservative extension of interactive Markov chains. The model is rich enough to embody the semantics of generalised stochastic Petri nets. We define strong and weak bisimulations and discuss...

  18. Probabilistic metric spaces

    CERN Document Server

    Schweizer, B

    2005-01-01

    Topics include special classes of probabilistic metric spaces, topologies, and several related structures, such as probabilistic normed and inner-product spaces. 1983 edition, updated with 3 new appendixes. Includes 17 illustrations.

  19. A simplified model of choice behavior under uncertainty

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lin

    2016-08-01

    Full Text Available The Iowa Gambling Task (IGT has been standardized as a clinical assessment tool (Bechara, 2007. Nonetheless, numerous research groups have attempted to modify IGT models to optimize parameters for predicting the choice behavior of normal controls and patients. A decade ago, most researchers considered the expected utility (EU model (Busemeyer and Stout, 2002 to be the optimal model for predicting choice behavior under uncertainty. However, in recent years, studies have demonstrated the prospect utility (PU models (Ahn et al., 2008 to be more effective than the EU models in the IGT. Nevertheless, after some preliminary tests, we propose that Ahn et al. (2008 PU model is not optimal due to some incompatible results between our behavioral and modeling data. This study aims to modify Ahn et al. (2008 PU model to a simplified model and collected 145 subjects’ IGT performance as the benchmark data for comparison. In our simplified PU model, the best goodness-of-fit was found mostly while α approaching zero. More specifically, we retested the key parameters α, λ , and A in the PU model. Notably, the power of influence of the parameters α, λ, and A has a hierarchical order in terms of manipulating the goodness-of-fit in the PU model. Additionally, we found that the parameters λ and A may be ineffective when the parameter α is close to zero in the PU model. The present simplified model demonstrated that decision makers mostly adopted the strategy of gain-stay-loss-shift rather than foreseeing the long-term outcome. However, there still have other behavioral variables that are not well revealed under these dynamic uncertainty situations. Therefore, the optimal behavioral models may not have been found. In short, the best model for predicting choice behavior under dynamic-uncertainty situations should be further evaluated.

  20. Models of Affective Decision Making: How Do Feelings Predict Choice?

    Science.gov (United States)

    Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

    2016-06-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

  1. Choices and Changes: Eccles’ Expectancy-Value Model and Upper-Secondary School Students’ Longitudinal Reflections about their Choice of a STEM Education

    DEFF Research Database (Denmark)

    Lykkegaard, Eva; Ulriksen, Lars

    2016-01-01

    During the past 30 years, Eccles’ comprehensive social-psychological Expectancy-Value Model of Motivated Behavioural Choices (EV-MBC model) has been proven suitable for studying educational choices related to Science, Technology, Engineering and/or Mathematics (STEM). The reflections of 15 students...... in their last year in upper-secondary school concerning their choice of tertiary education were examined using quantitative EV-MBC surveys and repeated qualitative interviews. This article presents the analyses of three cases in detail. The analytical focus was whether the factors indicated in the EV-MBC model......, and that significant changes in the students’ reflections were not captured by the factors of the EV-MBC model. This questions the validity of the EVMBC surveys. Moreover, the quantitative factors from the EV-MBC model did not sufficiently explain students’ dynamical educational choice processes where students...

  2. Probabilistic record linkage.

    Science.gov (United States)

    Sayers, Adrian; Ben-Shlomo, Yoav; Blom, Ashley W; Steele, Fiona

    2016-06-01

    Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods. © The Author 2015; Published by Oxford University Press on behalf of the International Epidemiological Association.

  3. Probabilistic cellular automata.

    Science.gov (United States)

    Agapie, Alexandru; Andreica, Anca; Giuclea, Marius

    2014-09-01

    Cellular automata are binary lattices used for modeling complex dynamical systems. The automaton evolves iteratively from one configuration to another, using some local transition rule based on the number of ones in the neighborhood of each cell. With respect to the number of cells allowed to change per iteration, we speak of either synchronous or asynchronous automata. If randomness is involved to some degree in the transition rule, we speak of probabilistic automata, otherwise they are called deterministic. With either type of cellular automaton we are dealing with, the main theoretical challenge stays the same: starting from an arbitrary initial configuration, predict (with highest accuracy) the end configuration. If the automaton is deterministic, the outcome simplifies to one of two configurations, all zeros or all ones. If the automaton is probabilistic, the whole process is modeled by a finite homogeneous Markov chain, and the outcome is the corresponding stationary distribution. Based on our previous results for the asynchronous case-connecting the probability of a configuration in the stationary distribution to its number of zero-one borders-the article offers both numerical and theoretical insight into the long-term behavior of synchronous cellular automata.

  4. Ambient Surveillance by Probabilistic-Possibilistic Perception

    NARCIS (Netherlands)

    Bittermann, M.S.; Ciftcioglu, O.

    2013-01-01

    A method for quantifying ambient surveillance is presented, which is based on probabilistic-possibilistic perception. The human surveillance of a scene through observing camera sensed images on a monitor is modeled in three steps. First immersion of the observer is simulated by modeling perception

  5. Probabilistic risk assessment model for allergens in food: sensitivity analysis of the minimum eliciting dose and food consumption

    NARCIS (Netherlands)

    Kruizinga, A.G.; Briggs, D.; Crevel, R.W.R.; Knulst, A.C.; Bosch, L.M.C.v.d.; Houben, G.F.

    2008-01-01

    Previously, TNO developed a probabilistic model to predict the likelihood of an allergic reaction, resulting in a quantitative assessment of the risk associated with unintended exposure to food allergens. The likelihood is estimated by including in the model the proportion of the population who is

  6. ToPS: a framework to manipulate probabilistic models of sequence data.

    Directory of Open Access Journals (Sweden)

    André Yoshiaki Kashiwabara

    Full Text Available Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i independent and identically distributed process; (ii variable-length Markov chain; (iii inhomogeneous Markov chain; (iv hidden Markov model; (v profile hidden Markov model; (vi pair hidden Markov model; (vii generalized hidden Markov model; and (viii similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC. The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

  7. Empirical analyses of a choice model that captures ordering among attribute values

    DEFF Research Database (Denmark)

    Mabit, Stefan Lindhard

    2017-01-01

    an alternative additionally because it has the highest price. In this paper, we specify a discrete choice model that takes into account the ordering of attribute values across alternatives. This model is used to investigate the effect of attribute value ordering in three case studies related to alternative-fuel...... vehicles, mode choice, and route choice. In our application to choices among alternative-fuel vehicles, we see that especially the price coefficient is sensitive to changes in ordering. The ordering effect is also found in the applications to mode and route choice data where both travel time and cost...

  8. The drift diffusion model as the choice rule in reinforcement learning.

    Science.gov (United States)

    Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

    2017-08-01

    Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

  9. Probabilistic hypergraph based hash codes for social image search

    Institute of Scientific and Technical Information of China (English)

    Yi XIE; Hui-min YU; Roland HU

    2014-01-01

    With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order repre-sentation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hy-peredges. Experiments on Flickr image datasets verify the performance of our proposed approach.

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

    Science.gov (United States)

    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.

  11. Patient choice modelling: how do patients choose their hospitals?

    Science.gov (United States)

    Smith, Honora; Currie, Christine; Chaiwuttisak, Pornpimol; Kyprianou, Andreas

    2018-06-01

    As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.

  12. Probabilistic method/techniques of evaluation/modeling that permits to optimize/reduce the necessary resources

    International Nuclear Information System (INIS)

    Florescu, G.; Apostol, M.; Farcasiu, M.; Luminita Bedreaga, M.; Nitoi, M.; Turcu, I.

    2004-01-01

    Fault tree/event tree modeling approach is widely used in modeling and behavior simulation of nuclear structures, systems and components (NSSCs), during different condition of operation. Evaluation of NSSCs reliability, availability, risk or safety, during operation, by using probabilistic techniques, is also largely used. Development of computer capabilities offered new possibilities for large NSSCs models designing, processing and using. There are situations where large, complex and correct NSSC models are desired to be associated with rapid results/solutions/decisions or with multiple processing in order to obtain specific results. Large fault/event trees are hardly to be developed, reviewed and processed. During operation of NSSCs, the time, especially, is an important factor in taking decision. The paper presents a probabilistic method that permits evaluation/modeling of NSSCs and intents to solve the above problems by adopting appropriate techniques. The method is stated for special applications and is based on specific PSA analysis steps, information, algorithms, criteria and relations, in correspondence with the fault tree/event tree modeling and similar techniques, in order to obtain appropriate results for NSSC model analysis. Special classification of NSSCs is stated in order to reflect aspects of use of the method. Also the common reliability databases are part of information necessary to complete the analysis process. Special data and information bases contribute to state the proposed method/techniques. The paper also presents the specific steps of the method, its applicability, the main advantages and problems to be furthermore studied. The method permits optimization/reducing of resources used to perform the PSA activities. (author)

  13. Probabilistic Criterion for the Economical Assessment of Nuclear Reactors

    International Nuclear Information System (INIS)

    Juanico, L; Florido, Pablo; Bergallo, Juan

    2000-01-01

    In this paper a MonteCarlo probabilistic model for the economic evaluation of nuclear power plants is presented.The probabilistic results have shown a wide spread on the economic performance due to the schedule complexity and coupling if tasks.This spread increasing to the discount rate, end hence, it becomes more important for developing countries

  14. Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

    Directory of Open Access Journals (Sweden)

    Dirk Temme

    2008-12-01

    Full Text Available Integrated choice and latent variable (ICLV models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

  15. Probabilistic safety analysis procedures guide

    International Nuclear Information System (INIS)

    Papazoglou, I.A.; Bari, R.A.; Buslik, A.J.

    1984-01-01

    A procedures guide for the performance of probabilistic safety assessment has been prepared for interim use in the Nuclear Regulatory Commission programs. The probabilistic safety assessment studies performed are intended to produce probabilistic predictive models that can be used and extended by the utilities and by NRC to sharpen the focus of inquiries into a range of tissues affecting reactor safety. This guide addresses the determination of the probability (per year) of core damage resulting from accident initiators internal to the plant and from loss of offsite electric power. The scope includes analyses of problem-solving (cognitive) human errors, a determination of importance of the various core damage accident sequences, and an explicit treatment and display of uncertainties for the key accident sequences. Ultimately, the guide will be augmented to include the plant-specific analysis of in-plant processes (i.e., containment performance) and the risk associated with external accident initiators, as consensus is developed regarding suitable methodologies in these areas. This guide provides the structure of a probabilistic safety study to be performed, and indicates what products of the study are essential for regulatory decision making. Methodology is treated in the guide only to the extent necessary to indicate the range of methods which is acceptable; ample reference is given to alternative methodologies which may be utilized in the performance of the study

  16. Random regret-based discrete-choice modelling: an application to healthcare.

    Science.gov (United States)

    de Bekker-Grob, Esther W; Chorus, Caspar G

    2013-07-01

    A new modelling approach for analysing data from discrete-choice experiments (DCEs) has been recently developed in transport economics based on the notion of regret minimization-driven choice behaviour. This so-called Random Regret Minimization (RRM) approach forms an alternative to the dominant Random Utility Maximization (RUM) approach. The RRM approach is able to model semi-compensatory choice behaviour and compromise effects, while being as parsimonious and formally tractable as the RUM approach. Our objectives were to introduce the RRM modelling approach to healthcare-related decisions, and to investigate its usefulness in this domain. Using data from DCEs aimed at determining valuations of attributes of osteoporosis drug treatments and human papillomavirus (HPV) vaccinations, we empirically compared RRM models, RUM models and Hybrid RUM-RRM models in terms of goodness of fit, parameter ratios and predicted choice probabilities. In terms of model fit, the RRM model did not outperform the RUM model significantly in the case of the osteoporosis DCE data (p = 0.21), whereas in the case of the HPV DCE data, the Hybrid RUM-RRM model outperformed the RUM model (p implied by the two models can vary substantially. Differences in model fit between RUM, RRM and Hybrid RUM-RRM were found to be small. Although our study did not show significant differences in parameter ratios, the RRM and Hybrid RUM-RRM models did feature considerable differences in terms of the trade-offs implied by these ratios. In combination, our results suggest that RRM and Hybrid RUM-RRM modelling approach hold the potential of offering new and policy-relevant insights for health researchers and policy makers.

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

    Science.gov (United States)

    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…

  18. Modeling route choice criteria from home to major streets: A discrete choice approach

    Directory of Open Access Journals (Sweden)

    Jose Osiris Vidana-Bencomo

    2018-03-01

    Full Text Available A discrete choice model that consists of three sub-models was developed to investigates the route choice criteria of drivers who travel from their homes in the morning to the access point along the major streets that bound the Traffic Analysis Zones (TAZs. The first sub-model is a Nested Logit Model (NLM that estimates the probability of a driver has or has no multiple routes, and if the driver has multiple routes, the route selection criteria are based on the access point’s intersection control type or other factors. The second sub-model is a Mixed Logit (MXL model. It estimates the probabilities of the type of intersection control preferred by a driver. The third sub-model is a NLM that estimates the probabilities of a driver selecting his/her route for its shortest travel time or to avoid pedestrian, and if the aim is to take the fastest route, the decision criteria is based on the shortest distance or minimum stops and turns. Data gathered in a questionnaire survey were used to estimate the sub-models. The attributes of the utility functions of the sub-models are the driver’s demographic and trip characteristics. The model provides a means for transportation planners to distribute the total number of home-based trips generated within a TAZ to the access points along the major streets that bound the TAZ.

  19. Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing

    International Nuclear Information System (INIS)

    Tanaka, Kazuyuki; Shouno, Hayaru; Okada, Masato; Titterington, D M

    2004-01-01

    We investigate the accuracy of statistical-mechanical approximations for the estimation of hyperparameters from observable data in probabilistic image processing, which is based on Bayesian statistics and maximum likelihood estimation. Hyperparameters in statistical science correspond to interactions or external fields in the statistical-mechanics context. In this paper, hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood. A practical algorithm is described for grey-level image restoration based on a Gaussian graphical model and the Bethe approximation. The algorithm corresponds to loopy belief propagation in artificial intelligence. We examine the accuracy of hyperparameter estimation when we use the Bethe approximation. It is well known that a practical algorithm for probabilistic image processing can be prescribed analytically when a Gaussian graphical model is adopted as a prior probabilistic model in Bayes' formula. We are therefore able to compare, in a numerical study, results obtained through mean-field-type approximations with those based on exact calculation

  20. Understanding Predisposition in College Choice: Toward an Integrated Model of College Choice and Theory of Reasoned Action

    Science.gov (United States)

    Pitre, Paul E.; Johnson, Todd E.; Pitre, Charisse Cowan

    2006-01-01

    This article seeks to improve traditional models of college choice that draw from recruitment and enrollment management paradigms. In adopting a consumer approach to college choice, this article seeks to build upon consumer-related research, which centers on behavior and reasoning. More specifically, this article seeks to move inquiry beyond the…

  1. Cooperation in an evolutionary prisoner’s dilemma game with probabilistic strategies

    International Nuclear Information System (INIS)

    Li Haihong; Dai Qionglin; Cheng Hongyan; Yang Junzhong

    2012-01-01

    Highlights: ► Introducing probabilistic strategies instead of the pure C/D in the PDG. ► The strategies patterns depends on interaction structures and updating rules. ► There exists an optimal increment of the probabilistic strategy. - Abstract: In this work, we investigate an evolutionary prisoner’s dilemma game in structured populations with probabilistic strategies instead of the pure strategies of cooperation and defection. We explore the model in details by considering different strategy update rules and different population structures. We find that the distribution of probabilistic strategies patterns is dependent on both the interaction structures and the updating rules. We also find that, when an individual updates her strategy by increasing or decreasing her probabilistic strategy a certain amount towards that of her opponent, there exists an optimal increment of the probabilistic strategy at which the cooperator frequency reaches its maximum.

  2. Bayesian statistic methods and theri application in probabilistic simulation models

    Directory of Open Access Journals (Sweden)

    Sergio Iannazzo

    2007-03-01

    Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

  3. Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions

    Science.gov (United States)

    Piantadosi, Steven T.; Hayden, Benjamin Y.

    2015-01-01

    Economists often model choices as if decision-makers assign each option a scalar value variable, known as utility, and then select the option with the highest utility. It remains unclear whether as-if utility models describe real mental and neural steps in choice. Although choices alone cannot prove the existence of a utility stage, utility transformations are often taken to provide the most parsimonious or psychologically plausible explanation for choice data. Here, we show that it is possible to mathematically transform a large set of common utility-stage two-option choice models (specifically ones in which dimensions are can be decomposed into additive functions) into a heuristic model (specifically, a dimensional prioritization heuristic) that has no utility computation stage. We then show that under a range of plausible assumptions, both classes of model predict similar neural responses. These results highlight the difficulties in using neuroeconomic data to infer the existence of a value stage in choice. PMID:25914613

  4. On the Probabilistic Characterization of Robustness and Resilience

    DEFF Research Database (Denmark)

    Faber, Michael Havbro; Qin, J.; Miraglia, Simona

    2017-01-01

    Over the last decade significant research efforts have been devoted to the probabilistic modeling and analysis of system characteristics. Especially performance characteristics of systems subjected to random disturbances, such as robustness and resilience have been in the focus of these efforts...... in the modeling of robustness and resilience in the research areas of natural disaster risk management, socio-ecological systems and social systems and we propose a generic decision analysis framework for the modeling and analysis of systems across application areas. The proposed framework extends the concept...... of direct and indirect consequences and associated risks in probabilistic systems modeling formulated by the Joint Committee on Structural Safety (JCSS) to facilitate the modeling and analysis of resilience in addition to robustness and vulnerability. Moreover, based on recent insights in the modeling...

  5. Probabilistic projections of 21st century climate change over Northern Eurasia

    Science.gov (United States)

    Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

    2013-12-01

    We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.

  6. Probabilistic projections of 21st century climate change over Northern Eurasia

    International Nuclear Information System (INIS)

    Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

    2013-01-01

    We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia. (letter)

  7. A physical probabilistic model to predict failure rates in buried PVC pipelines

    International Nuclear Information System (INIS)

    Davis, P.; Burn, S.; Moglia, M.; Gould, S.

    2007-01-01

    For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical

  8. Probabilistic dual heuristic programming-based adaptive critic

    Science.gov (United States)

    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.

  9. Disjunctive Probabilistic Modal Logic is Enough for Bisimilarity on Reactive Probabilistic Systems

    OpenAIRE

    Bernardo, Marco; Miculan, Marino

    2016-01-01

    Larsen and Skou characterized probabilistic bisimilarity over reactive probabilistic systems with a logic including true, negation, conjunction, and a diamond modality decorated with a probabilistic lower bound. Later on, Desharnais, Edalat, and Panangaden showed that negation is not necessary to characterize the same equivalence. In this paper, we prove that the logical characterization holds also when conjunction is replaced by disjunction, with negation still being not necessary. To this e...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  12. Probabilistic modeling of fatigue crack growth in Ti-6Al-4V

    International Nuclear Information System (INIS)

    Soboyejo, W.O.; Shen, W.; Soboyejo, A.B.O.

    2001-01-01

    This paper presents the results of a combined experimental and analytical study of the probabilistic nature of fatigue crack growth in Ti-6Al-4V. A simple experimental fracture mechanics framework is presented for the determination of statistical fatigue crack growth parameters from two fatigue tests. The experimental studies show that the variabilities in long fatigue crack growth rate data and the Paris coefficient are well described by the log-normal distributions. The variabilities in the Paris exponent are also shown to be well characterized by a normal distribution. The measured statistical distributions are incorporated into a probabilistic fracture mechanics framework for the estimation of material reliability. The implications of the results are discussed for the probabilistic analysis of fatigue crack growth in engineering components and structures. (orig.)

  13. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

    Science.gov (United States)

    McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

    2017-03-01

    Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  14. Application of a probabilistic model of rainfall-induced shallow landslides to complex hollows

    Directory of Open Access Journals (Sweden)

    A. Talebi

    2008-07-01

    Full Text Available Recently, D'Odorico and Fagherazzi (2003 proposed "A probabilistic model of rainfall-triggered shallow landslides in hollows" (Water Resour. Res., 39, 2003. Their model describes the long-term evolution of colluvial deposits through a probabilistic soil mass balance at a point. Further building blocks of the model are: an infinite-slope stability analysis; a steady-state kinematic wave model (KW of hollow groundwater hydrology; and a statistical model relating intensity, duration, and frequency of extreme precipitation. Here we extend the work of D'Odorico and Fagherazzi (2003 by incorporating a more realistic description of hollow hydrology (hillslope storage Boussinesq model, HSB such that this model can also be applied to more gentle slopes and hollows with different plan shapes. We show that results obtained using the KW and HSB models are significantly different as in the KW model the diffusion term is ignored. We generalize our results by examining the stability of several hollow types with different plan shapes (different convergence degree. For each hollow type, the minimum value of the landslide-triggering saturated depth corresponding to the triggering precipitation (critical recharge rate is computed for steep and gentle hollows. Long term analysis of shallow landslides by the presented model illustrates that all hollows show a quite different behavior from the stability view point. In hollows with more convergence, landslide occurrence is limited by the supply of deposits (supply limited regime or rainfall events (event limited regime while hollows with low convergence degree are unconditionally stable regardless of the soil thickness or rainfall intensity. Overall, our results show that in addition to the effect of slope angle, plan shape (convergence degree also controls the subsurface flow and this process affects the probability distribution of landslide occurrence in different hollows. Finally, we conclude that

  15. Sensitivity analysis in multi-parameter probabilistic systems

    International Nuclear Information System (INIS)

    Walker, J.R.

    1987-01-01

    Probabilistic methods involving the use of multi-parameter Monte Carlo analysis can be applied to a wide range of engineering systems. The output from the Monte Carlo analysis is a probabilistic estimate of the system consequence, which can vary spatially and temporally. Sensitivity analysis aims to examine how the output consequence is influenced by the input parameter values. Sensitivity analysis provides the necessary information so that the engineering properties of the system can be optimized. This report details a package of sensitivity analysis techniques that together form an integrated methodology for the sensitivity analysis of probabilistic systems. The techniques have known confidence limits and can be applied to a wide range of engineering problems. The sensitivity analysis methodology is illustrated by performing the sensitivity analysis of the MCROC rock microcracking model

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

    Science.gov (United States)

    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...

  17. Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

    DEFF Research Database (Denmark)

    Wan, Can; Lin, Jin; Song, Yonghua

    2017-01-01

    This letter proposes a novel efficient probabilistic forecasting approach to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for P...... power generation is proposed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.......This letter proposes a novel efficient probabilistic forecasting approach to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV...

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

    Science.gov (United States)

    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.

  19. Uniform and localized corrosion modelling by means of probabilistic cellular automata

    International Nuclear Information System (INIS)

    Perez-Brokate, Cristian

    2016-01-01

    Numerical modelling is complementary tool for corrosion prediction. The objective of this work is to develop a corrosion model by means of a probabilistic cellular automata approach at a mesoscopic scale. In this work, we study the morphological evolution and kinetics of corrosion. This model couples electrochemical oxidation and reduction reactions. Regarding kinetics, cellular automata models are able to describe current as a function of the applied potential for a redox reaction on an inert electrode. The inclusion of probabilities allows the description of the stochastic nature of anodic and cathodic reactions. Corrosion morphology has been studied in different context: generalised corrosion, pitting corrosion and corrosion in an occluded environment. a general tendency of two regimes is found. a first regime of uniform corrosion where the anodic and cathodic reactions occur homogeneously over the surface. a second regime of localized corrosion when there is a spatial separation of anodic and cathodic zones, with an increase of anodic reaction rate. (author) [fr

  20. Recent case studies and advancements in probabilistic risk assessment

    International Nuclear Information System (INIS)

    Garrick, B.J.

    1985-01-01

    During the period from 1977 to 1984, Pickard, Lowe and Garrick, Inc., had the lead in preparing several full scope probabilistic risk assessments for electric utilities. Five of those studies are discussed from the point of view of advancements and lessons learned. The objective and trend of these studies is toward utilization of the risk models by the plant owners as risk management tools. Advancements that have been made are in presentation ad documentation of the PRAs, generation of more understandable plant level information, and improvements in methodology to facilitate technology transfer. Specific areas of advancement are in the treatment of such issues as dependent failures, human interaction, and the uncertainty in the source term. Lessons learned cover a wide spectrum and include the importance of plant specific models for meaningful risk management, the role of external events in risk, the sensitivity of contributors to choice of risk index, and the very important finding that the public risk is extremely small. The future direction of PRA is to establish less dependence on experts for in-plant application. Computerizing the PRAs such that they can be accessed on line and interactively is the key

  1. Incorporating organizational factors into Probabilistic Risk Assessment (PRA) of complex socio-technical systems: A hybrid technique formalization

    Energy Technology Data Exchange (ETDEWEB)

    Mohaghegh, Zahra [Center for Risk and Reliability, University of Maryland, College Park, MD 20742 (United States)], E-mail: mohagheg@umd.edu; Kazemi, Reza; Mosleh, Ali [Center for Risk and Reliability, University of Maryland, College Park, MD 20742 (United States)

    2009-05-15

    This paper is a result of a research with the primary purpose of extending Probabilistic Risk Assessment (PRA) modeling frameworks to include the effects of organizational factors as the deeper, more fundamental causes of accidents and incidents. There have been significant improvements in the sophistication of quantitative methods of safety and risk assessment, but the progress on techniques most suitable for organizational safety risk frameworks has been limited. The focus of this paper is on the choice of 'representational schemes' and 'techniques.' A methodology for selecting appropriate candidate techniques and their integration in the form of a 'hybrid' approach is proposed. Then an example is given through an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT) in order to demonstrate the feasibility and value of hybrid techniques. The proposed hybrid approach integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool for complex socio-technical systems. An application of the hybrid technique is provided in the aviation safety domain, focusing on airline maintenance systems. The example demonstrates how the hybrid method can be used to analyze the dynamic effects of organizational factors on system risk.

  2. Testing process predictions of models of risky choice: a quantitative model comparison approach

    Science.gov (United States)

    Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

    2013-01-01

    This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

  3. Testing Process Predictions of Models of Risky Choice: A Quantitative Model Comparison Approach

    Directory of Open Access Journals (Sweden)

    Thorsten ePachur

    2013-09-01

    Full Text Available This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or nonlinear functions thereof and the separate evaluation of risky options (expectation models. Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models. We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter, Gigerenzer, & Hertwig, 2006, and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up and direction of search (i.e., gamble-wise vs. reason-wise. In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly; acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988 called similarity. In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

  4. Probabilistic design framework for sustainable repari and rehabilitation of civil infrastructure

    DEFF Research Database (Denmark)

    Lepech, Michael; Geiker, Mette Rica; Stang, Henrik

    2011-01-01

    This paper presents a probabilistic-based framework for the design of civil infrastructure repair and rehabilitation to achieve targeted improvements in sustainability indicators. The framework consists of two types of models: (i) service life prediction models combining one or several deteriorat......This paper presents a probabilistic-based framework for the design of civil infrastructure repair and rehabilitation to achieve targeted improvements in sustainability indicators. The framework consists of two types of models: (i) service life prediction models combining one or several...

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

    Science.gov (United States)

    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.

  6. Probabilistic Power Flow Simulation allowing Temporary Current Overloading

    NARCIS (Netherlands)

    W.S. Wadman (Wander); G. Bloemhof; D.T. Crommelin (Daan); J.E. Frank (Jason)

    2012-01-01

    htmlabstractThis paper presents a probabilistic power flow model subject to connection temperature constraints. Renewable power generation is included and modelled stochastically in order to reflect its intermittent nature. In contrast to conventional models that enforce connection current

  7. Exploiting Tensor Rank-One Decomposition in Probabilistic Inference

    Czech Academy of Sciences Publication Activity Database

    Savický, Petr; Vomlel, Jiří

    2007-01-01

    Roč. 43, č. 5 (2007), s. 747-764 ISSN 0023-5954 R&D Projects: GA MŠk 1M0545; GA MŠk 1M0572; GA ČR GA201/04/0393 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z10750506 Keywords : graphical probabilistic models * probabilistic inference * tensor rank Subject RIV: BD - Theory of Information Impact factor: 0.552, year: 2007 http://dml.cz/handle/10338.dmlcz/135810

  8. Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Zugno, Marco; Madsen, Henrik

    2016-01-01

    The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known...... that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic-type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state......-dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non-parametric modelling to explore conditional correlation...

  9. Duplicate Detection in Probabilistic Data

    NARCIS (Netherlands)

    Panse, Fabian; van Keulen, Maurice; de Keijzer, Ander; Ritter, Norbert

    2009-01-01

    Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused

  10. The development and use of parametric sampling techniques for probabilistic risk assessment

    International Nuclear Information System (INIS)

    Dalrymple, G.J.; Broyd, T.W.

    1987-01-01

    In order to enable evaluation to be made of proposals for the underground disposal of low and intermediate level radioactive wastes in the United Kingdom, the Department of the Environment (DoE) research programme includes development of computer-based methods for use in a multistage assessment process. To test the adequacy of the various methods of data acquisitions and radiological assessment a mock assessment exercise is currently being conducted by the department. This paper outlines the proposed methodology which provides for the use of probabilistic modelling based upon the Atomic Energy of Canada Ltd SYVAC variability analysis approach using new models (SYVAC 'A') and data appropriate to UK conditions for a deep horizontal tunnel repository concept. This chapter describes the choice of a suitable technique for the sampling of data input to the SYVAC 'A' model and techniques for analysing the predictions of dose and risk made by the model. The sensitivity of the model predictions (risk and dose to man) to the input parameters was compared for four different methods. All four methods identified the same geological parameters as the most important. (author)

  11. Incorporating Responsiveness to Marketing Efforts in Brand Choice Modeling

    Directory of Open Access Journals (Sweden)

    Dennis Fok

    2014-02-01

    Full Text Available We put forward a brand choice model with unobserved heterogeneity that concerns responsiveness to marketing efforts. We introduce two latent segments of households. The first segment is assumed to respond to marketing efforts, while households in the second segment do not do so. Whether a specific household is a member of the first or the second segment at a specific purchase occasion is described by household-specific characteristics and characteristics concerning buying behavior. Households may switch between the two responsiveness states over time. When comparing the performance of our model with alternative choice models that account for various forms of heterogeneity for three different datasets, we find better face validity for our parameters. Our model also forecasts better.

  12. A Probabilistic Design Methodology for a Turboshaft Engine Overall Performance Analysis

    Directory of Open Access Journals (Sweden)

    Min Chen

    2014-05-01

    Full Text Available In reality, the cumulative effect of the many uncertainties in engine component performance may stack up to affect the engine overall performance. This paper aims to quantify the impact of uncertainty in engine component performance on the overall performance of a turboshaft engine based on Monte-Carlo probabilistic design method. A novel probabilistic model of turboshaft engine, consisting of a Monte-Carlo simulation generator, a traditional nonlinear turboshaft engine model, and a probability statistical model, was implemented to predict this impact. One of the fundamental results shown herein is that uncertainty in component performance has a significant impact on the engine overall performance prediction. This paper also shows that, taking into consideration the uncertainties in component performance, the turbine entry temperature and overall pressure ratio based on the probabilistic design method should increase by 0.76% and 8.33%, respectively, compared with the ones of deterministic design method. The comparison shows that the probabilistic approach provides a more credible and reliable way to assign the design space for a target engine overall performance.

  13. Geothermal probabilistic cost study

    Energy Technology Data Exchange (ETDEWEB)

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

    1981-08-01

    A tool is presented to quantify the risks of geothermal projects, the Geothermal Probabilistic Cost Model (GPCM). The GPCM model is 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 are analyzed. The leasing of geothermal land, contracting between the producer and the user of the geothermal heat, and insurance against faulty performance are examined. (MHR)

  14. Probabilistic Matching of Deidentified Data From a Trauma Registry and a Traumatic Brain Injury Model System Center: A Follow-up Validation Study.

    Science.gov (United States)

    Kumar, Raj G; Wang, Zhensheng; Kesinger, Matthew R; Newman, Mark; Huynh, Toan T; Niemeier, Janet P; Sperry, Jason L; Wagner, Amy K

    2018-04-01

    In a previous study, individuals from a single Traumatic Brain Injury Model Systems and trauma center were matched using a novel probabilistic matching algorithm. The Traumatic Brain Injury Model Systems is a multicenter prospective cohort study containing more than 14,000 participants with traumatic brain injury, following them from inpatient rehabilitation to the community over the remainder of their lifetime. The National Trauma Databank is the largest aggregation of trauma data in the United States, including more than 6 million records. Linking these two databases offers a broad range of opportunities to explore research questions not otherwise possible. Our objective was to refine and validate the previous protocol at another independent center. An algorithm generation and validation data set were created, and potential matches were blocked by age, sex, and year of injury; total probabilistic weight was calculated based on of 12 common data fields. Validity metrics were calculated using a minimum probabilistic weight of 3. The positive predictive value was 98.2% and 97.4% and sensitivity was 74.1% and 76.3%, in the algorithm generation and validation set, respectively. These metrics were similar to the previous study. Future work will apply the refined probabilistic matching algorithm to the Traumatic Brain Injury Model Systems and the National Trauma Databank to generate a merged data set for clinical traumatic brain injury research use.

  15. Resolving the paradox of suboptimal choice.

    Science.gov (United States)

    Zentall, Thomas R

    2016-01-01

    When humans engage in commercial (totally probabilistic) gambling they are making suboptimal choices because the return is generally less than the investment. This review (a) examines the literature on pigeon suboptimal choice, (b) describes the conditions under which it occurs, (c) identifies the mechanisms that appear to be responsible for the effect, and (d) suggests that similar processes may be able to account for analogous suboptimal choice when humans engage in commercial gambling. Pigeons show suboptimal choice when they choose between 1 alternative that 20% of the time provides them with a signal that they will always get fed or 80% of the time with a signal that they will not get fed (overall 20% reinforcement) and a second alternative that 100% of the time provides them with a signal that they will get fed 50% of the time (overall 50% reinforcement). The pigeons' strong preference for the suboptimal choice was investigated in a series of experiments that found the preference for the suboptimal alternative was determined by the value of the signal that predicted reinforcement, rather its frequency and that the frequency of the signal that predicted nonreinforcement had little effect on the suboptimal choice. Paradoxically, this account makes the prediction that pigeons will be indifferent between an alternative that 50% of the time provides a fully predictive stimulus for reinforcement and an alternative that 100% of the time provides a fully predictive stimulus for reinforcement. The similarities and differences of this suboptimal choice task to human gambling are discussed. (c) 2016 APA, all rights reserved).

  16. Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

    NARCIS (Netherlands)

    Rosas-Carbajal, M.; Linde, N.; Kalscheuer, T.; Vrugt, J.A.

    2014-01-01

    Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space

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

    Energy Technology Data Exchange (ETDEWEB)

    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 to 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.

  18. Compression of Probabilistic XML Documents

    Science.gov (United States)

    Veldman, Irma; de Keijzer, Ander; van Keulen, Maurice

    Database techniques to store, query and manipulate data that contains uncertainty receives increasing research interest. Such UDBMSs can be classified according to their underlying data model: relational, XML, or RDF. We focus on uncertain XML DBMS with as representative example the Probabilistic XML model (PXML) of [10,9]. The size of a PXML document is obviously a factor in performance. There are PXML-specific techniques to reduce the size, such as a push down mechanism, that produces equivalent but more compact PXML documents. It can only be applied, however, where possibilities are dependent. For normal XML documents there also exist several techniques for compressing a document. Since Probabilistic XML is (a special form of) normal XML, it might benefit from these methods even more. In this paper, we show that existing compression mechanisms can be combined with PXML-specific compression techniques. We also show that best compression rates are obtained with a combination of PXML-specific technique with a rather simple generic DAG-compression technique.

  19. Confluence reduction for probabilistic systems

    NARCIS (Netherlands)

    Timmer, Mark; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette

    In this presentation we introduce a novel technique for state space reduction of probabilistic specifications, based on a newly developed notion of confluence for probabilistic automata. We proved that this reduction preserves branching probabilistic bisimulation and can be applied on-the-fly. To

  20. A Conditional Curie-Weiss Model for Stylized Multi-group Binary Choice with Social Interaction

    Science.gov (United States)

    Opoku, Alex Akwasi; Edusei, Kwame Owusu; Ansah, Richard Kwame

    2018-04-01

    This paper proposes a conditional Curie-Weiss model as a model for decision making in a stylized society made up of binary decision makers that face a particular dichotomous choice between two options. Following Brock and Durlauf (Discrete choice with social interaction I: theory, 1955), we set-up both socio-economic and statistical mechanical models for the choice problem. We point out when both the socio-economic and statistical mechanical models give rise to the same self-consistent equilibrium mean choice level(s). Phase diagram of the associated statistical mechanical model and its socio-economic implications are discussed.

  1. Resolution and Probabilistic Models of Components in CryoEM Maps of Mature P22 Bacteriophage

    Science.gov (United States)

    Pintilie, Grigore; Chen, Dong-Hua; Haase-Pettingell, Cameron A.; King, Jonathan A.; Chiu, Wah

    2016-01-01

    CryoEM continues to produce density maps of larger and more complex assemblies with multiple protein components of mixed symmetries. Resolution is not always uniform throughout a cryoEM map, and it can be useful to estimate the resolution in specific molecular components of a large assembly. In this study, we present procedures to 1) estimate the resolution in subcomponents by gold-standard Fourier shell correlation (FSC); 2) validate modeling procedures, particularly at medium resolutions, which can include loop modeling and flexible fitting; and 3) build probabilistic models that combine high-accuracy priors (such as crystallographic structures) with medium-resolution cryoEM densities. As an example, we apply these methods to new cryoEM maps of the mature bacteriophage P22, reconstructed without imposing icosahedral symmetry. Resolution estimates based on gold-standard FSC show the highest resolution in the coat region (7.6 Å), whereas other components are at slightly lower resolutions: portal (9.2 Å), hub (8.5 Å), tailspike (10.9 Å), and needle (10.5 Å). These differences are indicative of inherent structural heterogeneity and/or reconstruction accuracy in different subcomponents of the map. Probabilistic models for these subcomponents provide new insights, to our knowledge, and structural information when taking into account uncertainty given the limitations of the observed density. PMID:26743049

  2. Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling

    Science.gov (United States)

    Papaioannou, George; Vasiliades, Lampros; Loukas, Athanasios; Aronica, Giuseppe T.

    2017-04-01

    Probabilistic flood inundation mapping is performed and analysed at the ungauged Xerias stream reach, Volos, Greece. The study evaluates the uncertainty introduced by the roughness coefficient values on hydraulic models in flood inundation modelling and mapping. The well-established one-dimensional (1-D) hydraulic model, HEC-RAS is selected and linked to Monte-Carlo simulations of hydraulic roughness. Terrestrial Laser Scanner data have been used to produce a high quality DEM for input data uncertainty minimisation and to improve determination accuracy on stream channel topography required by the hydraulic model. Initial Manning's n roughness coefficient values are based on pebble count field surveys and empirical formulas. Various theoretical probability distributions are fitted and evaluated on their accuracy to represent the estimated roughness values. Finally, Latin Hypercube Sampling has been used for generation of different sets of Manning roughness values and flood inundation probability maps have been created with the use of Monte Carlo simulations. Historical flood extent data, from an extreme historical flash flood event, are used for validation of the method. The calibration process is based on a binary wet-dry reasoning with the use of Median Absolute Percentage Error evaluation metric. The results show that the proposed procedure supports probabilistic flood hazard mapping at ungauged rivers and provides water resources managers with valuable information for planning and implementing flood risk mitigation strategies.

  3. Probabilistic simulation applications to reliability assessments

    International Nuclear Information System (INIS)

    Miller, Ian; Nutt, Mark W.; Hill, Ralph S. III

    2003-01-01

    Probabilistic risk/reliability (PRA) analyses for engineered systems are conventionally based on fault-tree methods. These methods are mature and efficient, and are well suited to systems consisting of interacting components with known, low probabilities of failure. Even complex systems, such as nuclear power plants or aircraft, are modeled by the careful application of these approaches. However, for systems that may evolve in complex and nonlinear ways, and where the performance of components may be a sensitive function of the history of their working environments, fault-tree methods can be very demanding. This paper proposes an alternative method of evaluating such systems, based on probabilistic simulation using intelligent software objects to represent the components of such systems. Using a Monte Carlo approach, simulation models can be constructed from relatively simple interacting objects that capture the essential behavior of the components that they represent. Such models are capable of reflecting the complex behaviors of the systems that they represent in a natural and realistic way. (author)

  4. Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models

    Energy Technology Data Exchange (ETDEWEB)

    Galan, S.F. [Dpto. de Inteligencia Artificial, E.T.S.I. Informatica (UNED), Juan del Rosal, 16, 28040 Madrid (Spain)]. E-mail: seve@dia.uned.es; Mosleh, A. [2100A Marie Mount Hall, Materials and Nuclear Engineering Department, University of Maryland, College Park, MD 20742 (United States)]. E-mail: mosleh@umd.edu; Izquierdo, J.M. [Area de Modelado y Simulacion, Consejo de Seguridad Nuclear, Justo Dorado, 11, 28040 Madrid (Spain)]. E-mail: jmir@csn.es

    2007-08-15

    The {omega}-factor approach is a method that explicitly incorporates organizational factors into Probabilistic safety assessment of nuclear power plants. Bayesian networks (BNs) are the underlying formalism used in this approach. They have a structural part formed by a graph whose nodes represent organizational variables, and a parametric part that consists of conditional probabilities, each of them quantifying organizational influences between one variable and its parents in the graph. The aim of this paper is twofold. First, we discuss some important limitations of current procedures in the {omega}-factor approach for either assessing conditional probabilities from experts or estimating them from data. We illustrate the discussion with an example that uses data from Licensee Events Reports of nuclear power plants for the estimation task. Second, we introduce significant improvements in the way BNs for the {omega}-factor approach can be constructed, so that parameter acquisition becomes easier and more intuitive. The improvements are based on the use of noisy-OR gates as model of multicausal interaction between each BN node and its parents.

  5. Incorporating organizational factors into probabilistic safety assessment of nuclear power plants through canonical probabilistic models

    International Nuclear Information System (INIS)

    Galan, S.F.; Mosleh, A.; Izquierdo, J.M.

    2007-01-01

    The ω-factor approach is a method that explicitly incorporates organizational factors into Probabilistic safety assessment of nuclear power plants. Bayesian networks (BNs) are the underlying formalism used in this approach. They have a structural part formed by a graph whose nodes represent organizational variables, and a parametric part that consists of conditional probabilities, each of them quantifying organizational influences between one variable and its parents in the graph. The aim of this paper is twofold. First, we discuss some important limitations of current procedures in the ω-factor approach for either assessing conditional probabilities from experts or estimating them from data. We illustrate the discussion with an example that uses data from Licensee Events Reports of nuclear power plants for the estimation task. Second, we introduce significant improvements in the way BNs for the ω-factor approach can be constructed, so that parameter acquisition becomes easier and more intuitive. The improvements are based on the use of noisy-OR gates as model of multicausal interaction between each BN node and its parents

  6. Choices and Changes: Eccles' Expectancy-Value Model and Upper-Secondary School Students' Longitudinal Reflections about Their Choice of a STEM Education

    Science.gov (United States)

    Lykkegaard, Eva; Ulriksen, Lars

    2016-01-01

    During the past 30 years, Eccles' comprehensive social-psychological Expectancy-Value Model of Motivated Behavioural Choices (EV-MBC model) has been proven suitable for studying educational choices related to Science, Technology, Engineering and/or Mathematics (STEM). The reflections of 15 students in their last year in upper-secondary school…

  7. Use of probabilistic studies in the analysis of modifications of French nuclear power plants

    International Nuclear Information System (INIS)

    Gros, G.; Milhem, J.M.

    1985-11-01

    The 900 MWe water pressurized reactors have been designed on deterministic basis. It appeared that some safety systems had a probability of failure non negligible and that their total failure could involve, in a short-term, severe consequences. This situation led Electricite de France to propose complementary measures (control-procedures, and associated modifications). To judge the efficiency of such measures, the safety authorities thought it was advisable to rest on probabilistic studies which have been developed by the Department of Safety Analysis of the C.E.A. The contribution of such studies, in the choice of modifications by information on the weak points and in the judgement on the efficiency of these modifications by probabilistic estimation of meltdown, is illustrated with the example of electric power supplies [fr

  8. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, Warren C.; Scherbov, Sergei; O'Neill, Brian C.; Lutz, Wolfgang

    2004-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because...

  9. An application of probabilistic safety assessment methods to model aircraft systems and accidents

    Energy Technology Data Exchange (ETDEWEB)

    Martinez-Guridi, G.; Hall, R.E.; Fullwood, R.R.

    1998-08-01

    A case study modeling the thrust reverser system (TRS) in the context of the fatal accident of a Boeing 767 is presented to illustrate the application of Probabilistic Safety Assessment methods. A simplified risk model consisting of an event tree with supporting fault trees was developed to represent the progression of the accident, taking into account the interaction between the TRS and the operating crew during the accident, and the findings of the accident investigation. A feasible sequence of events leading to the fatal accident was identified. Several insights about the TRS and the accident were obtained by applying PSA methods. Changes proposed for the TRS also are discussed.

  10. A Probabilistic Approach for Robustness Evaluation of Timber Structures

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Sørensen, John Dalsgaard

    of Structures and a probabilistic modelling of the timber material proposed in the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS). Due to the framework in the Danish Code the timber structure has to be evaluated with respect to the following criteria where at least one shall...... to criteria a) and b) the timber frame structure has one column with a reliability index a bit lower than an assumed target level. By removal three columns one by one no significant extensive failure of the entire structure or significant parts of it are obatined. Therefore the structure can be considered......A probabilistic based robustness analysis has been performed for a glulam frame structure supporting the roof over the main court in a Norwegian sports centre. The robustness analysis is based on the framework for robustness analysis introduced in the Danish Code of Practice for the Safety...

  11. From equilibrium spin models to probabilistic cellular automata

    International Nuclear Information System (INIS)

    Georges, A.; Le Doussal, P.

    1989-01-01

    The general equivalence between D-dimensional probabilistic cellular automata (PCA) and (D + 1)-dimensional equilibrium spin models satisfying a disorder condition is first described in a pedagogical way and then used to analyze the phase diagrams, the critical behavior, and the universality classes of some automato. Diagrammatic representations of time-dependent correlation functions PCA are introduced. Two important classes of PCA are singled out for which these correlation functions simplify: (1) Quasi-Hamiltonian automata, which have a current-carrying steady state, and for which some correlation functions are those of a D-dimensional static model PCA satisfying the detailed balance condition appear as a particular case of these rules for which the current vanishes. (2) Linear (and more generally affine) PCA for which the diagrammatics reduces to a random walk problem closely related to (D + 1)-dimensional directed SAWs: both problems display a critical behavior with mean-field exponents in any dimension. The correlation length and effective velocity of propagation of excitations can be calculated for affine PCA, as is shown on an explicit D = 1 example. The authors conclude with some remarks on nonlinear PCA, for which the diagrammatics is related to reaction-diffusion processes, and which belong in some cases to the universality class of Reggeon field theory

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

    Science.gov (United States)

    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.

  13. Probabilistic estimates of drought impacts on agricultural production

    Science.gov (United States)

    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.

  14. MODELLING CONSUMER CHOICE IN THE MARKET SWITCHBOARD EQUIPMENT USING IBM SPSS STATISTICS

    Directory of Open Access Journals (Sweden)

    Sergey V. Mkhitaryan

    2014-01-01

    Full Text Available Modelling consumer choice in the marketswitch equipment will allow manufacturing enterprises to improve the efficiencyof design and marketing activities byreducing the financial and human losses associated with pre-treatment orders. Todevelop a model of consumer choice canbe used logistic regression.

  15. Probabilistic model for fluences and peak fluxes of solar energetic particles

    International Nuclear Information System (INIS)

    Nymmik, R.A.

    1999-01-01

    The model is intended for calculating the probability for solar energetic particles (SEP), i.e., protons and Z=2-28 ions, to have an effect on hardware and on biological and other objects in the space. The model describes the probability for the ≥10 MeV/nucleon SEP fluences and peak fluxes to occur in the near-Earth space beyond the Earth magnetosphere under varying solar activity. The physical prerequisites of the model are as follows. The occurrence of SEP is a probabilistic process. The mean SEP occurrence frequency is a power-law function of solar activity (sunspot number). The SEP size (taken to be the ≥30 MeV proton fluence size) distribution is a power-law function within a 10 5 -10 11 proton/cm 2 range. The SEP event particle energy spectra are described by a common function whose parameters are distributed log-normally. The SEP mean composition is energy-dependent and suffers fluctuations described by log-normal functions in separate events

  16. Conditional Probabilistic Population Forecasting

    OpenAIRE

    Sanderson, W.C.; Scherbov, S.; O'Neill, B.C.; Lutz, W.

    2003-01-01

    Since policy makers often prefer to think in terms of scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy makers it allows them to answer "what if"...

  17. Conditional probabilistic population forecasting

    OpenAIRE

    Sanderson, Warren; Scherbov, Sergei; O'Neill, Brian; Lutz, Wolfgang

    2003-01-01

    Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them...

  18. Deep Generative Models for Molecular Science

    DEFF Research Database (Denmark)

    Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

    2018-01-01

    Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

  19. Branching bisimulation congruence for probabilistic systems

    NARCIS (Netherlands)

    Andova, S.; Georgievska, S.; Trcka, N.

    2012-01-01

    A notion of branching bisimilarity for the alternating model of probabilistic systems, compatible with parallel composition, is defined. For a congruence result, an internal transition immediately followed by a non-trivial probability distribution is not considered inert. A weaker definition of

  20. Students’ difficulties in probabilistic problem-solving

    Science.gov (United States)

    Arum, D. P.; Kusmayadi, T. A.; Pramudya, I.

    2018-03-01

    There are many errors can be identified when students solving mathematics problems, particularly in solving the probabilistic problem. This present study aims to investigate students’ difficulties in solving the probabilistic problem. It focuses on analyzing and describing students errors during solving the problem. This research used the qualitative method with case study strategy. The subjects in this research involve ten students of 9th grade that were selected by purposive sampling. Data in this research involve students’ probabilistic problem-solving result and recorded interview regarding students’ difficulties in solving the problem. Those data were analyzed descriptively using Miles and Huberman steps. The results show that students have difficulties in solving the probabilistic problem and can be divided into three categories. First difficulties relate to students’ difficulties in understanding the probabilistic problem. Second, students’ difficulties in choosing and using appropriate strategies for solving the problem. Third, students’ difficulties with the computational process in solving the problem. Based on the result seems that students still have difficulties in solving the probabilistic problem. It means that students have not able to use their knowledge and ability for responding probabilistic problem yet. Therefore, it is important for mathematics teachers to plan probabilistic learning which could optimize students probabilistic thinking ability.

  1. Probabilistic liver atlas construction.

    Science.gov (United States)

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

    2017-01-13

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

  2. Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling

    DEFF Research Database (Denmark)

    Opper, Manfred; Winther, Ole

    2001-01-01

    We develop a generalization of the Thouless-Anderson-Palmer (TAP) mean-field approach of disorder physics. which makes the method applicable to the computation of approximate averages in probabilistic models for real data. In contrast to the conventional TAP approach, where the knowledge...... of the distribution of couplings between the random variables is required, our method adapts to the concrete set of couplings. We show the significance of the approach in two ways: Our approach reproduces replica symmetric results for a wide class of toy models (assuming a nonglassy phase) with given disorder...... distributions in the thermodynamic limit. On the other hand, simulations on a real data model demonstrate that the method achieves more accurate predictions as compared to conventional TAP approaches....

  3. A Probabilistic Palimpsest Model of Visual Short-term Memory

    Science.gov (United States)

    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

  4. Probabilistic consequence model of accidenal or intentional chemical releases.

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Y.-S.; Samsa, M. E.; Folga, S. M.; Hartmann, H. M.

    2008-06-02

    In this work, general methodologies for evaluating the impacts of large-scale toxic chemical releases are proposed. The potential numbers of injuries and fatalities, the numbers of hospital beds, and the geographical areas rendered unusable during and some time after the occurrence and passage of a toxic plume are estimated on a probabilistic basis. To arrive at these estimates, historical accidental release data, maximum stored volumes, and meteorological data were used as inputs into the SLAB accidental chemical release model. Toxic gas footprints from the model were overlaid onto detailed population and hospital distribution data for a given region to estimate potential impacts. Output results are in the form of a generic statistical distribution of injuries and fatalities associated with specific toxic chemicals and regions of the United States. In addition, indoor hazards were estimated, so the model can provide contingency plans for either shelter-in-place or evacuation when an accident occurs. The stochastic distributions of injuries and fatalities are being used in a U.S. Department of Homeland Security-sponsored decision support system as source terms for a Monte Carlo simulation that evaluates potential measures for mitigating terrorist threats. This information can also be used to support the formulation of evacuation plans and to estimate damage and cleanup costs.

  5. Characterizing the topology of probabilistic biological networks.

    Science.gov (United States)

    Todor, Andrei; Dobra, Alin; Kahveci, Tamer

    2013-01-01

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

  6. A General Framework for Probabilistic Characterizing Formulae

    DEFF Research Database (Denmark)

    Sack, Joshua; Zhang, Lijun

    2012-01-01

    Recently, a general framework on characteristic formulae was proposed by Aceto et al. It offers a simple theory that allows one to easily obtain characteristic formulae of many non-probabilistic behavioral relations. Our paper studies their techniques in a probabilistic setting. We provide...... a general method for determining characteristic formulae of behavioral relations for probabilistic automata using fixed-point probability logics. We consider such behavioral relations as simulations and bisimulations, probabilistic bisimulations, probabilistic weak simulations, and probabilistic forward...

  7. A Conceptual Model of Leisure-Time Choice Behavior.

    Science.gov (United States)

    Bergier, Michel J.

    1981-01-01

    Methods of studying the gap between predisposition and actual behavior of consumers of spectator sports is discussed. A model is drawn from the areas of behavioral sciences, consumer behavior, and leisure research. The model is constructed around the premise that choice is primarily a function of personal, product, and environmental factors. (JN)

  8. A probabilistic Hu-Washizu variational principle

    Science.gov (United States)

    Liu, W. K.; Belytschko, T.; Besterfield, G. H.

    1987-01-01

    A Probabilistic Hu-Washizu Variational Principle (PHWVP) for the Probabilistic Finite Element Method (PFEM) is presented. This formulation is developed for both linear and nonlinear elasticity. The PHWVP allows incorporation of the probabilistic distributions for the constitutive law, compatibility condition, equilibrium, domain and boundary conditions into the PFEM. Thus, a complete probabilistic analysis can be performed where all aspects of the problem are treated as random variables and/or fields. The Hu-Washizu variational formulation is available in many conventional finite element codes thereby enabling the straightforward inclusion of the probabilistic features into present codes.

  9. Memristive Probabilistic Computing

    KAUST Repository

    Alahmadi, Hamzah

    2017-10-01

    In the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.

  10. Predicting coastal cliff erosion using a Bayesian probabilistic model

    Science.gov (United States)

    Hapke, Cheryl J.; Plant, Nathaniel G.

    2010-01-01

    Regional coastal cliff retreat is difficult to model due to the episodic nature of failures and the along-shore variability of retreat events. There is a growing demand, however, for predictive models that can be used to forecast areas vulnerable to coastal erosion hazards. Increasingly, probabilistic models are being employed that require data sets of high temporal density to define the joint probability density function that relates forcing variables (e.g. wave conditions) and initial conditions (e.g. cliff geometry) to erosion events. In this study we use a multi-parameter Bayesian network to investigate correlations between key variables that control and influence variations in cliff retreat processes. The network uses Bayesian statistical methods to estimate event probabilities using existing observations. Within this framework, we forecast the spatial distribution of cliff retreat along two stretches of cliffed coast in Southern California. The input parameters are the height and slope of the cliff, a descriptor of material strength based on the dominant cliff-forming lithology, and the long-term cliff erosion rate that represents prior behavior. The model is forced using predicted wave impact hours. Results demonstrate that the Bayesian approach is well-suited to the forward modeling of coastal cliff retreat, with the correct outcomes forecast in 70–90% of the modeled transects. The model also performs well in identifying specific locations of high cliff erosion, thus providing a foundation for hazard mapping. This approach can be employed to predict cliff erosion at time-scales ranging from storm events to the impacts of sea-level rise at the century-scale.

  11. PRECIS -- A probabilistic risk assessment system

    International Nuclear Information System (INIS)

    Peterson, D.M.; Knowlton, R.G. Jr.

    1996-01-01

    A series of computer tools has been developed to conduct the exposure assessment and risk characterization phases of human health risk assessments within a probabilistic framework. The tools are collectively referred to as the Probabilistic Risk Evaluation and Characterization Investigation System (PRECIS). With this system, a risk assessor can calculate the doses and risks associated with multiple environmental and exposure pathways, for both chemicals and radioactive contaminants. Exposure assessment models in the system account for transport of contaminants to receptor points from a source zone originating in unsaturated soils above the water table. In addition to performing calculations of dose and risk based on initial concentrations, PRECIS can also be used in an inverse manner to compute soil concentrations in the source area that must not be exceeded if prescribed limits on dose or risk are to be met. Such soil contaminant levels, referred to as soil guidelines, are computed for both single contaminants and chemical mixtures and can be used as action levels or cleanup levels. Probabilistic estimates of risk, dose and soil guidelines are derived using Monte Carlo techniques

  12. Metro passengers’ route choice model and its application considering perceived transfer threshold

    Science.gov (United States)

    Jin, Fanglei; Zhang, Yongsheng; Liu, Shasha

    2017-01-01

    With the rapid development of the Metro network in China, the greatly increased route alternatives make passengers’ route choice behavior and passenger flow assignment more complicated, which presents challenges to the operation management. In this paper, a path sized logit model is adopted to analyze passengers’ route choice preferences considering such parameters as in-vehicle time, number of transfers, and transfer time. Moreover, the “perceived transfer threshold” is defined and included in the utility function to reflect the penalty difference caused by transfer time on passengers’ perceived utility under various numbers of transfers. Next, based on the revealed preference data collected in the Guangzhou Metro, the proposed model is calibrated. The appropriate perceived transfer threshold value and the route choice preferences are analyzed. Finally, the model is applied to a personalized route planning case to demonstrate the engineering practicability of route choice behavior analysis. The results show that the introduction of the perceived transfer threshold is helpful to improve the model’s explanatory abilities. In addition, personalized route planning based on route choice preferences can meet passengers’ diversified travel demands. PMID:28957376

  13. Ignorability in Statistical and Probabilistic Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2005-01-01

    When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...

  14. Analytical probabilistic modeling of RBE-weighted dose for ion therapy

    Science.gov (United States)

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

    2017-12-01

    Particle therapy is especially prone to uncertainties. This issue is usually addressed with uncertainty quantification and minimization techniques based on scenario sampling. For proton therapy, however, it was recently shown that it is also possible to use closed-form computations based on analytical probabilistic modeling (APM) for this purpose. APM yields unique features compared to sampling-based approaches, motivating further research in this context. This paper demonstrates the application of APM for intensity-modulated carbon ion therapy to quantify the influence of setup and range uncertainties on the RBE-weighted dose. In particular, we derive analytical forms for the nonlinear computations of the expectation value and variance of the RBE-weighted dose by propagating linearly correlated Gaussian input uncertainties through a pencil beam dose calculation algorithm. Both exact and approximation formulas are presented for the expectation value and variance of the RBE-weighted dose and are subsequently studied in-depth for a one-dimensional carbon ion spread-out Bragg peak. With V and B being the number of voxels and pencil beams, respectively, the proposed approximations induce only a marginal loss of accuracy while lowering the computational complexity from order O(V × B^2) to O(V × B) for the expectation value and from O(V × B^4) to O(V × B^2) for the variance of the RBE-weighted dose. Moreover, we evaluated the approximated calculation of the expectation value and standard deviation of the RBE-weighted dose in combination with a probabilistic effect-based optimization on three patient cases considering carbon ions as radiation modality against sampled references. The resulting global γ-pass rates (2 mm,2%) are > 99.15% for the expectation value and > 94.95% for the standard deviation of the RBE-weighted dose, respectively. We applied the derived analytical model to carbon ion treatment planning, although the concept is in general applicable to other

  15. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

    Science.gov (United States)

    Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

    2017-10-01

    Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

  16. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

    Directory of Open Access Journals (Sweden)

    Marcello Benedetti

    2017-11-01

    Full Text Available Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

  17. Development of the PRO-LOCA Probabilistic Fracture Mechanics Code, MERIT Final Report

    International Nuclear Information System (INIS)

    Scott, Paul; Kurth, Robert; Cox, Andrew; Olson, Rick; Rudland, Dave

    2010-12-01

    The MERIT project has been an internationally financed program with the main purpose of developing probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code named PRO-LOCA. The principal objective of the project has been to develop probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code. The MERIT program has produced a code named PRO-LOCA with the following features: - Crack initiation models for fatigue or stress corrosion cracking for previously unflawed material. - Subcritical crack growth models for fatigue and stress corrosion cracking for both initiated and pre-existing circumferential defects. - Models for flaw detection by inspections and leak detection. - Crack stability. The PRO-LOCA code can thus predict the leak or break frequency for the whole sequence of initiation, subcritical crack growth until wall penetration and leakage, instability of the through-wall crack (pipe rupture). The outcome of the PRO-LOCA code are a sequence of failure frequencies which represents the probability of surface crack developing, a through-wall crack developing and six different sizes of crack opening areas corresponding to different leak flow rates or LOCA categories. Note that the level of quality assurance of the PRO-LOCA code is such that the code in its current state of development is considered to be more of a research code than a regulatory tool.

  18. Development of the PRO-LOCA Probabilistic Fracture Mechanics Code, MERIT Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Scott, Paul; Kurth, Robert; Cox, Andrew; Olson, Rick (Battelle Columbus (United States)); Rudland, Dave (Nuclear Regulatory Commission (United States))

    2010-12-15

    The MERIT project has been an internationally financed program with the main purpose of developing probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code named PRO-LOCA. The principal objective of the project has been to develop probabilistic models for piping failure of nuclear components and to include these models in a probabilistic code. The MERIT program has produced a code named PRO-LOCA with the following features: - Crack initiation models for fatigue or stress corrosion cracking for previously unflawed material. - Subcritical crack growth models for fatigue and stress corrosion cracking for both initiated and pre-existing circumferential defects. - Models for flaw detection by inspections and leak detection. - Crack stability. The PRO-LOCA code can thus predict the leak or break frequency for the whole sequence of initiation, subcritical crack growth until wall penetration and leakage, instability of the through-wall crack (pipe rupture). The outcome of the PRO-LOCA code are a sequence of failure frequencies which represents the probability of surface crack developing, a through-wall crack developing and six different sizes of crack opening areas corresponding to different leak flow rates or LOCA categories. Note that the level of quality assurance of the PRO-LOCA code is such that the code in its current state of development is considered to be more of a research code than a regulatory tool.

  19. An individual-based probabilistic model for simulating fisheries population dynamics

    Directory of Open Access Journals (Sweden)

    Jie Cao

    2016-12-01

    Full Text Available The purpose of stock assessment is to support managers to provide intelligent decisions regarding removal from fish populations. Errors in assessment models may have devastating impacts on the population fitness and negative impacts on the economy of the resource users. Thus, accuracte estimations of population size, growth rates are critical for success. Evaluating and testing the behavior and performance of stock assessment models and assessing the consequences of model mis-specification and the impact of management strategies requires an operating model that accurately describe the dynamics of the target species, and can resolve spatial and seasonal changes. In addition, the most thorough evaluations of assessment models use an operating model that takes a different form than the assessment model. This paper presents an individual-based probabilistic model used to simulate the complex dynamics of populations and their associated fisheries. Various components of population dynamics are expressed as random Bernoulli trials in the model and detailed life and fishery histories of each individual are tracked over their life span. The simulation model is designed to be flexible so it can be used for different species and fisheries. It can simulate mixing among multiple stocks and link stock-recruit relationships to environmental factors. Furthermore, the model allows for flexibility in sub-models (e.g., growth and recruitment and model assumptions (e.g., age- or size-dependent selectivity. This model enables the user to conduct various simulation studies, including testing the performance of assessment models under different assumptions, assessing the impacts of model mis-specification and evaluating management strategies.

  20. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.

    Science.gov (United States)

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

    2017-04-01

    Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study. © 2016 Society for Risk Analysis.