WorldWideScience

Sample records for bayesian network expansion

  1. Inference in hybrid Bayesian networks

    DEFF Research Database (Denmark)

    Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;

    2009-01-01

    Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

  2. Irregular-Time Bayesian Networks

    CERN Document Server

    Ramati, Michael

    2012-01-01

    In many fields observations are performed irregularly along time, due to either measurement limitations or lack of a constant immanent rate. While discrete-time Markov models (as Dynamic Bayesian Networks) introduce either inefficient computation or an information loss to reasoning about such processes, continuous-time Markov models assume either a discrete state space (as Continuous-Time Bayesian Networks), or a flat continuous state space (as stochastic dif- ferential equations). To address these problems, we present a new modeling class called Irregular-Time Bayesian Networks (ITBNs), generalizing Dynamic Bayesian Networks, allowing substantially more compact representations, and increasing the expressivity of the temporal dynamics. In addition, a globally optimal solution is guaranteed when learning temporal systems, provided that they are fully observed at the same irregularly spaced time-points, and a semiparametric subclass of ITBNs is introduced to allow further adaptation to the irregular nature of t...

  3. Neuronanatomy, neurology and Bayesian networks

    OpenAIRE

    Bielza Lozoya, Maria Concepcion

    2014-01-01

    Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related qualit...

  4. Bayesian networks and food security - An introduction

    NARCIS (Netherlands)

    Stein, A.

    2004-01-01

    This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision sup

  5. Bayesian Network--Response Regression

    OpenAIRE

    WANG, LU; Durante, Daniele; Dunson, David B.

    2016-01-01

    There is an increasing interest in learning how human brain networks vary with continuous traits (e.g., personality, cognitive abilities, neurological disorders), but flexible procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and Gaussian process priors to allow flexible shifts of the conditional expectation for a network-valued random variable across the feature space, while including subject-specific random eff...

  6. Plug & Play object oriented Bayesian networks

    DEFF Research Database (Denmark)

    Bangsø, Olav; Flores, J.; Jensen, Finn Verner

    2003-01-01

    Object oriented Bayesian networks have proven themselves useful in recent years. The idea of applying an object oriented approach to Bayesian networks has extended their scope to larger domains that can be divided into autonomous but interrelated entities. Object oriented Bayesian networks have...... been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... by constructing a junction tree from this network. In this paper we propose a method for translating directly from object oriented Bayesian networks to junction trees, avoiding the intermediate translation. We pursue two main purposes: firstly, to maintain the original structure organized in an instance tree...

  7. Space Shuttle RTOS Bayesian Network

    Science.gov (United States)

    Morris, A. Terry; Beling, Peter A.

    2001-01-01

    With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores

  8. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

    2004-01-01

    We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...... and differentiating these circuits in time linear in their size. We report on experimental results showing the successful compilation, and efficient inference, on relational Bayesian networks whose {\\primula}--generated propositional instances have thousands of variables, and whose jointrees have clusters...

  9. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

    , and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...... primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples...

  10. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

    under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented on model construction and verification, modeling techniques and tricks, learning......Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...... sections, in addition to fully-updated examples, tables, figures, and a revised appendix. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning...

  11. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  12. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  13. Bayesian Network for multiple hypthesis tracking

    NARCIS (Netherlands)

    W.P. Zajdel; B.J.A. Kröse

    2002-01-01

    For a flexible camera-to-camera tracking of multiple objects we model the objects behavior with a Bayesian network and combine it with the multiple hypohesis framework that associates observations with objects. Bayesian networks offer a possibility to factor complex, joint distributions into a produ

  14. An Intuitive Dashboard for Bayesian Network Inference

    International Nuclear Information System (INIS)

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++

  15. An Intuitive Dashboard for Bayesian Network Inference

    Science.gov (United States)

    Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

    2014-03-01

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

  16. Bayesian networks in educational assessment

    CERN Document Server

    Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M

    2015-01-01

    Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...

  17. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

    2006-01-01

    We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...... by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose PRIMULA--generated propositional instances have thousands of variables, and whose jointrees have clusters...

  18. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  19. An introduction to Gaussian Bayesian networks.

    Science.gov (United States)

    Grzegorczyk, Marco

    2010-01-01

    The extraction of regulatory networks and pathways from postgenomic data is important for drug -discovery and development, as the extracted pathways reveal how genes or proteins regulate each other. Following up on the seminal paper of Friedman et al. (J Comput Biol 7:601-620, 2000), Bayesian networks have been widely applied as a popular tool to this end in systems biology research. Their popularity stems from the tractability of the marginal likelihood of the network structure, which is a consistent scoring scheme in the Bayesian context. This score is based on an integration over the entire parameter space, for which highly expensive computational procedures have to be applied when using more complex -models based on differential equations; for example, see (Bioinformatics 24:833-839, 2008). This chapter gives an introduction to reverse engineering regulatory networks and pathways with Gaussian Bayesian networks, that is Bayesian networks with the probabilistic BGe scoring metric [see (Geiger and Heckerman 235-243, 1995)]. In the BGe model, the data are assumed to stem from a Gaussian distribution and a normal-Wishart prior is assigned to the unknown parameters. Gaussian Bayesian network methodology for analysing static observational, static interventional as well as dynamic (observational) time series data will be described in detail in this chapter. Finally, we apply these Bayesian network inference methods (1) to observational and interventional flow cytometry (protein) data from the well-known RAF pathway to evaluate the global network reconstruction accuracy of Bayesian network inference and (2) to dynamic gene expression time series data of nine circadian genes in Arabidopsis thaliana to reverse engineer the unknown regulatory network topology for this domain. PMID:20824469

  20. The Diagnosis of Reciprocating Machinery by Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    A Bayesian Network is a reasoning tool based on probability theory and has many advantages that other reasoning tools do not have. This paper discusses the basic theory of Bayesian networks and studies the problems in constructing Bayesian networks. The paper also constructs a Bayesian diagnosis network of a reciprocating compressor. The example helps us to draw a conclusion that Bayesian diagnosis networks can diagnose reciprocating machinery effectively.

  1. Fuzzy Functional Dependencies and Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    LIU WeiYi(刘惟一); SONG Ning(宋宁)

    2003-01-01

    Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.

  2. Scaling Bayesian network discovery through incremental recovery

    NARCIS (Netherlands)

    Castelo, J.R.; Siebes, A.P.J.M.

    1999-01-01

    Bayesian networks are a type of graphical models that, e.g., allow one to analyze the interaction among the variables in a database. A well-known problem with the discovery of such models from a database is the ``problem of high-dimensionality''. That is, the discovery of a network from a database w

  3. Learning Bayesian Networks from Correlated Data

    Science.gov (United States)

    Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

    2016-05-01

    Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

  4. Most frugal explanations in Bayesian networks

    NARCIS (Netherlands)

    Kwisthout, J.H.P.

    2015-01-01

    Inferring the most probable explanation to a set of variables, given a partial observation of the remaining variables, is one of the canonical computational problems in Bayesian networks, with widespread applications in AI and beyond. This problem, known as MAP, is computationally intractable (NP-ha

  5. On local optima in learning bayesian networks

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Kocka, Tomas; Pena, Jose

    2003-01-01

    This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...

  6. Modelling crime linkage with Bayesian networks

    NARCIS (Netherlands)

    J. de Zoete; M. Sjerps; D. Lagnado; N. Fenton

    2015-01-01

    When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model

  7. Bayesian Overlapping Community Detection in Dynamic Networks

    CERN Document Server

    Ghorbani, Mahsa; Khodadadi, Ali

    2016-01-01

    Detecting community structures in social networks has gained considerable attention in recent years. However, lack of prior knowledge about the number of communities, and their overlapping nature have made community detection a challenging problem. Moreover, many of the existing methods only consider static networks, while most of real world networks are dynamic and evolve over time. Hence, finding consistent overlapping communities in dynamic networks without any prior knowledge about the number of communities is still an interesting open research problem. In this paper, we present an overlapping community detection method for dynamic networks called Dynamic Bayesian Overlapping Community Detector (DBOCD). DBOCD assumes that in every snapshot of network, overlapping parts of communities are dense areas and utilizes link communities instead of common node communities. Using Recurrent Chinese Restaurant Process and community structure of the network in the last snapshot, DBOCD simultaneously extracts the numbe...

  8. Bayesian network learning for natural hazard assessments

    Science.gov (United States)

    Vogel, Kristin

    2016-04-01

    Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

  9. Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Yufei Huang

    2007-06-01

    Full Text Available Reverse engineering of genetic regulatory networks from time series microarray data are investigated. We propose a dynamic Bayesian networks (DBNs modeling and a full Bayesian learning scheme. The proposed DBN directly models the continuous expression levels and also is associated with parameters that indicate the degree as well as the type of regulations. To learn the network from data, we proposed a reversible jump Markov chain Monte Carlo (RJMCMC algorithm. The RJMCMC algorithm can provide not only more accurate inference results than the deterministic alternative algorithms but also an estimate of the a posteriori probabilities (APPs of the network topology. The estimated APPs provide useful information on the confidence of the inferred results and can also be used for efficient Bayesian data integration. The proposed approach is tested on yeast cell cycle microarray data and the results are compared with the KEGG pathway map.

  10. Bayesian networks for enterprise risk assessment

    CERN Document Server

    Bonafede, C E

    2006-01-01

    According to different typologies of activity and priority, risks can assume diverse meanings and it can be assessed in different ways. In general risk is measured in terms of a probability combination of an event (frequency) and its consequence (impact). To estimate the frequency and the impact (severity) historical data or expert opinions (either qualitative or quantitative data) are used. Moreover qualitative data must be converted in numerical values to be used in the model. In the case of enterprise risk assessment the considered risks are, for instance, strategic, operational, legal and of image, which many times are difficult to be quantified. So in most cases only expert data, gathered by scorecard approaches, are available for risk analysis. The Bayesian Network is a useful tool to integrate different information and in particular to study the risk's joint distribution by using data collected from experts. In this paper we want to show a possible approach for building a Bayesian networks in the parti...

  11. Distributed Bayesian Networks for User Modeling

    DEFF Research Database (Denmark)

    Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang;

    2006-01-01

    The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...

  12. Software Health Management with Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole; Schumann, JOhann

    2011-01-01

    Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

  13. Learning Bayesian networks using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Chen Fei; Wang Xiufeng; Rao Yimei

    2007-01-01

    A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

  14. Improving Environmental Scanning Systems Using Bayesian Networks

    OpenAIRE

    Simon Welter; Jörg H. Mayer; Reiner Quick

    2013-01-01

    As companies’ environment is becoming increasingly volatile, scanning systems gain in importance. We propose a hybrid process model for such systems' information gathering and interpretation tasks that combines quantitative information derived from regression analyses and qualitative knowledge from expert interviews. For the latter, we apply Bayesian networks. We derive the need for such a hybrid process model from a literature review. We lay out our model to find a suitable set of business e...

  15. Forming Object Concept Using Bayesian Network

    OpenAIRE

    Nakamura, Tomoaki; Nagai, Takayuki

    2010-01-01

    This chapter hase discussed a novel framework for object understanding. Implementation of the proposed framework using Bayesian Network has been presented. Although the result given in this paper is preliminary one, we have shown that the system can form object concept by observing the performance by human hands. The on-line learning is left for the future works. Moreover the model should be extended so that it can represent the object usage and work objects.

  16. Bayesian belief networks in business continuity.

    Science.gov (United States)

    Phillipson, Frank; Matthijssen, Edwin; Attema, Thomas

    2014-01-01

    Business continuity professionals aim to mitigate the various challenges to the continuity of their company. The goal is a coherent system of measures that encompass detection, prevention and recovery. Choices made in one part of the system affect other parts as well as the continuity risks of the company. In complex organisations, however, these relations are far from obvious. This paper proposes the use of Bayesian belief networks to expose these relations, and presents a modelling framework for this approach. PMID:25193453

  17. Option Pricing Using Bayesian Neural Networks

    CERN Document Server

    Pires, Michael Maio

    2007-01-01

    Options have provided a field of much study because of the complexity involved in pricing them. The Black-Scholes equations were developed to price options but they are only valid for European styled options. There is added complexity when trying to price American styled options and this is why the use of neural networks has been proposed. Neural Networks are able to predict outcomes based on past data. The inputs to the networks here are stock volatility, strike price and time to maturity with the output of the network being the call option price. There are two techniques for Bayesian neural networks used. One is Automatic Relevance Determination (for Gaussian Approximation) and one is a Hybrid Monte Carlo method, both used with Multi-Layer Perceptrons.

  18. Revealing ecological networks using Bayesian network inference algorithms.

    Science.gov (United States)

    Milns, Isobel; Beale, Colin M; Smith, V Anne

    2010-07-01

    Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis. PMID:20715607

  19. Bayesian Recurrent Neural Network for Language Modeling.

    Science.gov (United States)

    Chien, Jen-Tzung; Ku, Yuan-Chu

    2016-02-01

    A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.

  20. Bayesian information fusion networks for biosurveillance applications.

    Science.gov (United States)

    Mnatsakanyan, Zaruhi R; Burkom, Howard S; Coberly, Jacqueline S; Lombardo, Joseph S

    2009-01-01

    This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.

  1. Bayesian networks as a tool for epidemiological systems analysis

    OpenAIRE

    Lewis, F.I.

    2012-01-01

    Bayesian network analysis is a form of probabilistic modeling which derives from empirical data a directed acyclic graph (DAG) describing the dependency structure between random variables. Bayesian networks are increasingly finding application in areas such as computational and systems biology, and more recently in epidemiological analyses. The key distinction between standard empirical modeling approaches, such as generalised linear modeling, and Bayesian network analyses is that the latter ...

  2. Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks

    CERN Document Server

    Andrade, Daniel

    2012-01-01

    We present a new method to propagate lower bounds on conditional probability distributions in conventional Bayesian networks. Our method guarantees to provide outer approximations of the exact lower bounds. A key advantage is that we can use any available algorithms and tools for Bayesian networks in order to represent and infer lower bounds. This new method yields results that are provable exact for trees with binary variables, and results which are competitive to existing approximations in credal networks for all other network structures. Our method is not limited to a specific kind of network structure. Basically, it is also not restricted to a specific kind of inference, but we restrict our analysis to prognostic inference in this article. The computational complexity is superior to that of other existing approaches.

  3. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  4. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge;

    2009-01-01

    Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

  5. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  6. Email Spam Filter using Bayesian Neural Networks

    Directory of Open Access Journals (Sweden)

    Nibedita Chakraborty

    2012-03-01

    Full Text Available Nowadays, e-mail is widely becoming one of the fastest and most economical forms of communication but they are prone to be misused. One such misuse is the posting of unsolicited, unwanted e-mails known as spam or junk e-mails. This paper presents and discusses an implementation of a spam filtering system. The idea is to use a neural network which will be trained to recognize different forms of often used words in spam mails. The Bayesian ANN is trained with finite sample sizes to approximate the ideal observer. This strategy can provide improved filtering of Spam than existing Static Spam filters.

  7. Bayesian Network Based XP Process Modelling

    Directory of Open Access Journals (Sweden)

    Mohamed Abouelela

    2010-07-01

    Full Text Available A Bayesian Network based mathematical model has been used for modelling Extreme Programmingsoftware development process. The model is capable of predicting the expected finish time and theexpected defect rate for each XP release. Therefore, it can be used to determine the success/failure of anyXP Project. The model takes into account the effect of three XP practices, namely: Pair Programming,Test Driven Development and Onsite Customer practices. The model’s predictions were validated againsttwo case studies. Results show the precision of our model especially in predicting the project finish time.

  8. Some Quantum Information Inequalities from a Quantum Bayesian Networks Perspective

    OpenAIRE

    Tucci, Robert R.

    2012-01-01

    This is primarily a pedagogical paper. The paper re-visits some well-known quantum information theory inequalities. It does this from a quantum Bayesian networks perspective. The paper illustrates some of the benefits of using quantum Bayesian networks to discuss quantum SIT (Shannon Information Theory).

  9. Non-homogeneous dynamic Bayesian networks for continuous data

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2011-01-01

    Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with c

  10. A Gaussian Mixed Model for Learning Discrete Bayesian Networks.

    Science.gov (United States)

    Balov, Nikolay

    2011-02-01

    In this paper we address the problem of learning discrete Bayesian networks from noisy data. Considered is a graphical model based on mixture of Gaussian distributions with categorical mixing structure coming from a discrete Bayesian network. The network learning is formulated as a Maximum Likelihood estimation problem and performed by employing an EM algorithm. The proposed approach is relevant to a variety of statistical problems for which Bayesian network models are suitable - from simple regression analysis to learning gene/protein regulatory networks from microarray data.

  11. Application of Bayesian Network Learning Methods to Land Resource Evaluation

    Institute of Scientific and Technical Information of China (English)

    HUANG Jiejun; HE Xiaorong; WAN Youchuan

    2006-01-01

    Bayesian network has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to deal with prediction, classification and clustering. Firstly, this paper presented an overview of Bayesian network and its characteristics, and discussed how to learn a Bayesian network structure from given data, and then constructed a Bayesian network model for land resource evaluation with expert knowledge and the dataset. The experimental results based on the test dataset are that evaluation accuracy is 87.5%, and Kappa index is 0.826. All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.

  12. Learning Bayesian Networks from Data by Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Learning Bayesian network is an NP-hard problem. When the number of variables is large, the process of searching optimal network structure could be very time consuming and tends to return a structure which is local optimal. The particle swarm optimization (PSO) was introduced to the problem of learning Bayesian networks and a novel structure learning algorithm using PSO was proposed. To search in directed acyclic graphs spaces efficiently, a discrete PSO algorithm especially for structure learning was proposed based on the characteristics of Bayesian networks. The results of experiments show that our PSO based algorithm is fast for convergence and can obtain better structures compared with genetic algorithm based algorithms.

  13. Using consensus bayesian network to model the reactive oxygen species regulatory pathway.

    Directory of Open Access Journals (Sweden)

    Liangdong Hu

    Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.

  14. Learning Bayesian network structure with immune algorithm

    Institute of Scientific and Technical Information of China (English)

    Zhiqiang Cai; Shubin Si; Shudong Sun; Hongyan Dui

    2015-01-01

    Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This pa-per proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Further-more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Final y, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

  15. Bayesian network learning with cutting planes

    CERN Document Server

    Cussens, James

    2012-01-01

    The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log marginal likelihood (BDe score). Integer programming, specifically the SCIP framework, is used to solve this optimisation problem. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Finding good cutting planes is the key to the success of the approach -the search for such cutting planes is effected using a sub-IP. Results show that this is a particularly fast method for exact BN learning.

  16. Logistic regression against a divergent Bayesian network

    Directory of Open Access Journals (Sweden)

    Noel Antonio Sánchez Trujillo

    2015-01-01

    Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.

  17. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  18. Inference of Gene Regulatory Network Based on Local Bayesian Networks

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Chen, Luonan

    2016-01-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  19. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  20. ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Chaudhari

    2011-06-01

    Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.

  1. Developing Large-Scale Bayesian Networks by Composition

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale...

  2. Designing Resource-Bounded Reasoners using Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — In this work we are concerned with the conceptual design of large-scale diagnostic and health management systems that use Bayesian networks. While they are...

  3. Macroscopic Models of Clique Tree Growth for Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to...

  4. Using Bayesian Networks to Improve Knowledge Assessment

    Science.gov (United States)

    Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

    2013-01-01

    In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

  5. A Bayesian Networks in Intrusion Detection Systems

    Directory of Open Access Journals (Sweden)

    M. Mehdi

    2007-01-01

    Full Text Available Intrusion detection systems (IDSs have been widely used to overcome security threats in computer networks. Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behaviour which may result in a large number of false alarms caused by incorrect classification of events in current systems. We propose a new approach of an anomaly Intrusion detection system (IDS. It consists of building a reference behaviour model and the use of a Bayesian classification procedure associated to unsupervised learning algorithm to evaluate the deviation between current and reference behaviour. Continuous re-estimation of model parameters allows for real time operation. The use of recursive Log-likelihood and entropy estimation as a measure for monitoring model degradation related with behavior changes and the associated model update show that the accuracy of the event classification process is significantly improved using our proposed approach for reducing the missing-alarm.

  6. HEURISTIC DISCRETIZATION METHOD FOR BAYESIAN NETWORKS

    Directory of Open Access Journals (Sweden)

    Mariana D.C. Lima

    2014-01-01

    Full Text Available Bayesian Network (BN is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as division by quartiles. In this article we present a discretization using a heuristic that identifies events called peak and valley. Genetic Algorithm was used to identify these events having the minimization of the error between the estimated average for BN and the actual value of the numeric variable output as the objective function. The BN has been modeled from a database of Bit’s Rate of Penetration of the Brazilian pre-salt layer with 5 numerical variables and one categorical variable, using the proposed discretization and the division of the data by the quartiles. The results show that the proposed heuristic discretization has higher accuracy than the quartiles discretization.

  7. Study of Online Bayesian Networks Learning in a Multi-Agent System

    Directory of Open Access Journals (Sweden)

    Yonghui Cao

    2013-01-01

    Full Text Available This paper introduces online Bayesian network learning in detail. The structural and parametric learning abilities of the online Bayesian network learning are explored. The paper starts with revisiting the multi-agent self-organization problem and the proposed solution. Then, we explain the proposed Bayesian network learning, three scoring functions, namely Log-Likelihood, Minimum description length, and Bayesian scores.

  8. Algorithm of capacity expansion on networks optimization

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The complexity of the algorithm is also analyzed. As required by the algorithm, some virtual sources are imported through the whole positive direction subsection in the network, in which a certain capacity value is given. Simultaneously, a corresponding capacity-expanded network is constructed to search all minimum cutsets. For a given maximum flow value of the network, the authors found an adjustment value of each minimum cutset arc's group with gradually reverse calculation and marked out the feasible flow on the capacity-extended networks again with the adjustment value increasing. All this has been done repeatedly until the original topology structure is resumed. So the algorithm can increase the capacity of networks effectively and solve the bottleneck problem of networks.

  9. Fault Diagnosis of an Intelligent Building Facility Using Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qi-ding; XU Jin-yu; BAI Er-lei

    2008-01-01

    There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model for fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.

  10. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks.......It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...

  11. Bayesian networks for mastitis management on dairy farms

    NARCIS (Netherlands)

    Steeneveld, Wilma; van der Gaag, Linda; Barkema, H.W.; Hogeveen, H.

    2009-01-01

    This manuscript presents the idea of providing dairy farmers with probability distributions to support decisions on mastitis management and illustrates its feasibility by two applications. Naive Bayesian networks were developed for both applications. The networks in the first application were used t

  12. Bayesian networks as a tool for epidemiological systems analysis

    Science.gov (United States)

    Lewis, F. I.

    2012-11-01

    Bayesian network analysis is a form of probabilistic modeling which derives from empirical data a directed acyclic graph (DAG) describing the dependency structure between random variables. Bayesian networks are increasingly finding application in areas such as computational and systems biology, and more recently in epidemiological analyses. The key distinction between standard empirical modeling approaches, such as generalised linear modeling, and Bayesian network analyses is that the latter attempts not only to identify statistically associated variables, but to additionally, and empirically, separate these into those directly and indirectly dependent with one or more outcome variables. Such discrimination is vastly more ambitious but has the potential to reveal far more about key features of complex disease systems. Applying Bayesian network modeling to biological and medical data has considerable computational demands, combined with the need to ensure robust model selection given the vast model space of possible DAGs. These challenges require the use of approximation techniques, such as the Laplace approximation, Markov chain Monte Carlo simulation and parametric bootstrapping, along with computational parallelization. A case study in structure discovery - identification of an optimal DAG for given data - is presented which uses additive Bayesian networks to explore veterinary disease data of industrial and medical relevance.

  13. Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data.

    Science.gov (United States)

    Shah, Abhik; Woolf, Peter

    2009-06-01

    In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

  14. Uncertainty Modeling Based on Bayesian Network in Ontology Mapping

    Institute of Scientific and Technical Information of China (English)

    LI Yuhua; LIU Tao; SUN Xiaolin

    2006-01-01

    How to deal with uncertainty is crucial in exact concept mapping between ontologies. This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks (BN). In our approach, ontology Web language (OWL) is extended to add probabilistic markups for attaching probability information, the source and target ontologies (expressed by patulous OWL) are translated into bayesian networks (BNs), the mapping between the two ontologies can be digged out by constructing the conditional probability tables (CPTs) of the BN using a improved algorithm named I-IPFP based on iterative proportional fitting procedure (IPFP). The basic idea of this framework and algorithm are validated by positive results from computer experiments.

  15. The application of Bayesian networks in natural hazard analyses

    Directory of Open Access Journals (Sweden)

    K. Vogel

    2013-10-01

    Full Text Available In natural hazards we face several uncertainties due to our lack of knowledge and/or the intrinsic randomness of the underlying natural processes. Nevertheless, deterministic analysis approaches are still widely used in natural hazard assessments, with the pitfall of underestimating the hazard with potentially disastrous consequences. In this paper we show that the Bayesian network approach offers a flexible framework for capturing and expressing a broad range of different uncertainties as those encountered in natural hazard assessments. Although well studied in theory, the application of Bayesian networks on real-world data is often not straightforward and requires specific tailoring and adaption of existing algorithms. We demonstrate by way of three case studies (a ground motion model for a seismic hazard analysis, a flood damage assessment, and a landslide susceptibility study the applicability of Bayesian networks across different domains showcasing various properties and benefits of the Bayesian network framework. We offer suggestions as how to tackle practical problems arising along the way, mainly concentrating on the handling of continuous variables, missing observations, and the interaction of both. We stress that our networks are completely data-driven, although prior domain knowledge can be included if desired.

  16. Bayesian网中的独立关系%The Independence Relations in Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    王飞; 刘大有; 卢奕男; 薛万欣

    2001-01-01

    Bayesian networks are compact representation of joint probabilistic distribution. Independence is soul of Bayesian networks because it enables to save storage space,to reduce computational complexity and to simplify knowledge acquisition and modeling. In this paper,we discuss three kinds of independences in Bayesian networks :conditional independence,context-specific independence and causal influence independence.

  17. Reasoning under uncertainty in natural language dialogue using Bayesian networks

    NARCIS (Netherlands)

    Keizer, Simon

    2003-01-01

    In which uncertainty in natural language dialogue is introduced as the central problem in the research described in this thesis. The idea of using of Bayesian networks is hypothesised as a possible solution to this problem. Dialogue acts are presented as the central notion in our approach to dialogu

  18. Applying Bayesian networks in practical customer satisfaction studies

    NARCIS (Netherlands)

    Jaronski, W.; Bloemer, J.M.M.; Vanhoof, K.; Wets, G.

    2004-01-01

    This chapter presents an application of Bayesian network technology in an empirical customer satisfaction study. The findings of the study should provide insight to the importance of product/service dimensions in terms of the strength of their influence on overall (dis)satisfaction. To this end we a

  19. Exploiting sensitivity analysis in Bayesian networks for consumer satisfaction study

    NARCIS (Netherlands)

    Jaronski, W.; Bloemer, J.M.M.; Vanhoof, K.; Wets, G.

    2004-01-01

    The paper presents an application of Bayesian network technology in a empirical customer satisfaction study. The findings of the study should provide insight as to the importance of product/service dimensions in terms of the strength of their influence on overall satisfaction. To this end we apply a

  20. Nursing Home Care Quality: Insights from a Bayesian Network Approach

    Science.gov (United States)

    Goodson, Justin; Jang, Wooseung; Rantz, Marilyn

    2008-01-01

    Purpose: The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures…

  1. Implementation of an Adaptive Learning System Using a Bayesian Network

    Science.gov (United States)

    Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki

    2015-01-01

    An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…

  2. A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

    Institute of Scientific and Technical Information of China (English)

    徐俊刚; 赵越; 陈健; 韩超

    2015-01-01

    Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial-time (NP) hard problem. To solve this problem, many heuristic algorithms have been proposed, and some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction and use restriction. This makes it difficult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into the Markov chain Monte Carlo (MCMC) algorithm and propose an algorithm called Constrained MCMC (C-MCMC) algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined: existence of parent node, absence of parent node, and distribution knowledge including the conditional probability distribution (CPD) of edges and the probability distribution (PD) of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.

  3. A Bayesian network approach to coastal storm impact modeling

    NARCIS (Netherlands)

    Jäger, W.S.; Den Heijer, C.; Bolle, A.; Hanea, A.M.

    2015-01-01

    In this paper we develop a Bayesian network (BN) that relates offshore storm conditions to their accompagnying flood characteristics and damages to residential buildings, following on the trend of integrated flood impact modeling. It is based on data from hydrodynamic storm simulations, information

  4. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  5. Differential gene co-expression networks via Bayesian biclustering models

    OpenAIRE

    Gao, Chuan; Zhao, Shiwen; McDowell, Ian C.; Brown, Christopher D.; Barbara E Engelhardt

    2014-01-01

    Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes whose covariation may be observed in only a subset of the samples. Our biclustering me...

  6. Bayesian variable selection and data integration for biological regulatory networks

    OpenAIRE

    Jensen, Shane T; Chen, Guang; Stoeckert, Jr, Christian J.

    2007-01-01

    A substantial focus of research in molecular biology are gene regulatory networks: the set of transcription factors and target genes which control the involvement of different biological processes in living cells. Previous statistical approaches for identifying gene regulatory networks have used gene expression data, ChIP binding data or promoter sequence data, but each of these resources provides only partial information. We present a Bayesian hierarchical model that integrates all three dat...

  7. Research of Gene Regulatory Network with Multi-Time Delay Based on Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    LIU Bei; MENG Fanjiang; LI Yong; LIU Liyan

    2008-01-01

    The gene regulatory network was reconstructed according to time-series microarray data getting from hybridization at different time between gene chips to analyze coordination and restriction between genes. An algorithm for controlling the gene expression regulatory network of the whole cell was designed using Bayesian network which provides an effective aided analysis for gene regulatory network.

  8. Using Bayesian networks to support decision-focused information retrieval

    Energy Technology Data Exchange (ETDEWEB)

    Lehner, P.; Elsaesser, C.; Seligman, L. [Mitre Corp., McLean, VA (United States)

    1996-12-31

    This paper has described an approach to controlling the process of pulling data/information from distributed data bases in a way that is specific to a persons specific decision making context. Our prototype implementation of this approach uses a knowledge-based planner to generate a plan, an automatically constructed Bayesian network to evaluate the plan, specialized processing of the network to derive key information items that would substantially impact the evaluation of the plan (e.g., determine that replanning is needed), automated construction of Standing Requests for Information (SRIs) which are automated functions that monitor changes and trends in distributed data base that are relevant to the key information items. This emphasis of this paper is on how Bayesian networks are used.

  9. Bayesian networks modeling for thermal error of numerical control machine tools

    Institute of Scientific and Technical Information of China (English)

    Xin-hua YAO; Jian-zhong FU; Zi-chen CHEN

    2008-01-01

    The interaction between the heat source location,its intensity,thermal expansion coefficient,the machine system configuration and the running environment creates complex thermal behavior of a machine tool,and also makes thermal error prediction difficult.To address this issue,a novel prediction method for machine tool thermal error based on Bayesian networks (BNs) was presented.The method described causal relationships of factors inducing thermal deformation by graph theory and estimated the thermal error by Bayesian statistical techniques.Due to the effective combination of domain knowledge and sampled data,the BN method could adapt to the change of running state of machine,and obtain satisfactory prediction accuracy.Ex-periments on spindle thermal deformation were conducted to evaluate the modeling performance.Experimental results indicate that the BN method performs far better than the least squares(LS)analysis in terms of modeling estimation accuracy.

  10. Bayesian methods for estimating the reliability in complex hierarchical networks (interim report).

    Energy Technology Data Exchange (ETDEWEB)

    Marzouk, Youssef M.; Zurn, Rena M.; Boggs, Paul T.; Diegert, Kathleen V. (Sandia National Laboratories, Albuquerque, NM); Red-Horse, John Robert (Sandia National Laboratories, Albuquerque, NM); Pebay, Philippe Pierre

    2007-05-01

    Current work on the Integrated Stockpile Evaluation (ISE) project is evidence of Sandia's commitment to maintaining the integrity of the nuclear weapons stockpile. In this report, we undertake a key element in that process: development of an analytical framework for determining the reliability of the stockpile in a realistic environment of time-variance, inherent uncertainty, and sparse available information. This framework is probabilistic in nature and is founded on a novel combination of classical and computational Bayesian analysis, Bayesian networks, and polynomial chaos expansions. We note that, while the focus of the effort is stockpile-related, it is applicable to any reasonably-structured hierarchical system, including systems with feedback.

  11. Looking for Sustainable Urban Mobility through Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Giovanni Fusco

    2004-11-01

    Full Text Available There is no formalised theory of sustainable urban mobility systems. Observed patterns of urban mobility are often considered unsustainable. But we don’t know what a city with sustainable mobility should look like. It is nevertheless increasingly apparent that the urban mobility system plays an important role in the achievement of the city’s wider sustainability objectives.In this paper we explore the characteristics of sustainable urban mobility systems through the technique of Bayesian networks. At the frontier between multivariate statistics and artificial intelligence, Bayesian networks provide powerful models of causal knowledge in an uncertain context. Using data on urban structure, transportation offer, mobility demand, resource consumption and environmental externalities from seventy-five world cities, we developed a systemic model of the city-transportation-environment interaction in the form of a Bayesian network. The network could then be used to infer the features of the city with sustainable mobility.The Bayesian model indicates that the city with sustainable mobility is most probably a dense city with highly efficient transit and multimodal mobility. It produces high levels of accessibility without relying on a fast road network. The achievement of sustainability objectives for urban mobility is probably compatible with all socioeconomic contexts.By measuring the distance of world cities from the inferred sustainability profile, we finally derive a geography of sustainability for mobility systems. The cities closest to the sustainability profile are in Central Europe as well as in affluent countries of the Far East. Car-dependent American cities are the farthest from the desired sustainability profile.

  12. A full bayesian approach for boolean genetic network inference.

    Directory of Open Access Journals (Sweden)

    Shengtong Han

    Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

  13. Nuclear charge radii: Density functional theory meets Bayesian neural networks

    CERN Document Server

    Utama, Raditya; Piekarewicz, Jorge

    2016-01-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonst...

  14. [A medical image semantic modeling based on hierarchical Bayesian networks].

    Science.gov (United States)

    Lin, Chunyi; Ma, Lihong; Yin, Junxun; Chen, Jianyu

    2009-04-01

    A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.

  15. Predicting Software Suitability Using a Bayesian Belief Network

    Science.gov (United States)

    Beaver, Justin M.; Schiavone, Guy A.; Berrios, Joseph S.

    2005-01-01

    The ability to reliably predict the end quality of software under development presents a significant advantage for a development team. It provides an opportunity to address high risk components earlier in the development life cycle, when their impact is minimized. This research proposes a model that captures the evolution of the quality of a software product, and provides reliable forecasts of the end quality of the software being developed in terms of product suitability. Development team skill, software process maturity, and software problem complexity are hypothesized as driving factors of software product quality. The cause-effect relationships between these factors and the elements of software suitability are modeled using Bayesian Belief Networks, a machine learning method. This research presents a Bayesian Network for software quality, and the techniques used to quantify the factors that influence and represent software quality. The developed model is found to be effective in predicting the end product quality of small-scale software development efforts.

  16. Research on Bayesian Network Based User's Interest Model

    Institute of Scientific and Technical Information of China (English)

    ZHANG Weifeng; XU Baowen; CUI Zifeng; XU Lei

    2007-01-01

    It has very realistic significance for improving the quality of users' accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users' interest models and some basic questions of users' interest (representation, derivation and identification of users' interest), a Bayesian network based users' interest model is given. In this model, the users' interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users' interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user's interest, and has higher performance and good usability.

  17. Decision Support System for Maintenance Management Using Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The maintenance process has undergone several major developments that have led to proactive considerations and the transformation from the traditional "fail and fix" practice into the "predict and prevent" proactive maintenance methodology. The anticipation action, which characterizes this proactive maintenance strategy is mainly based on monitoring, diagnosis, prognosis and decision-making modules. Oil monitoring is a key component of a successful condition monitoring program. It can be used as a proactive tool to identify the wear modes of rubbing parts and diagnoses the faults in machinery. But diagnosis relying on oil analysis technology must deal with uncertain knowledge and fuzzy input data. Besides other methods, Bayesian Networks have been extensively applied to fault diagnosis with the advantages of uncertainty inference; however, in the area of oil monitoring, it is a new field. This paper presents an integrated Bayesian network based decision support for maintenance of diesel engines.

  18. Bayesian network models in brain functional connectivity analysis

    OpenAIRE

    Ide, Jaime S.; Zhang, Sheng; Chiang-shan R. Li

    2013-01-01

    Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. In computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and wh...

  19. Partial Order MCMC for Structure Discovery in Bayesian Networks

    CERN Document Server

    Niinimaki, Teppo; Koivisto, Mikko

    2012-01-01

    We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.

  20. Filtering in hybrid dynamic Bayesian networks (left)

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...

  1. Filtering in hybrid dynamic Bayesian networks

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    2004-01-01

    that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...

  2. Filtering in hybrid dynamic Bayesian networks (center)

    DEFF Research Database (Denmark)

    Andersen, Morten Nonboe; Andersen, Rasmus Ørum; Wheeler, Kevin

    that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the watertank simulation. Theory and implementation is based on the theory presented in (v.d. Merwe et al...

  3. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    Science.gov (United States)

    Goodrich, Michael S.

    2011-01-01

    Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

  4. Bayesian network models for error detection in radiotherapy plans

    Science.gov (United States)

    Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.

    2015-04-01

    The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

  5. Prediction of the insulin sensitivity index using Bayesian networks

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard; Dethlefsen, Claus

    The insulin sensitivity index () can be used in assessing the risk of developing type 2 diabetes. An intravenous study is used to determine using Bergmans minimal model. However, an intravenous study is time consuming and expensive and therefore not suitable for large scale epidemiological studies....... In this paper we learn the parameters and structure of several Bayesian networks relating measurements from an oral glucose tolerance test to the insulin sensitivity index determined from an intravenous study on the same individuals. The networks can then be used in prediction of from an oral glucose tolerance...

  6. Bayesian Inference of Reticulate Phylogenies under the Multispecies Network Coalescent.

    Science.gov (United States)

    Wen, Dingqiao; Yu, Yun; Nakhleh, Luay

    2016-05-01

    The multispecies coalescent (MSC) is a statistical framework that models how gene genealogies grow within the branches of a species tree. The field of computational phylogenetics has witnessed an explosion in the development of methods for species tree inference under MSC, owing mainly to the accumulating evidence of incomplete lineage sorting in phylogenomic analyses. However, the evolutionary history of a set of genomes, or species, could be reticulate due to the occurrence of evolutionary processes such as hybridization or horizontal gene transfer. We report on a novel method for Bayesian inference of genome and species phylogenies under the multispecies network coalescent (MSNC). This framework models gene evolution within the branches of a phylogenetic network, thus incorporating reticulate evolutionary processes, such as hybridization, in addition to incomplete lineage sorting. As phylogenetic networks with different numbers of reticulation events correspond to points of different dimensions in the space of models, we devise a reversible-jump Markov chain Monte Carlo (RJMCMC) technique for sampling the posterior distribution of phylogenetic networks under MSNC. We implemented the methods in the publicly available, open-source software package PhyloNet and studied their performance on simulated and biological data. The work extends the reach of Bayesian inference to phylogenetic networks and enables new evolutionary analyses that account for reticulation. PMID:27144273

  7. Modeling operational risks of the nuclear industry with Bayesian networks

    International Nuclear Information System (INIS)

    Basically, planning a new industrial plant requires information on the industrial management, regulations, site selection, definition of initial and planned capacity, and on the estimation of the potential demand. However, this is far from enough to assure the success of an industrial enterprise. Unexpected and extremely damaging events may occur that deviates from the original plan. The so-called operational risks are not only in the system, equipment, process or human (technical or managerial) failures. They are also in intentional events such as frauds and sabotage, or extreme events like terrorist attacks or radiological accidents and even on public reaction to perceived environmental or future generation impacts. For the nuclear industry, it is a challenge to identify and to assess the operational risks and their various sources. Early identification of operational risks can help in preparing contingency plans, to delay the decision to invest or to approve a project that can, at an extreme, affect the public perception of the nuclear energy. A major problem in modeling operational risk losses is the lack of internal data that are essential, for example, to apply the loss distribution approach. As an alternative, methods that consider qualitative and subjective information can be applied, for example, fuzzy logic, neural networks, system dynamic or Bayesian networks. An advantage of applying Bayesian networks to model operational risk is the possibility to include expert opinions and variables of interest, to structure the model via causal dependencies among these variables, and to specify subjective prior and conditional probabilities distributions at each step or network node. This paper suggests a classification of operational risks in industry and discusses the benefits and obstacles of the Bayesian networks approach to model those risks. (author)

  8. Mobile sensor network noise reduction and recalibration using a Bayesian network

    Science.gov (United States)

    Xiang, Y.; Tang, Y.; Zhu, W.

    2016-02-01

    People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

  9. Parameter estimation of general regression neural network using Bayesian approach

    Science.gov (United States)

    Choir, Achmad Syahrul; Prasetyo, Rindang Bangun; Ulama, Brodjol Sutijo Suprih; Iriawan, Nur; Fitriasari, Kartika; Dokhi, Mohammad

    2016-02-01

    General Regression Neural Network (GRNN) has been applied in a large number of forecasting/prediction problem. Generally, there are two types of GRNN: GRNN which is based on kernel density; and Mixture Based GRNN (MBGRNN) which is based on adaptive mixture model. The main problem on GRNN modeling lays on how its parameters were estimated. In this paper, we propose Bayesian approach and its computation using Markov Chain Monte Carlo (MCMC) algorithms for estimating the MBGRNN parameters. This method is applied in simulation study. In this study, its performances are measured by using MAPE, MAE and RMSE. The application of Bayesian method to estimate MBGRNN parameters using MCMC is straightforward but it needs much iteration to achieve convergence.

  10. Bayesian probabilistic network approach for managing earthquake risks of cities

    DEFF Research Database (Denmark)

    Bayraktarli, Yahya; Faber, Michael

    2011-01-01

    This paper considers the application of Bayesian probabilistic networks (BPNs) to large-scale risk based decision making in regard to earthquake risks. A recently developed risk management framework is outlined which utilises Bayesian probabilistic modelling, generic indicator based risk models...... and geographical information systems. The proposed framework comprises several modules: A module on the probabilistic description of potential future earthquake shaking intensity, a module on the probabilistic assessment of spatial variability of soil liquefaction, a module on damage assessment of buildings...... and a fourth module on the consequences of an earthquake. Each of these modules is integrated into a BPN. Special attention is given to aggregated risk, i.e. the risk contribution from assets at multiple locations in a city subjected to the same earthquake. The application of the methodology is illustrated...

  11. ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

    OpenAIRE

    Santosh Kumar Chaudhari; Murthy, Hema A.

    2011-01-01

    A Network Management System (NMS) plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to ...

  12. Bayesian Inference of Natural Rankings in Incomplete Competition Networks

    CERN Document Server

    Park, Juyong

    2013-01-01

    Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest -- essential in determining reward or penalty -- is almost always an ambiguous task due to the incomplete nature of competition networks. Here we introduce ``Natural Ranking," a desirably unambiguous ranking method applicable to a complete (full) competition network, and formulate an analytical model based on the Bayesian formula inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in solving issues in ranking by applying to a real-world competition network of economic and social importance.

  13. A Software Risk Analysis Model Using Bayesian Belief Network

    Institute of Scientific and Technical Information of China (English)

    Yong Hu; Juhua Chen; Mei Liu; Yang Yun; Junbiao Tang

    2006-01-01

    The uncertainty during the period of software project development often brings huge risks to contractors and clients. Ifwe can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project, we can reduce the risk.Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table. In this paper, we built up network structure by Delphi method for conditional probability table learning, and learn update probability table and nodes' confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately. This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.

  14. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes....... In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us...... to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models....

  15. Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.

    OpenAIRE

    Kahn, C. E.; Roberts, L. M.; K. Wang; Jenks, D.; Haddawy, P.

    1995-01-01

    Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional-probability data, such as sensitivity and specificity, were derived from peer-reviewed jour...

  16. Road network safety evaluation using Bayesian hierarchical joint model.

    Science.gov (United States)

    Wang, Jie; Huang, Helai

    2016-05-01

    Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.

  17. Bayesian Network Structure Learning Based On Rough Set and Mutual Information

    Directory of Open Access Journals (Sweden)

    Zuhong Feng

    2013-09-01

    Full Text Available Abstract In Bayesian network structure learning for incomplete data set, a common problem is too many attributes causing low efficiency and high computation complexity. In this paper, an algorithm of attribute reduction based on rough set is introduced. The algorithm can effectively reduce the dimension of attributes and quickly determine the network structure using mutual information for Bayesian network structure learning.

  18. A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776

    Science.gov (United States)

    West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.

    2010-01-01

    A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco…

  19. Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

    OpenAIRE

    Ildikó Ungvári; Gábor Hullám; Péter Antal; Petra Sz Kiszel; András Gézsi; Éva Hadadi; Viktor Virág; Gergely Hajós; András Millinghoffer; Adrienne Nagy; András Kiss; Semsei, Ágnes F.; Gergely Temesi; Béla Melegh; Péter Kisfali

    2012-01-01

    Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA). Th...

  20. Bayesian-network-based safety risk analysis in construction projects

    International Nuclear Information System (INIS)

    This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical

  1. Node Augmentation Technique in Bayesian Network Evidence Analysis and Marshaling

    Energy Technology Data Exchange (ETDEWEB)

    Keselman, Dmitry [Los Alamos National Laboratory; Tompkins, George H [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory

    2010-01-01

    Given a Bayesian network, sensitivity analysis is an important activity. This paper begins by describing a network augmentation technique which can simplifY the analysis. Next, we present two techniques which allow the user to determination the probability distribution of a hypothesis node under conditions of uncertain evidence; i.e. the state of an evidence node or nodes is described by a user specified probability distribution. Finally, we conclude with a discussion of three criteria for ranking evidence nodes based on their influence on a hypothesis node. All of these techniques have been used in conjunction with a commercial software package. A Bayesian network based on a directed acyclic graph (DAG) G is a graphical representation of a system of random variables that satisfies the following Markov property: any node (random variable) is independent of its non-descendants given the state of all its parents (Neapolitan, 2004). For simplicities sake, we consider only discrete variables with a finite number of states, though most of the conclusions may be generalized.

  2. Risk Analysis of New Product Development Using Bayesian Networks

    Directory of Open Access Journals (Sweden)

    MohammadRahim Ramezanian

    2012-06-01

    Full Text Available The process of presenting new product development (NPD to market is of great importance due to variability of competitive rules in the business world. The product development teams face a lot of pressures due to rapid growth of technology, increased risk-taking of world markets and increasing variations in the customers` needs. However, the process of NPD is always associated with high uncertainties and complexities. To be successful in completing NPD project, existing risks should be identified and assessed. On the other hand, the Bayesian networks as a strong approach of decision making modeling of uncertain situations has attracted many researchers in various areas. These networks provide a decision supporting system for problems with uncertainties or probable reasoning. In this paper, the available risk factors in product development have been first identified in an electric company and then, the Bayesian network has been utilized and their interrelationships have been modeled to evaluate the available risk in the process. To determine the primary and conditional probabilities of the nodes, the viewpoints of experts in this area have been applied. The available risks in this process have been divided to High (H, Medium (M and Low (L groups and analyzed by the Agena Risk software. The findings derived from software output indicate that the production of the desired product has relatively high risk. In addition, Predictive support and Diagnostic support have been performed on the model with two different scenarios..

  3. Risk Analysis of New Product Development Using Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Rahim Ramezanian

    2012-01-01

    Full Text Available The process of presenting new product development (NPD to market is of great importance due to variability of competitive rules in the business world. The product development teams face a lot of pressures due to rapid growth of technology, increased risk-taking of world markets and increasing variations in the customers` needs. However, the process of NPD is always associated with high uncertainties and complexities. To be successful in completing NPD project, existing risks should be identified and assessed. On the other hand, the Bayesian networks as a strong approach of decision making modeling of uncertain situations has attracted many researchers in various areas. These networks provide a decision supporting system for problems with uncertainties or probable reasoning. In this paper, the available risk factors in product development have been first identified in an electric company and then, the Bayesian network has been utilized and their interrelationships have been modeled to evaluate the available risk in the process. To determine the primary and conditional probabilities of the nodes, the viewpoints of experts in this area have been applied. The available risks in this process have been divided to High (H, Medium (M and Low (L groups and analyzed by the Agena Risk software. The findings derived from software output indicate that the production of the desired product has relatively high risk. In addition, Predictive support and Diagnostic support have been performed on the model with two different scenarios.

  4. Learning Continuous Time Bayesian Network Classifiers Using MapReduce

    Directory of Open Access Journals (Sweden)

    Simone Villa

    2014-12-01

    Full Text Available Parameter and structural learning on continuous time Bayesian network classifiers are challenging tasks when you are dealing with big data. This paper describes an efficient scalable parallel algorithm for parameter and structural learning in the case of complete data using the MapReduce framework. Two popular instances of classifiers are analyzed, namely the continuous time naive Bayes and the continuous time tree augmented naive Bayes. Details of the proposed algorithm are presented using Hadoop, an open-source implementation of a distributed file system and the MapReduce framework for distributed data processing. Performance evaluation of the designed algorithm shows a robust parallel scaling.

  5. A Bayesian Network View on Nested Effects Models

    Directory of Open Access Journals (Sweden)

    Fröhlich Holger

    2009-01-01

    Full Text Available Nested effects models (NEMs are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the /Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.

  6. Dynamic Bayesian Network Based Prognosis in Machining Processes

    Institute of Scientific and Technical Information of China (English)

    DONG Ming; YANG Zhi-bo

    2008-01-01

    Condition based maintenance (CBM) is becoming more and more popular in equipment main-tenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effectivediagnostics and prognostics. A dynamic Bayesian network (DBN) based prognosis method was investigated topredict the remaining useful life (RUL) for an equipment. First, a DBN based prognosis framework and specificsteps for building a DBN based prognosis model were presented. Then, the corresponding inference algorithmsfor DBN based prognosis were provided. Finally, a prognosis procedure based on particle filtering algorithmswas used to predict the RUL of drill-bits of a vertical drilling machine, which is commonly used in industrialprocess. Preliminary experimental results are promising.

  7. Probe Error Modeling Research Based on Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    Wu Huaiqiang; Xing Zilong; Zhang Jian; Yan Yan

    2015-01-01

    Probe calibration is carried out under specific conditions; most of the error caused by the change of speed parameter has not been corrected. In order to reduce the measuring error influence on measurement accuracy, this article analyzes the relationship between speed parameter and probe error, and use Bayesian network to establish the model of probe error. Model takes account of prior knowledge and sample data, with the updating of data, which can reflect the change of the errors of the probe and constantly revised modeling results.

  8. Efficient Bayesian Learning in Social Networks with Gaussian Estimators

    CERN Document Server

    Mossel, Elchanan

    2010-01-01

    We propose a simple and efficient Bayesian model of iterative learning on social networks. This model is efficient in two senses: the process both results in an optimal belief, and can be carried out with modest computational resources for large networks. This result extends Condorcet's Jury Theorem to general social networks, while preserving rationality and computational feasibility. The model consists of a group of agents who belong to a social network, so that a pair of agents can observe each other's actions only if they are neighbors. We assume that the network is connected and that the agents have full knowledge of the structure of the network. The agents try to estimate some state of the world S (say, the price of oil a year from today). Each agent has a private measurement of S. This is modeled, for agent v, by a number S_v picked from a Gaussian distribution with mean S and standard deviation one. Accordingly, agent v's prior belief regarding S is a normal distribution with mean S_v and standard dev...

  9. Applying Hierarchical Bayesian Neural Network in Failure Time Prediction

    Directory of Open Access Journals (Sweden)

    Ling-Jing Kao

    2012-01-01

    Full Text Available With the rapid technology development and improvement, the product failure time prediction becomes an even harder task because only few failures in the product life tests are recorded. The classical statistical model relies on the asymptotic theory and cannot guarantee that the estimator has the finite sample property. To solve this problem, we apply the hierarchical Bayesian neural network (HBNN approach to predict the failure time and utilize the Gibbs sampler of Markov chain Monte Carlo (MCMC to estimate model parameters. In this proposed method, the hierarchical structure is specified to study the heterogeneity among products. Engineers can use the heterogeneity estimates to identify the causes of the quality differences and further enhance the product quality. In order to demonstrate the effectiveness of the proposed hierarchical Bayesian neural network model, the prediction performance of the proposed model is evaluated using multiple performance measurement criteria. Sensitivity analysis of the proposed model is also conducted using different number of hidden nodes and training sample sizes. The result shows that HBNN can provide not only the predictive distribution but also the heterogeneous parameter estimates for each path.

  10. Clustering and Bayesian network for image of faces classification

    CERN Document Server

    Jayech, Khlifia

    2012-01-01

    In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors. Then, we use K-means to cluster the low-level features including color and texture information to build a vector of labels for each image. Finally, we apply five variants of Bayesian networks classifiers (Na\\"ive Bayes, Global Tree Augmented Na\\"ive Bayes (GTAN), Global Forest Augmented Na\\"ive Bayes (GFAN), Tree Augmented Na\\"ive Bayes for each class (TAN), and Forest Augmented Na\\"ive Bayes for each class (FAN) to classify the image of faces using the vector of labels. ...

  11. Application of Bayesian Networks to hindcast barrier island morphodynamics

    Science.gov (United States)

    Wilson, Kathleen E.; Adams, Peter N.; Hapke, Cheryl J.; Lentz, Erika E.; Brenner, Owen T.

    2015-01-01

    Prediction of coastal vulnerability is of increasing concern to policy makers, coastal managers and other stakeholders. Coastal regions and barrier islands along the Atlantic and Gulf coasts are subject to frequent, large storms, whose waves and storm surge can dramatically alter beach morphology, threaten infrastructure, and impact local economies. Given that precise forecasts of regional hazards are challenging, because of the complex interactions between processes on many scales, a range of probable geomorphic change in response to storm conditions is often more helpful than deterministic predictions. Site-specific probabilistic models of coastal change are reliable because they are formulated with observations so that local factors, of potentially high influence, are inherent in the model. The development and use of predictive tools such as Bayesian Networks in response to future storms has the potential to better inform management decisions and hazard preparation in coastal communities. We present several Bayesian Networks designed to hindcast distinct morphologic changes attributable to the Nor'Ida storm of 2009, at Fire Island, New York. Model predictions are informed with historical system behavior, initial morphologic conditions, and a parameterized treatment of wave climate.

  12. An Exploratory Study Examining the Feasibility of Using Bayesian Networks to Predict Circuit Analysis Understanding

    Science.gov (United States)

    Chung, Gregory K. W. K.; Dionne, Gary B.; Kaiser, William J.

    2006-01-01

    Our research question was whether we could develop a feasible technique, using Bayesian networks, to diagnose gaps in student knowledge. Thirty-four college-age participants completed tasks designed to measure conceptual knowledge, procedural knowledge, and problem-solving skills related to circuit analysis. A Bayesian network was used to model…

  13. Direct message passing for hybrid Bayesian networks and performance analysis

    Science.gov (United States)

    Sun, Wei; Chang, K. C.

    2010-04-01

    Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous variables, has been an important research topic over the recent years. This is not only because a number of efficient inference algorithms have been developed and used maturely for simple types of networks such as pure discrete model, but also for the practical needs that continuous variables are inevitable in modeling complex systems. Pearl's message passing algorithm provides a simple framework to compute posterior distribution by propagating messages between nodes and can provides exact answer for polytree models with pure discrete or continuous variables. In addition, applying Pearl's message passing to network with loops usually converges and results in good approximation. However, for hybrid model, there is a need of a general message passing algorithm between different types of variables. In this paper, we develop a method called Direct Message Passing (DMP) for exchanging messages between discrete and continuous variables. Based on Pearl's algorithm, we derive formulae to compute messages for variables in various dependence relationships encoded in conditional probability distributions. Mixture of Gaussian is used to represent continuous messages, with the number of mixture components up to the size of the joint state space of all discrete parents. For polytree Conditional Linear Gaussian (CLG) Bayesian network, DMP has the same computational requirements and can provide exact solution as the one obtained by the Junction Tree (JT) algorithm. However, while JT can only work for the CLG model, DMP can be applied for general nonlinear, non-Gaussian hybrid model to produce approximate solution using unscented transformation and loopy propagation. Furthermore, we can scale the algorithm by restricting the number of mixture components in the messages. Empirically, we found that the approximation errors are relatively small especially for nodes that are far away from

  14. Learning Bayesian networks from big meteorological spatial datasets. An alternative to complex network analysis

    Science.gov (United States)

    Gutiérrez, Jose Manuel; San Martín, Daniel; Herrera, Sixto; Santiago Cofiño, Antonio

    2016-04-01

    The growing availability of spatial datasets (observations, reanalysis, and regional and global climate models) demands efficient multivariate spatial modeling techniques for many problems of interest (e.g. teleconnection analysis, multi-site downscaling, etc.). Complex networks have been recently applied in this context using graphs built from pairwise correlations between the different stations (or grid boxes) forming the dataset. However, this analysis does not take into account the full dependence structure underlying the data, gien by all possible marginal and conditional dependencies among the stations, and does not allow a probabilistic analysis of the dataset. In this talk we introduce Bayesian networks as an alternative multivariate analysis and modeling data-driven technique which allows building a joint probability distribution of the stations including all relevant dependencies in the dataset. Bayesian networks is a sound machine learning technique using a graph to 1) encode the main dependencies among the variables and 2) to obtain a factorization of the joint probability distribution of the stations given by a reduced number of parameters. For a particular problem, the resulting graph provides a qualitative analysis of the spatial relationships in the dataset (alternative to complex network analysis), and the resulting model allows for a probabilistic analysis of the dataset. Bayesian networks have been widely applied in many fields, but their use in climate problems is hampered by the large number of variables (stations) involved in this field, since the complexity of the existing algorithms to learn from data the graphical structure grows nonlinearly with the number of variables. In this contribution we present a modified local learning algorithm for Bayesian networks adapted to this problem, which allows inferring the graphical structure for thousands of stations (from observations) and/or gridboxes (from model simulations) thus providing new

  15. Bayesian Diagnostic Network: A Powerful Model for Representation and Reasoning of Engineering Diagnostic Knowledge

    Institute of Scientific and Technical Information of China (English)

    HU Zhao-yong

    2005-01-01

    Engineering diagnosis is essential to the operation of industrial equipment. The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesian network is a powerful tool for it. This paper utilizes the Bayesian network to represent and reason diagnostic knowledge, named Bayesian diagnostic network. It provides a three-layer topologic structure based on operating conditions, possible faults and corresponding symptoms. The paper also discusses an approximate stochastic sampling algorithm. Then a practical Bayesian network for gas turbine diagnosis is constructed on a platform developed under a Visual C++ environment. It shows that the Bayesian network is a powerful model for representation and reasoning of diagnostic knowledge. The three-layer structure and the approximate algorithm are effective also.

  16. Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network

    Science.gov (United States)

    de Nijs, Patrick J.; Berry, Nicholas J.; Wells, Geoff J.; Reay, Dave S.

    2014-10-01

    The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

  17. Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2013-01-01

    To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying assumption is that the parameters associated with time series segments delimited by multiple changepoints are a priori inde

  18. Construction and Experiment of Hierarchical Bayesian Network in Data Assimilation

    International Nuclear Information System (INIS)

    A Hierarchical Bayesian Network Algorithm (HBN) is developed for data assimilation and tested with an instance of soil moisture assimilation from hydrological model and ground observations. In this work, data assimilation separates into data level, process level and parameter level, and conditional probability models are defined for each level. The data model mainly deals with the scale differences between multiple data, while the process model is designed to take account of non-stationary process. Soil moisture from Soil Moisture Experiment in 2003 and Variable Infiltration Capacity Model is sequentially assimilated with HBN. The result shows that the assimilation with HBN provides spatial and temporal distribution information of soil moisture and the assimilation result agrees well with the ground observations

  19. Designing and testing inflationary models with Bayesian networks

    CERN Document Server

    Price, Layne C; Frazer, Jonathan; Easther, Richard

    2015-01-01

    Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use $N_f$--quadratic inflation as an illustrative example, finding that the number of $e$-folds $N_*$ between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.

  20. Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment

    Directory of Open Access Journals (Sweden)

    Gregory Koshmak

    2014-05-01

    Full Text Available Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.

  1. NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Kontkanen Petri

    2007-01-01

    Full Text Available Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks.

  2. Reduced complexity turbo equalization using a dynamic Bayesian network

    Science.gov (United States)

    Myburgh, Hermanus C.; Olivier, Jan C.; van Zyl, Augustinus J.

    2012-12-01

    It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.

  3. Risk analysis of dust explosion scenarios using Bayesian networks.

    Science.gov (United States)

    Yuan, Zhi; Khakzad, Nima; Khan, Faisal; Amyotte, Paul

    2015-02-01

    In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow-tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures. PMID:25264172

  4. Designing and testing inflationary models with Bayesian networks

    Energy Technology Data Exchange (ETDEWEB)

    Price, Layne C. [Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Physics; Auckland Univ. (New Zealand). Dept. of Physics; Peiris, Hiranya V. [Univ. College London (United Kingdom). Dept. of Physics and Astronomy; Frazer, Jonathan [DESY Hamburg (Germany). Theory Group; Univ. of the Basque Country, Bilbao (Spain). Dept. of Theoretical Physics; Basque Foundation for Science, Bilbao (Spain). IKERBASQUE; Easther, Richard [Auckland Univ. (New Zealand). Dept. of Physics

    2015-11-15

    Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use N{sub f}-quadratic inflation as an illustrative example, finding that the number of e-folds N{sub *} between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.

  5. Aggregated Residential Load Modeling Using Dynamic Bayesian Networks

    Energy Technology Data Exchange (ETDEWEB)

    Vlachopoulou, Maria; Chin, George; Fuller, Jason C.; Lu, Shuai

    2014-09-28

    Abstract—It is already obvious that the future power grid will have to address higher demand for power and energy, and to incorporate renewable resources of different energy generation patterns. Demand response (DR) schemes could successfully be used to manage and balance power supply and demand under operating conditions of the future power grid. To achieve that, more advanced tools for DR management of operations and planning are necessary that can estimate the available capacity from DR resources. In this research, a Dynamic Bayesian Network (DBN) is derived, trained, and tested that can model aggregated load of Heating, Ventilation, and Air Conditioning (HVAC) systems. DBNs can provide flexible and powerful tools for both operations and planing, due to their unique analytical capabilities. The DBN model accuracy and flexibility of use is demonstrated by testing the model under different operational scenarios.

  6. Safety Analysis of Liquid Rocket Engine Using Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    WANG Hua-wei; YAN Zhi-qiang

    2007-01-01

    Safety analysis for liquid rocket engine has a great meaning for shortening development cycle, saving development expenditure and reducing development risk. The relationship between the structure and component of liquid rocket engine is much more complex, furthermore test data are absent in development phase. Thereby, the uncertainties exist in safety analysis for liquid rocket engine. A safety analysis model integrated with FMEA(failure mode and effect analysis)based on Bayesian networks (BN) is brought forward for liquid rocket engine, which can combine qualitative analysis with quantitative decision. The method has the advantages of fusing multi-information, saving sample amount and having high veracity. An example shows that the method is efficient.

  7. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

    Science.gov (United States)

    Meng, Jia; Zhang, Jianqiu(Michelle); Qi, Yuan(Alan); Chen, Yidong; Huang, Yufei

    2010-12-01

    The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ([InlineEquation not available: see fulltext.]) status and Estrogen Receptor negative ([InlineEquation not available: see fulltext.]) status, respectively.

  8. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

    Directory of Open Access Journals (Sweden)

    Qi Yuan(Alan

    2010-01-01

    Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

  9. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  10. Exact Structure Discovery in Bayesian Networks with Less Space

    CERN Document Server

    Parviainen, Pekka

    2012-01-01

    The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2nnO(1). The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space-time tradeoffs for finding an optimal network structure. When little space is available, we apply the Gurevich-Shelah recurrence-originally proposed for the Hamiltonian path problem-and obtain time 22n-snO(1) in space 2snO(1) for any s = n/2, n/4, n/8, . . .; we assume the indegree of each node is bounded by a constant. For the more practical setting with moderate amounts of space, we present a novel scheme. It yields running time 2n(3/2)pnO(1) in space 2n(3/4)pnO(1) for any p = 0, 1, . . ., n/2; these bounds hold as long as the indegrees are at most 0.238n. Furthermore, the latter scheme allows easy and efficient parallelization beyond previous algorithms. We also explore empirically the potential of the presented techniques.

  11. E-commerce System Security Assessment based on Bayesian Network Algorithm Research

    OpenAIRE

    Ting Li; Xin Li

    2013-01-01

    Evaluation of e-commerce network security is based on assessment method Bayesian networks, and it first defines the vulnerability status of e-commerce system evaluation index and the vulnerability of the state model of e-commerce systems, and after the principle of the Bayesian network reliability of e-commerce system and the criticality of the vulnerabilities were analyzed, experiments show that the change method is a good evaluation of the security of e-commerce systems.

  12. Construction of gene regulatory networks using biclustering and bayesian networks

    OpenAIRE

    Alakwaa Fadhl M; Solouma Nahed H; Kadah Yasser M

    2011-01-01

    Abstract Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA mi...

  13. A Dynamic Bayesian Network Approach to Location Prediction in Ubiquitous Computing Environments

    Science.gov (United States)

    Lee, Sunyoung; Lee, Kun Chang; Cho, Heeryon

    The ability to predict the future contexts of users significantly improves service quality and user satisfaction in ubiquitous computing environments. Location prediction is particularly useful because ubiquitous computing environments can dynamically adapt their behaviors according to a user's future location. In this paper, we present an inductive approach to recognizing a user's location by establishing a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data collected from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power.

  14. Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

    Science.gov (United States)

    Levy, Roy

    2014-01-01

    Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

  15. Construction of gene regulatory networks using biclustering and bayesian networks

    Directory of Open Access Journals (Sweden)

    Alakwaa Fadhl M

    2011-10-01

    Full Text Available Abstract Background Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling. Results In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method. Conclusions Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.

  16. Heterogeneous LTE-Advanced Network Expansion for 1000x Capacity

    DEFF Research Database (Denmark)

    Hu, Liang; Sanchez, Maria Laura Luque; Maternia, Michal;

    2013-01-01

    this paper studies LTE (Long-Term Evolution)-Advanced heterogeneous network expansion in a dense urban environment for a 1000 times capacity increase and a 10 times increase in minimum user data rate requirements. The radio network capacity enhancement via outdoor and indoor small cell densificat......this paper studies LTE (Long-Term Evolution)-Advanced heterogeneous network expansion in a dense urban environment for a 1000 times capacity increase and a 10 times increase in minimum user data rate requirements. The radio network capacity enhancement via outdoor and indoor small cell...

  17. Making Supply Chains Resilient to Floods Using a Bayesian Network

    Science.gov (United States)

    Haraguchi, M.

    2015-12-01

    Natural hazards distress the global economy by disrupting the interconnected supply chain networks. Manufacturing companies have created cost-efficient supply chains by reducing inventories, streamlining logistics and limiting the number of suppliers. As a result, today's supply chains are profoundly susceptible to systemic risks. In Thailand, for example, the GDP growth rate declined by 76 % in 2011 due to prolonged flooding. Thailand incurred economic damage including the loss of USD 46.5 billion, approximately 70% of which was caused by major supply chain disruptions in the manufacturing sector. Similar problems occurred after the Great East Japan Earthquake and Tsunami in 2011, the Mississippi River floods and droughts during 2011 - 2013, and Hurricane Sandy in 2012. This study proposes a methodology for modeling supply chain disruptions using a Bayesian network analysis (BNA) to estimate expected values of countermeasures of floods, such as inventory management, supplier management and hard infrastructure management. We first performed a spatio-temporal correlation analysis between floods and extreme precipitation data for the last 100 years at a global scale. Then we used a BNA to create synthetic networks that include variables associated with the magnitude and duration of floods, major components of supply chains and market demands. We also included decision variables of countermeasures that would mitigate potential losses caused by supply chain disruptions. Finally, we conducted a cost-benefit analysis by estimating the expected values of these potential countermeasures while conducting a sensitivity analysis. The methodology was applied to supply chain disruptions caused by the 2011 Thailand floods. Our study demonstrates desirable typical data requirements for the analysis, such as anonymized supplier network data (i.e. critical dependencies, vulnerability information of suppliers) and sourcing data(i.e. locations of suppliers, and production rates and

  18. Bayesian phylogeny of sucrose transporters: Ancient origins, differential expansion and convergent evolution in monocots and dicots

    Directory of Open Access Journals (Sweden)

    Duo ePeng

    2014-11-01

    Full Text Available Sucrose transporters (SUTs are essential for the export and efficient movement of sucrose from source leaves to sink organs in plants. The angiosperm SUT family was previously classified into three or four distinct groups, Types I, II (subgroup IIB and III, with dicot-specific Type I and monocot-specific Type IIB functioning in phloem loading. To shed light on the underlying drivers of SUT evolution, Bayesian phylogenetic inference was undertaken using 41 sequenced plant genomes, including seven basal lineages at key evolutionary junctures. Our analysis supports four phylogenetically and structurally distinct SUT subfamilies, originating from two ancient groups (AG1 and AG2 that diverged early during terrestrial colonization. In both AG1 and AG2, multiple intron acquisition events in the progenitor vascular plant established the gene structures of modern SUTs. Tonoplastic Type III and plasmalemmal Type II represent evolutionarily conserved descendants of AG1 and AG2, respectively. Type I and Type IIB were previously thought to evolve after the dicot-monocot split. We show, however, that divergence of Type I from Type III SUT predated basal angiosperms, likely associated with evolution of vascular cambium and phloem transport. Type I SUT was subsequently lost in monocots along with vascular cambium, and independent evolution of Type IIB coincided with modified monocot vasculature. Both Type I and Type IIB underwent lineage-specific expansion. In multiple unrelated taxa, the newly-derived SUTs exhibit biased expression in reproductive tissues, suggesting a functional link between phloem loading and reproductive fitness. Convergent evolution of Type I and Type IIB for SUT function in phloem loading and reproductive organs supports the idea that differential vascular development in dicots and monocots is a strong driver for SUT family evolution in angiosperms.

  19. A Bayesian belief network of threat anticipation and terrorist motivations

    Science.gov (United States)

    Olama, Mohammed M.; Allgood, Glenn O.; Davenport, Kristen M.; Schryver, Jack C.

    2010-04-01

    Recent events highlight the need for efficient tools for anticipating the threat posed by terrorists, whether individual or groups. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security measures, planning for future events, halting an event in process, and ultimately mitigating and managing the consequences of an event. To analyze such components, one must understand various aspects of threat elements like physical assets and their economic and social impacts. To this aim, we developed a three-layer Bayesian belief network (BBN) model that takes into consideration the relative threat of an attack against a particular asset (physical layer) as well as the individual psychology and motivations that would induce a person to either act alone or join a terrorist group and commit terrorist acts (social and economic layers). After researching the many possible motivations to become a terrorist, the main factors are compiled and sorted into categories such as initial and personal indicators, exclusion factors, and predictive behaviors. Assessing such threats requires combining information from disparate data sources most of which involve uncertainties. BBN combines these data in a coherent, analytically defensible, and understandable manner. The developed BBN model takes into consideration the likelihood and consequence of a threat in order to draw inferences about the risk of a terrorist attack so that mitigation efforts can be optimally deployed. The model is constructed using a network engineering process that treats the probability distributions of all the BBN nodes within the broader context of the system development process.

  20. Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management

    Science.gov (United States)

    Bashar, Abul; Parr, Gerard; McClean, Sally; Scotney, Bryan; Nauck, Detlef

    The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application.

  1. Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks

    OpenAIRE

    Grzegorczyk, M.; Husmeier, D.

    2009-01-01

    Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be learned. However, we show that the widely applied BGe score is susceptible to learning spurious feedback loops, which are a consequence of non-linear regulation and autocorrelation in the data. We propose a non-linear generalisation of the BGe m...

  2. A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Sho Fukuda

    2014-12-01

    Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

  3. Bayesian network as a modelling tool for risk management in agriculture

    DEFF Research Database (Denmark)

    Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens

    The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools....... In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models....... We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions...

  4. Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

    DEFF Research Database (Denmark)

    Paluszewski, Martin; Hamelryck, Thomas Wim

    2010-01-01

    Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations...

  5. A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

    OpenAIRE

    Ji Yae Shin; Muhammad Ajmal; Jiyoung Yoo; Tae-Woong Kim

    2016-01-01

    Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designe...

  6. Predicting academic major of students using bayesian networks to the case of iran

    OpenAIRE

    Asadianfam, Shiva; Shamsi, Mahboubeh; Asadianfam, Sima

    2015-01-01

    In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school students using Bayesian networks. The effective factors have been used in academic major selection for the first time as an effective indicator of Bayesian networks. Evaluation of...

  7. Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity

    OpenAIRE

    Cussens, James; Järvisalo, Matti; Korhonen, Janne H.; Bartlett, Mark

    2016-01-01

    The challenging task of learning structures of probabilistic graphical models is an important problem within modern AI research. Recent years have witnessed several major algorithmic advances in structure learning for Bayesian networks---arguably the most central class of graphical models---especially in what is known as the score-based setting. A successful generic approach to optimal Bayesian network structure learning (BNSL), based on integer programming (IP), is implemented in the GOBNILP...

  8. Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach

    OpenAIRE

    Andrew Sanford; Imad Moosa

    2015-01-01

    This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited ava...

  9. Flexible Transport Network Expansion via Open WDM Interfaces

    DEFF Research Database (Denmark)

    Fagertun, Anna Manolova; Skjoldstrup, Bjarke

    2013-01-01

    This paper presents a successful test-bed implementation of a multi-vendor transport network interconnection via open WDM interfaces. The concept of applying Alien Wavelengths (AWs) for network expansion was successfully illustrated via deployment of multi-domain/multi-vendor end-to-end OTN...

  10. Bayesian Networks as a Decision Tool for O&M of Offshore Wind Turbines

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2010-01-01

    Costs to operation and maintenance (O&M) of offshore wind turbines are large. This paper presents how influence diagrams can be used to assist in rational decision making for O&M. An influence diagram is a graphical representation of a decision tree based on Bayesian Networks. Bayesian Networks...... offer efficient Bayesian updating of a damage model when imperfect information from inspections/monitoring is available. The extension to an influence diagram offers the calculation of expected utilities for decision alternatives, and can be used to find the optimal strategy among different alternatives...

  11. Overview of methods of reverse engineering of gene regulatory networks: Boolean and Bayesian networks

    Directory of Open Access Journals (Sweden)

    Frolova A. O.

    2012-06-01

    Full Text Available Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Boolean and Bayesian networks. The mathematical description of each method is given, as well as several algorithmic approaches to modeling gene networks using these methods; the complexity of algorithms and the problems that arise during its implementation are also noted.

  12. Criminal and Civil Identification with DNA Databases Using Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Marina Andrade

    2009-10-01

    Full Text Available Forensic identification problems are examples in which the study of DNA profilesis a common approach. Here we present some problems and develop theirtreatment putting the focus in the use of Object-Oriented Bayesian Networks -OOBN. The use of DNA databases, which began in 1995 in England, hascreated new challenges about its use. In Portugal the legislation for theconstruction of a genetic database was defined in 2008. With this it is importantto determine how to use it in an appropriate way.For a crime that has been committed, forensic laboratories identify geneticcharacteristics in order to connect one or more individuals to it. Apart thelaboratories results it is a matter of great importance to quantify the informationobtained, i.e., to know how to evaluate and interpret the results obtainedproviding support to the judicial system. Other forensic identification problemsare body identification; whether the identification of a body (or more than onefound, together with the information of missing persons belonging to one or moreknown families, for which there may be information of family members whoclaimed the disappearance. In this work we intend to discuss how to use thedatabase; the hypotheses of interest and the database use to determine thelikelihood ratios, i.e., how to evaluate the evidence for different situations.

  13. Evidence for single top quark production using Bayesian neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kau, Daekwang [Florida State Univ., Tallahassee, FL (United States)

    2007-01-01

    We present results of a search for single top quark production in p$\\bar{p}$ collisions using a dataset of approximately 1 fb-1 collected with the D0 detector. This analysis considers the muon+jets and electron+jets final states and makes use of Bayesian neural networks to separate the expected signals from backgrounds. The observed excess is associated with a p-value of 0.081%, assuming the background-only hypothesis, which corresponds to an excess over background of 3.2 standard deviations for a Gaussian density. The p-value computed using the SM signal cross section of 2.9 pb is 1.6%, corresponding to an expected significance of 2.2 standard deviations. Assuming the observed excess is due to single top production, we measure a single top quark production cross section of σ(p$\\bar{p}$ → tb + X, tqb + X) = 4.4 ± 1.5 pb.

  14. Bayesian network model of crowd emotion and negative behavior

    Science.gov (United States)

    Ramli, Nurulhuda; Ghani, Noraida Abdul; Hatta, Zulkarnain Ahmad; Hashim, Intan Hashimah Mohd; Sulong, Jasni; Mahudin, Nor Diana Mohd; Rahman, Shukran Abd; Saad, Zarina Mat

    2014-12-01

    The effects of overcrowding have become a major concern for event organizers. One aspect of this concern has been the idea that overcrowding can enhance the occurrence of serious incidents during events. As one of the largest Muslim religious gathering attended by pilgrims from all over the world, Hajj has become extremely overcrowded with many incidents being reported. The purpose of this study is to analyze the nature of human emotion and negative behavior resulting from overcrowding during Hajj events from data gathered in Malaysian Hajj Experience Survey in 2013. The sample comprised of 147 Malaysian pilgrims (70 males and 77 females). Utilizing a probabilistic model called Bayesian network, this paper models the dependence structure between different emotions and negative behaviors of pilgrims in the crowd. The model included the following variables of emotion: negative, negative comfortable, positive, positive comfortable and positive spiritual and variables of negative behaviors; aggressive and hazardous acts. The study demonstrated that emotions of negative, negative comfortable, positive spiritual and positive emotion have a direct influence on aggressive behavior whereas emotion of negative comfortable, positive spiritual and positive have a direct influence on hazardous acts behavior. The sensitivity analysis showed that a low level of negative and negative comfortable emotions leads to a lower level of aggressive and hazardous behavior. Findings of the study can be further improved to identify the exact cause and risk factors of crowd-related incidents in preventing crowd disasters during the mass gathering events.

  15. CEO emotional bias and investment decision, Bayesian network method

    Directory of Open Access Journals (Sweden)

    Jarboui Anis

    2012-08-01

    Full Text Available This research examines the determinants of firms’ investment introducing a behavioral perspective that has received little attention in corporate finance literature. The following central hypothesis emerges from a set of recently developed theories: Investment decisions are influenced not only by their fundamentals but also depend on some other factors. One factor is the biasness of any CEO to their investment, biasness depends on the cognition and emotions, because some leaders use them as heuristic for the investment decision instead of fundamentals. This paper shows how CEO emotional bias (optimism, loss aversion and overconfidence affects the investment decisions. The proposed model of this paper uses Bayesian Network Method to examine this relationship. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 100 Tunisian executives. Our results have revealed that the behavioral analysis of investment decision implies leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its investment choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.

  16. Classification of Maize and Weeds by Bayesian Networks

    Science.gov (United States)

    Chapron, Michel; Oprea, Alina; Sultana, Bogdan; Assemat, Louis

    2007-11-01

    Precision Agriculture is concerned with all sorts of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e. fertilization, water) or for the agrochemicals use for pest control (i.e. herbicide). Moreover, this low efficacy means high environmental cost (pollution) and reduced economic return for the farmer. Weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, an automatic recognition method of vegetal species is described. First, the pixels of soil and vegetation are classified in two classes, then the vegetation part of the input image is segmented from the distance image by using the watershed method and finally the leaves of the vegetation are partitioned in two parts maize and weeds thanks to the two Bayesian networks.

  17. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming;

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight ...

  18. Bayesian Belief Networks Approach for Modeling Irrigation Behavior

    Science.gov (United States)

    Andriyas, S.; McKee, M.

    2012-12-01

    Canal operators need information to manage water deliveries to irrigators. Short-term irrigation demand forecasts can potentially valuable information for a canal operator who must manage an on-demand system. Such forecasts could be generated by using information about the decision-making processes of irrigators. Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to learn irrigation decision-making behavior of farmers and utilizes the resulting model to make forecasts of future irrigation decisions based on factor interaction and posterior probabilities. Models for studying irrigation behavior have been rarely explored in the past. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops of the location. The model has been tested with a portion of the data to affirm the model predictive capabilities. Irrigation rules were deduced in the process of learning and verified in the testing phase. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, which indicates the level of water available to the plant in the soil profile, was found to be one of the most significant likely driving forces for irrigation. Irrigations appeared to be triggered by a farmer's perception of soil stress, or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities of 94.4% for alfalfa. With additional factors like weekend and irrigating when a neighbor irrigates, alfalfa irrigation

  19. Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

    Directory of Open Access Journals (Sweden)

    Yunkai Wu

    2015-01-01

    component-level faults accurately for a high-speed railway traction system, a fault prognosis approach via Bayesian network and bond graph modeling techniques is proposed. The inherent structure of a railway traction system is represented by bond graph model, based on which a multilayer Bayesian network is developed for fault propagation analysis and fault prediction. For complete and incomplete data sets, two different parameter learning algorithms such as Bayesian estimation and expectation maximization (EM algorithm are adopted to determine the conditional probability table of the Bayesian network. The proposed prognosis approach using Pearl’s polytree propagation algorithm for joint probability reasoning can predict the failure probabilities of leaf nodes based on the current status of root nodes. Verification results in a high-speed railway traction simulation system can demonstrate the effectiveness of the proposed approach.

  20. Global Optimization for Transport Network Expansion and Signal Setting

    Directory of Open Access Journals (Sweden)

    Haoxiang Liu

    2015-01-01

    Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.

  1. Multi-year expansion planning of large transmission networks

    Energy Technology Data Exchange (ETDEWEB)

    Binato, S.; Oliveira, G.C. [Centro de Pesquisas de Energia Eletrica (CEPEL), Rio de Janeiro, RJ (Brazil)

    1994-12-31

    This paper describes a model for multi-year transmission network expansion to be used in long-term system planning. The network is represented by a linearized (DC) power flow and, for each year, operation costs are evaluated by a linear programming (LP) based algorithm that provides sensitivity indices for circuit reinforcements. A Backward/Forward approaches is proposed to devise an expansion plan over the study period. A case study with the southeastern Brazilian system is presented and discussed. (author) 18 refs., 5 figs., 1 tab.

  2. Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2010-01-01

    such that the preventive maintenance effort is balanced against the costs to corrective maintenance including indirect costs to reduced production. The basis for the optimization is the risk based Bayesian decision theory. The method is demonstrated through an application example....

  3. Distributed Diagnosis in Uncertain Environments Using Dynamic Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system...

  4. Utilization of extended bayesian networks in decision making under uncertainty

    Energy Technology Data Exchange (ETDEWEB)

    Van Eeckhout, Edward M [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory; Gibson, William L [Los Alamos National Laboratory

    2009-01-01

    Bayesian network tool (called IKE for Integrated Knowledge Engine) has been developed to assess the probability of undesirable events. The tool allows indications and observables from sensors and/or intelligence to feed directly into hypotheses of interest, thus allowing one to quantify the probability and uncertainty of these events resulting from very disparate evidence. For example, the probability that a facility is processing nuclear fuel or assembling a weapon can be assessed by examining the processes required, establishing the observables that should be present, then assembling information from intelligence, sensors and other information sources related to the observables. IKE also has the capability to determine tasking plans, that is, prioritize which observable should be collected next to most quickly ascertain the 'true' state and drive the probability toward 'zero' or 'one.' This optimization capability is called 'evidence marshaling.' One example to be discussed is a denied facility monitoring situation; there is concern that certain process(es) are being executed at the site (due to some intelligence or other data). We will show how additional pieces of evidence will then ascertain with some degree of certainty the likelihood of this process(es) as each piece of evidence is obtained. This example shows how both intelligence and sensor data can be incorporated into the analysis. A second example involves real-time perimeter security. For this demonstration we used seismic, acoustic, and optical sensors linked back to IKE. We show how these sensors identified and assessed the likelihood of 'intruder' versus friendly vehicles.

  5. Evaluation of a partial genome screening of two asthma susceptibility regions using bayesian network based bayesian multilevel analysis of relevance.

    Directory of Open Access Journals (Sweden)

    Ildikó Ungvári

    Full Text Available Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls. The results were evaluated with traditional frequentist methods and we applied a new statistical method, called bayesian network based bayesian multilevel analysis of relevance (BN-BMLA. This method uses bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated.With frequentist methods one SNP (rs3751464 in the FRMD6 gene provided evidence for an association with asthma (OR = 1.43(1.2-1.8; p = 3×10(-4. The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics.In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance.

  6. Bayesian networks applied to process diagnostics. Applications in energy industry

    Energy Technology Data Exchange (ETDEWEB)

    Widarsson, Bjoern (ed.); Karlsson, Christer; Dahlquist, Erik [Maelardalen Univ., Vaesteraas (Sweden); Nielsen, Thomas D.; Jensen, Finn V. [Aalborg Univ. (Denmark)

    2004-10-01

    Uncertainty in process operation occurs frequently in heat and power industry. This makes it hard to find the occurrence of an abnormal process state from a number of process signals (measurements) or find the correct cause to an abnormality. Among several other methods, Bayesian Networks (BN) is a method to build a model which can handle uncertainty in both process signals and the process itself. The purpose of this project is to investigate the possibilities to use BN for fault detection and diagnostics in combined heat and power industries through execution of two different applications. Participants from Aalborg University represent the knowledge of BN and participants from Maelardalen University have the experience from modelling heat and power applications. The co-operation also includes two energy companies; Elsam A/S (Nordjyllandsverket) and Maelarenergi AB (Vaesteraas CHP-plant), where the two applications are made with support from the plant personnel. The project ended out in two quite different applications. At Nordjyllandsverket, an application based (due to the lack of process knowledge) on pure operation data is build with capability to detect an abnormal process state in a coal mill. Detection is made through a conflict analysis when entering process signals into a model built by analysing the operation database. The application at Maelarenergi is built with a combination of process knowledge and operation data and can detect various faults caused by the fuel. The process knowledge is used to build a causal network structure and the structure is then trained by data from the operation database. Both applications are made as off-online applications, but they are ready for being run on-line. The performance of fault detection and diagnostics are good, but a lack of abnormal process states with known cause reduces the evaluation possibilities. Advantages with combining expert knowledge of the process with operation data are the possibility to represent

  7. The use of Bayesian Networks in Detecting the States of Ventilation Mills in Power Plants

    Directory of Open Access Journals (Sweden)

    Sanja Vujnović

    2014-06-01

    Full Text Available The main objective of this paper is to present a new method of predictive maintenance which can detect the states of coal grinding mills in thermal power plants using Bayesian networks. Several possible structures of Bayesian networks are proposed for solving this problem and one of them is implemented and tested on an actual system. This method uses acoustic signals and statistical signal pre-processing tools to compute the inputs of the Bayesian network. After that the network is trained and tested using signals measured in the vicinity of the mill in the period of 2 months. The goal of this algorithm is to increase the efficiency of the coal grinding process and reduce the maintenance cost by eliminating the unnecessary maintenance checks of the system.

  8. Learning Bayesian Network Structure%贝叶斯网络结构学习分析

    Institute of Scientific and Technical Information of China (English)

    王双成; 林士敏; 陆玉昌

    2000-01-01

    In this paper the analysis of principle and process of Bayesian network structure learning is given. Bayesian network structure learning is a process that seeks the best network structure fitting the prior knowledge and data. The computing of posterior can be closed when data are completed and some other conditions are satisfied ,while the computing is not closed when some data are missing. One solution for missing data is fill-in methods,another is to approximate the likelihood of structure,then to compute the probabilities of structure.

  9. Bayesian network modeling method based on case reasoning for emergency decision-making

    Directory of Open Access Journals (Sweden)

    XU Lei

    2013-06-01

    Full Text Available Bayesian network has the abilities of probability expression, uncertainty management and multi-information fusion.It can support emergency decision-making, which can improve the efficiency of decision-making.Emergency decision-making is highly time sensitive, which requires shortening the Bayesian Network modeling time as far as possible.Traditional Bayesian network modeling methods are clearly unable to meet that requirement.Thus, a Bayesian network modeling method based on case reasoning for emergency decision-making is proposed.The method can obtain optional cases through case matching by the functions of similarity degree and deviation degree.Then,new Bayesian network can be built through case adjustment by case merging and pruning.An example is presented to illustrate and test the proposed method.The result shows that the method does not have a huge search space or need sample data.The only requirement is the collection of expert knowledge and historical case models.Compared with traditional methods, the proposed method can reuse historical case models, which can reduce the modeling time and improve the efficiency.

  10. Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature.

    Science.gov (United States)

    Biedermann, A; Taroni, F

    2012-03-01

    Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation.

  11. Bayesian networks for evaluating forensic DNA profiling evidence: a review and guide to literature.

    Science.gov (United States)

    Biedermann, A; Taroni, F

    2012-03-01

    Almost 30 years ago, Bayesian networks (BNs) were developed in the field of artificial intelligence as a framework that should assist researchers and practitioners in applying the theory of probability to inference problems of more substantive size and, thus, to more realistic and practical problems. Since the late 1980s, Bayesian networks have also attracted researchers in forensic science and this tendency has considerably intensified throughout the last decade. This review article provides an overview of the scientific literature that describes research on Bayesian networks as a tool that can be used to study, develop and implement probabilistic procedures for evaluating the probative value of particular items of scientific evidence in forensic science. Primary attention is drawn here to evaluative issues that pertain to forensic DNA profiling evidence because this is one of the main categories of evidence whose assessment has been studied through Bayesian networks. The scope of topics is large and includes almost any aspect that relates to forensic DNA profiling. Typical examples are inference of source (or, 'criminal identification'), relatedness testing, database searching and special trace evidence evaluation (such as mixed DNA stains or stains with low quantities of DNA). The perspective of the review presented here is not exclusively restricted to DNA evidence, but also includes relevant references and discussion on both, the concept of Bayesian networks as well as its general usage in legal sciences as one among several different graphical approaches to evidence evaluation. PMID:21775236

  12. A new research tool for hybrid Bayesian networks using script language

    Science.gov (United States)

    Sun, Wei; Park, Cheol Young; Carvalho, Rommel

    2011-06-01

    While continuous variables become more and more inevitable in Bayesian networks for modeling real-life applications in complex systems, there are not much software tools to support it. Popular commercial Bayesian network tools such as Hugin, and Netica etc., are either expensive or have to discretize continuous variables. In addition, some free programs existing in the literature, commonly known as BNT, GeNie/SMILE, etc, have their own advantages and disadvantages respectively. In this paper, we introduce a newly developed Java tool for model construction and inference for hybrid Bayesian networks. Via the representation power of the script language, this tool can build the hybrid model automatically based on a well defined string that follows the specific grammars. Furthermore, it implements several inference algorithms capable to accommodate hybrid Bayesian networks, including Junction Tree algorithm (JT) for conditional linear Gaussian model (CLG), and Direct Message Passing (DMP) for general hybrid Bayesian networks with CLG structure. We believe this tool will be useful for researchers in the field.

  13. Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

    Science.gov (United States)

    Zhu, Shijia; Wang, Yadong

    2015-12-01

    Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

  14. Unavailability of the residual system heat removal of Angra 1 by Bayesian networks considering dependent failures

    International Nuclear Information System (INIS)

    This work models by Bayesian networks the residual heat removal system (SRCR) of Angra I nuclear power plant, using fault tree mapping for systematically identifying all possible modes of occurrence caused by a large loss of coolant accident (large LOCA). The focus is on dependent events, such as the bridge system structure of the residual heat removal system and the occurrence of common-cause failures. We used the Netica™ tool kit, Norsys Software Corporation and Python 2.7.5 for modeling Bayesian networks and Microsoft Excel for modeling fault trees. Working with dependent events using Bayesian networks is similar to the solutions proposed by other models, beyond simple understanding and ease of application and modification throughout the analysis. The results obtained for the unavailability of the system were satisfactory, showing that in most cases the system will be available to mitigate the effects of an accident as described above. (author)

  15. Parameterizing Bayesian network Representations of Social-Behavioral Models by Expert Elicitation

    Energy Technology Data Exchange (ETDEWEB)

    Walsh, Stephen J.; Dalton, Angela C.; Whitney, Paul D.; White, Amanda M.

    2010-05-23

    Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g parameter estimation, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. Parameter estimation from relevant extant data is a common approach to calibrating the model parameters. In practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and using the results in a methodology for calibrating the parameters of a Bayesian network.

  16. Bayesian Belief Network Method for Predicting Asphaltene Precipitation in Light Oil Reservoirs

    Directory of Open Access Journals (Sweden)

    Jeffrey O. Oseh (M.Sc.

    2015-04-01

    Full Text Available Asphaltene precipitation is caused by a number of factors including changes in pressure, temperature, and composition. The two most prevalent causes of asphaltene precipitation in light oil reservoirs are decreasing pressure and mixing oil with injected solvent in improved oil recovery processes. This study focused on predicting the amount of asphaltene precipitation with increasing Gas-Oil Ratio in a light oil reservoir using Bayesian Belief Network Method. These Artificial Intelligence-Bayesian Belief Network Method employed were validated and tested by unseen data to determine their accuracy and trend stability and were also compared with the findings obtained from Scaling equations. The obtained Bayesian Belief Network results indicated that the method showed an improved performance of predicting the amount of asphaltene precipitated in light oil reservoirs thus reducing the number of experiments required.

  17. Constitution and application of reactor make-up system's fault diagnostic Bayesian networks

    International Nuclear Information System (INIS)

    A fault diagnostic Bayesian network of reactor make-up system was constituted. The system's structure characters, operation rules and experts' experience were combined and an initial net was built. As the fault date sets were learned with the particle swarm optimization based Bayesian network structure, the structure of diagnostic net was completed and used to inference case. The built net can analyze diagnostic probability of every node in the net and afford assistant decision to fault diagnosis. (authors)

  18. Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks

    CERN Document Server

    Rao, Vinayak

    2012-01-01

    Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserved paths in these models by introducing a fast auxiliary variable Gibbs sampler. Our approach is based on the idea of uniformization, and sets up a Markov chain over paths by sampling a finite set of virtual jump times and then running a standard hidden Markov model forward filtering-backward sampling algorithm over states at the set of extant and virtual jump times. We demonstrate significant computational benefits over a state-of-the-art Gibbs sampler on a number of continuous time Bayesian networks.

  19. Multi-variable Echo State Network Optimized by Bayesian Regulation for Daily Peak Load Forecasting

    Directory of Open Access Journals (Sweden)

    Dongxiao Niu

    2012-11-01

    Full Text Available In this paper, a multi-variable echo state network trained with Bayesian regulation has been developed for the short-time load forecasting. In this study, we focus on the generalization of a new recurrent network. Therefore, Bayesian regulation and Levenberg-Marquardt algorithm is adopted to modify the output weight. The model is verified by data from a local power company in south China and its performance is rather satisfactory. Besides, traditional methods are also used for the same task as comparison. The simulation results lead to the conclusion that the proposed scheme is feasible and has great robustness and satisfactory capacity of generalization.

  20. Using literature and data to learn Bayesian networks as clinical models of ovarian tumors

    DEFF Research Database (Denmark)

    Antal, P.; Fannes, G.; Timmerman, D.;

    2004-01-01

    Thanks to its increasing availability, electronic literature has become a potential source of information for the development of complex Bayesian networks (BN), when human expertise is missing or data is scarce or contains much noise. This opportunity raises the question of how to integrate...... information from free-text resources with statistical data in learning Bayesian networks. Firstly, we report on the collection of prior information resources in the ovarian cancer domain, which includes "kernel" annotations of the domain variables. We introduce methods based on the annotations and literature...

  1. Learn the effective connectivity pattern of attention networks: a resting functional MRI and Bayesian network study

    Science.gov (United States)

    Li, Juan; Li, Rui; Yao, Li; Wu, Xia

    2011-03-01

    Task-based neuroimaging studies revealed that different attention operations were carried out by the functional interaction and cooperation between two attention systems: the dorsal attention network (DAN) and the ventral attention network (VAN), which were respectively involved in the "top-down" endogenous attention orienting and the "bottomup" exogenous attention reorienting process. Recent focused resting functional MRI (fMRI) studies found the two attention systems were inherently organized in the human brain regardless of whether or not the attention process were required, but how the two attention systems interact with each other in the absence of task is yet to be investigated. In this study, we first separated the DAN and VAN by applying the group independent component analysis (ICA) to the resting fMRI data acquired from 12 healthy young subjects, then used Gaussian Bayesian network (BN) learning approach to explore the plausible effective connectivity pattern of the two attention systems. It was found regions from the same attention network were strongly intra-dependent, and all the connections were located in the information flow from VAN to DAN, which suggested that an orderly functional interactions and information exchanges between the two attention networks existed in the intrinsic spontaneous brain activity, and the inherent connections might benefit the efficient cognitive process between DAN and VAN, such as the "top-down" and "bottom-up" reciprocal interaction when attention-related tasks were involved.

  2. Transmission Network Expansion Planning Considering Phase-Shifter Transformers

    Directory of Open Access Journals (Sweden)

    Celso T. Miasaki

    2012-01-01

    Full Text Available This paper presents a novel mathematical model for the transmission network expansion planning problem. Main idea is to consider phase-shifter (PS transformers as a new element of the transmission system expansion together with other traditional components such as transmission lines and conventional transformers. In this way, PS are added in order to redistribute active power flows in the system and, consequently, to diminish the total investment costs due to new transmission lines. Proposed mathematical model presents the structure of a mixed-integer nonlinear programming (MINLP problem and is based on the standard DC model. In this paper, there is also applied a specialized genetic algorithm aimed at optimizing the allocation of candidate components in the network. Results obtained from computational simulations carried out with IEEE-24 bus system show an outstanding performance of the proposed methodology and model, indicating the technical viability of using these nonconventional devices during the planning process.

  3. Applying Bayesian belief networks in rapid response situations

    Energy Technology Data Exchange (ETDEWEB)

    Gibson, William L [Los Alamos National Laboratory; Deborah, Leishman, A. [Los Alamos National Laboratory; Van Eeckhout, Edward [Los Alamos National Laboratory

    2008-01-01

    The authors have developed an enhanced Bayesian analysis tool called the Integrated Knowledge Engine (IKE) for monitoring and surveillance. The enhancements are suited for Rapid Response Situations where decisions must be made based on uncertain and incomplete evidence from many diverse and heterogeneous sources. The enhancements extend the probabilistic results of the traditional Bayesian analysis by (1) better quantifying uncertainty arising from model parameter uncertainty and uncertain evidence, (2) optimizing the collection of evidence to reach conclusions more quickly, and (3) allowing the analyst to determine the influence of the remaining evidence that cannot be obtained in the time allowed. These extended features give the analyst and decision maker a better comprehension of the adequacy of the acquired evidence and hence the quality of the hurried decisions. They also describe two example systems where the above features are highlighted.

  4. Bayesian networks for evaluation of evidence from forensic entomology.

    Science.gov (United States)

    Andersson, M Gunnar; Sundström, Anders; Lindström, Anders

    2013-09-01

    In the aftermath of a CBRN incident, there is an urgent need to reconstruct events in order to bring the perpetrators to court and to take preventive actions for the future. The challenge is to discriminate, based on available information, between alternative scenarios. Forensic interpretation is used to evaluate to what extent results from the forensic investigation favor the prosecutors' or the defendants' arguments, using the framework of Bayesian hypothesis testing. Recently, several new scientific disciplines have been used in a forensic context. In the AniBioThreat project, the framework was applied to veterinary forensic pathology, tracing of pathogenic microorganisms, and forensic entomology. Forensic entomology is an important tool for estimating the postmortem interval in, for example, homicide investigations as a complement to more traditional methods. In this article we demonstrate the applicability of the Bayesian framework for evaluating entomological evidence in a forensic investigation through the analysis of a hypothetical scenario involving suspect movement of carcasses from a clandestine laboratory. Probabilities of different findings under the alternative hypotheses were estimated using a combination of statistical analysis of data, expert knowledge, and simulation, and entomological findings are used to update the beliefs about the prosecutors' and defendants' hypotheses and to calculate the value of evidence. The Bayesian framework proved useful for evaluating complex hypotheses using findings from several insect species, accounting for uncertainty about development rate, temperature, and precolonization. The applicability of the forensic statistic approach to evaluating forensic results from a CBRN incident is discussed.

  5. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Directory of Open Access Journals (Sweden)

    Zengkai Liu

    Full Text Available This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  6. Providing probability distributions for the causal pathogen of clinical mastitis using naive Bayesian networks

    NARCIS (Netherlands)

    Steeneveld, W.; Gaag, van der L.C.; Barkema, H.W.; Hogeveen, H.

    2009-01-01

    Clinical mastitis (CM) can be caused by a wide variety of pathogens and farmers must start treatment before the actual causal pathogen is known. By providing a probability distribution for the causal pathogen, naive Bayesian networks (NBN) can serve as a management tool for farmers to decide which t

  7. Featuring Multiple Local Optima to Assist the User in the Interpretation of Induced Bayesian Network Models

    DEFF Research Database (Denmark)

    Dalgaard, Jens; Pena, Jose; Kocka, Tomas

    2004-01-01

    We propose a method to assist the user in the interpretation of the best Bayesian network model indu- ced from data. The method consists in extracting relevant features from the model (e.g. edges, directed paths and Markov blankets) and, then, assessing the con¯dence in them by studying multiple...

  8. On the use of Bayesian networks to combine raw data from related studies on sensory satiation

    NARCIS (Netherlands)

    Phan, V.A.; Ramaekers, M.G.; Bolhuis, D.P.; Garczarek, U.; Boekel, van M.A.J.S.; Dekker, M.

    2012-01-01

    Bayesian networks were used to combine raw datasets from two independently performed but related studies. Both studies investigated sensory satiation by measuring ad libitum intake of a tomato soup model. The Aroma study varied aroma concentration and aroma duration as the explanatory variables, and

  9. Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2008-01-01

    Applications of Bayesian networks in systems biology are computationally demanding due to the large number of model parameters. Conventional MCMC schemes based on proposal moves in structure space tend to be too slow in mixing and convergence, and have recently been superseded by proposal moves in t

  10. Comparison of a Bayesian Network with a Logistic Regression Model to Forecast IgA Nephropathy

    Directory of Open Access Journals (Sweden)

    Michel Ducher

    2013-01-01

    Full Text Available Models are increasingly used in clinical practice to improve the accuracy of diagnosis. The aim of our work was to compare a Bayesian network to logistic regression to forecast IgA nephropathy (IgAN from simple clinical and biological criteria. Retrospectively, we pooled the results of all biopsies (n=155 performed by nephrologists in a specialist clinical facility between 2002 and 2009. Two groups were constituted at random. The first subgroup was used to determine the parameters of the models adjusted to data by logistic regression or Bayesian network, and the second was used to compare the performances of the models using receiver operating characteristics (ROC curves. IgAN was found (on pathology in 44 patients. Areas under the ROC curves provided by both methods were highly significant but not different from each other. Based on the highest Youden indices, sensitivity reached (100% versus 67% and specificity (73% versus 95% using the Bayesian network and logistic regression, respectively. A Bayesian network is at least as efficient as logistic regression to estimate the probability of a patient suffering IgAN, using simple clinical and biological data obtained during consultation.

  11. Comparison of a Bayesian network with a logistic regression model to forecast IgA nephropathy.

    Science.gov (United States)

    Ducher, Michel; Kalbacher, Emilie; Combarnous, François; Finaz de Vilaine, Jérome; McGregor, Brigitte; Fouque, Denis; Fauvel, Jean Pierre

    2013-01-01

    Models are increasingly used in clinical practice to improve the accuracy of diagnosis. The aim of our work was to compare a Bayesian network to logistic regression to forecast IgA nephropathy (IgAN) from simple clinical and biological criteria. Retrospectively, we pooled the results of all biopsies (n = 155) performed by nephrologists in a specialist clinical facility between 2002 and 2009. Two groups were constituted at random. The first subgroup was used to determine the parameters of the models adjusted to data by logistic regression or Bayesian network, and the second was used to compare the performances of the models using receiver operating characteristics (ROC) curves. IgAN was found (on pathology) in 44 patients. Areas under the ROC curves provided by both methods were highly significant but not different from each other. Based on the highest Youden indices, sensitivity reached (100% versus 67%) and specificity (73% versus 95%) using the Bayesian network and logistic regression, respectively. A Bayesian network is at least as efficient as logistic regression to estimate the probability of a patient suffering IgAN, using simple clinical and biological data obtained during consultation.

  12. 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.

  13. Bayesian networks for clinical decision support : a rational approach to dynamic decision-making under uncertainty

    NARCIS (Netherlands)

    Gerven, M.A.J. van

    2007-01-01

    This dissertation deals with decision support in the context of clinical oncology. (Dynamic) Bayesian networks are used as a framework for (dynamic) decision-making under uncertainty and applied to a variety of diagnostic, prognostic, and treatment problems in medicine. It is shown that the proposed

  14. Constraint-based approach to discovery of inter module dependencies in modular Bayesian networks

    NARCIS (Netherlands)

    P. de Oude; G. Pavlin

    2009-01-01

    This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functional modules, each having a limited domain knowledge captured by a local Bayesian network. Different modules originate fr

  15. Bayesian estimation inherent in a Mexican-hat-type neural network

    Science.gov (United States)

    Takiyama, Ken

    2016-05-01

    Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

  16. Bayesian estimation inherent in a Mexican-hat-type neural network.

    Science.gov (United States)

    Takiyama, Ken

    2016-05-01

    Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

  17. Spatiotemporal Bayesian Networks for Malaria Prediction: Case Study of Northern Thailand.

    Science.gov (United States)

    Haddawy, Peter; Kasantikul, Rangwan; Hasan, A H M Imrul; Rattanabumrung, Chunyanuch; Rungrun, Pichamon; Suksopee, Natwipa; Tantiwaranpant, Saran; Niruntasuk, Natcha

    2016-01-01

    While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations of inferences. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating a village level model with weekly temporal resolution for Tha Song Yang district in northern Thailand. The network is learned using data on cases and environmental covariates. The network models incidence over time as well as evolution of the environmental variables, and captures time lagged and nonlinear effects. Out of sample evaluation shows the model to have high accuracy for one and two week predictions. PMID:27577491

  18. Measuring the consequences of wildfires in a Bayesian network with vulnerability and exposure indicators

    Science.gov (United States)

    Papakosta, Panagiota; Botzler, Sebastian; Krug, Kai; Straub, Daniel

    2013-04-01

    areas and rare species is also included. Presence of cultural heritage sites, power stations and power line network influence social exposure. The conceptual framework is demonstrated with a Bayesian Network (BN). The BN model incorporates empirical observation, physical models and expert knowledge; it can also explicitly account for uncertainty in the indicators. The proposed model is applied to the island of Cyprus. Maps support the demonstration of results. [1] Keeley, J.E.; Bond, W.J.; Bradstock, R.A.; Pausas, J.G.; Rundel, P.W. (2012): Fire in Mediterranean ecosystems: ecology, evolution and management. Cambridge University Press, New York, USA. [2] UN/ISDR (International Strategy for Disaster Reduction (2004): Living with Risk: A Global Review of Disaster Reduction Initiatives, Geneva, UN Publications. [3] Birkmann, J. (2006): Measuring vulnerability to natural hazards: towards disaster resilient societies. United Nations University Press, Tokyo, Japan.

  19. Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

    OpenAIRE

    Bingpeng Zhou; Qingchun Chen; Tiffany Jing Li; Pei Xiao

    2014-01-01

    The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offe...

  20. OVERALL SENSITIVITY ANALYSIS UTILIZING BAYESIAN NETWORK FOR THE QUESTIONNAIRE INVESTIGATION ON SNS

    OpenAIRE

    Tsuyoshi Aburai; Kazuhiro Takeyasu

    2013-01-01

    Social Networking Service (SNS) is prevailing rapidly in Japan in recent years. The most popular ones are Facebook, mixi, and Twitter, which are utilized in various fields of life together with the convenient tool such as smart-phone. In this work, a questionnaire investigation is carried out in order to clarify the current usage condition, issues and desired functions. More than 1,000 samples are gathered. Bayesian network is utilized for this analysis. Sensitivity analysis is carried out by...

  1. Recovering network topologies via Taylor expansion and compressive sensing.

    Science.gov (United States)

    Li, Guangjun; Wu, Xiaoqun; Liu, Juan; Lu, Jun-an; Guo, Chi

    2015-04-01

    Gaining knowledge of the intrinsic topology of a complex dynamical network is the precondition to understand its evolutionary mechanisms and to control its dynamical and functional behaviors. In this article, a general framework is developed to recover topologies of complex networks with completely unknown node dynamics based on Taylor expansion and compressive sensing. Numerical simulations illustrate the feasibility and effectiveness of the proposed method. Moreover, this method is found to have good robustness to weak stochastic perturbations. Finally, the impact of two major factors on the topology identification performance is evaluated. This method provides a natural and direct point to reconstruct network topologies from measurable data, which is likely to have potential applicability in a wide range of fields.

  2. Recovering network topologies via Taylor expansion and compressive sensing

    Energy Technology Data Exchange (ETDEWEB)

    Li, Guangjun; Liu, Juan, E-mail: xqwu@whu.edu.cn, E-mail: liujuanjp@163.com [Computer School, Wuhan University, Hubei 430072 (China); Wu, Xiaoqun, E-mail: xqwu@whu.edu.cn, E-mail: liujuanjp@163.com; Lu, Jun-an [School of Mathematics and Statistics, Wuhan University, Hubei 430072 (China); Guo, Chi [Global Navigation Satellite System Research Center, Wuhan University, Hubei 430072 (China)

    2015-04-15

    Gaining knowledge of the intrinsic topology of a complex dynamical network is the precondition to understand its evolutionary mechanisms and to control its dynamical and functional behaviors. In this article, a general framework is developed to recover topologies of complex networks with completely unknown node dynamics based on Taylor expansion and compressive sensing. Numerical simulations illustrate the feasibility and effectiveness of the proposed method. Moreover, this method is found to have good robustness to weak stochastic perturbations. Finally, the impact of two major factors on the topology identification performance is evaluated. This method provides a natural and direct point to reconstruct network topologies from measurable data, which is likely to have potential applicability in a wide range of fields.

  3. ON THE BOTTLENECK CAPACITY EXPANSION PROBLEMS ON NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Yang Chao; Zhang Jianzhong

    2006-01-01

    This article considers a class of bottleneck capacity expansion problems. Such problems aim to enhance bottleneck capacity to a certain level with minimum cost. Given a network G(V, A,(-C)) consisting of a set of nodes V = {v1, v2,………, vn}, a set of arcs A(∪-){(vi, vj) | i = 1, 2,………, n; j = 1, 2,………, n} and a capacity vector (-C). The component (-C)ij of (-C) is the capacity of arc (vi, vj). Define the capacity of a subset A' of A as the minimum capacity of the arcs in A, the capacity of a family F of subsets of A is the maximum capacity of its members. There axe two types of expanding models. In the arc-expanding model, the unit cost to increase the capacity of arc (vi, vj) is wij. In the node-expanding model, it is assumed that the capacities of all arcs (vi, vi) which start at the same node vi should be increased by the same amount and that the unit cost to make such expansion is wi. This article considers three kinds of bottleneck capacity expansion problems (path,spanning arborescence and maximum flow) in both expanding models. For each kind of expansion problems, this article discusses the characteristics of the problems and presents several results on the complexity of the problems.

  4. Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

    Directory of Open Access Journals (Sweden)

    Benjamin W. Y. Lo

    2013-01-01

    Full Text Available Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH. Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients. Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs. Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

  5. Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ying Yan

    2013-01-01

    Full Text Available Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much uncertainty in root node is produced by each risk, a new index, epistemic importance, is defined. Numerical examples show that the proposed methods could provide a lot of useful information. With the information, effective approaches could be found to control and avoid these sensitive risks, thus keep logistics financial business working more reliable. The proposed method also gives a quantitative measure of risk levels in logistics financial business, which provides guidance for the selection of financing solutions.

  6. Regularized variational Bayesian learning of echo state networks with delay&sum readout.

    Science.gov (United States)

    Shutin, Dmitriy; Zechner, Christoph; Kulkarni, Sanjeev R; Poor, H Vincent

    2012-04-01

    In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition. PMID:22168555

  7. Bayesian state space models for dynamic genetic network construction across multiple tissues.

    Science.gov (United States)

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

    Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

  8. Bayesian state space models for dynamic genetic network construction across multiple tissues.

    Science.gov (United States)

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

    Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes. PMID:27343475

  9. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

    Directory of Open Access Journals (Sweden)

    Michael J McGeachie

    2014-06-01

    Full Text Available Bayesian Networks (BN have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

  10. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

    Science.gov (United States)

    McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

    2014-06-01

    Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

  11. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

    Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

  12. SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

    Energy Technology Data Exchange (ETDEWEB)

    Kalet, A; Phillips, M; Gennari, J [UniversityWashington, Seattle, WA (United States)

    2014-06-01

    Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the

  13. AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks

    CERN Document Server

    Cheng, J; 10.1613/jair.764

    2011-01-01

    Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang...

  14. Prediction of Sybil attack on WSN using Bayesian network and swarm intelligence

    Science.gov (United States)

    Muraleedharan, Rajani; Ye, Xiang; Osadciw, Lisa Ann

    2008-04-01

    Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems, by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions are time-critical.

  15. D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks

    CERN Document Server

    Poczos, Barnabas

    2008-01-01

    We introduce a novel online Bayesian method for the identification of a family of noisy recurrent neural networks (RNNs). We develop Bayesian active learning technique in order to optimize the interrogating stimuli given past experiences. In particular, we consider the unknown parameters as stochastic variables and use the D-optimality principle, also known as `\\emph{infomax method}', to choose optimal stimuli. We apply a greedy technique to maximize the information gain concerning network parameters at each time step. We also derive the D-optimal estimation of the additive noise that perturbs the dynamical system of the RNN. Our analytical results are approximation-free. The analytic derivation gives rise to attractive quadratic update rules.

  16. A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

    Directory of Open Access Journals (Sweden)

    Ji Yae Shin

    2016-01-01

    Full Text Available Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI. The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs, exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.

  17. A Bayesian network approach to knowledge integration and representation of farm irrigation: 2. Model validation

    Science.gov (United States)

    Robertson, D. E.; Wang, Q. J.; Malano, H.; Etchells, T.

    2009-02-01

    For models to be useful, they need to adequately describe the systems they represent. The probabilistic nature of Bayesian network models has traditionally meant that model validation is difficult. In this paper we present a process to validate Inteca-Farm, a Bayesian network model of farm irrigation that we described in the first paper of this series. We assessed three aspects of the quality of model predictions, namely, bias, accuracy, and skill, for the two variables for which validation data are available directly or indirectly. We also examined model predictions for any systematic errors. The validation results show that the bias and accuracy of the two validated variables are within acceptable tolerances and that systematic errors are minimal. This suggests that Inteca-Farm is a plausible representation of farm irrigation system in the Shepparton Irrigation Region of northern Victoria, Australia.

  18. A Bayesian network approach to knowledge integration and representation of farm irrigation: 3. Spatial application

    Science.gov (United States)

    Robertson, D. E.; Wang, Q. J.; McAllister, A. T.; Abuzar, M.; Malano, H. M.; Etchells, T.

    2009-02-01

    Catchment managers are interested in understanding impacts of the management options they promote at both farm and regional scales. In this third paper of this series, we use Inteca-Farm, a Bayesian network model of farm irrigation in the Shepparton Irrigation Region of northern Victoria, Australia, to assess the current condition of management outcome measures and the impact of historical and future management intervention. To help overcome difficulties in comprehending modeling results that are expressed as probability distributions, to capture uncertainties, we introduce methods to spatially display and compare the output from Bayesian network models and to use these methods to compare model predictions for three management scenarios. Model predictions suggest that management intervention has made a substantial improvement to the condition of management outcome measures and that further improvements are possible. The results highlight that the management impacts are spatially variable, which demonstrates that farm modeling can provide valuable evidence in substantiating the impact of catchment management intervention.

  19. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2010-01-01

    Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

  20. A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production

    OpenAIRE

    Ge, L.; Asseldonk, van, N.; Valeeva, N.I.; Hennen, W.H.G.J.; Bergevoet, R.H.M.

    2011-01-01

    Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about the rationale underlying antibiotic usage. Animal health status and management quality are considered the two most important factors that influence farmersâ¿¿ decision-making concerning antibiotic use. Information on these two factors is therefore crucial in designing incentive mechanisms. In this paper, a Bayesian belief network (BBN) is built to represent the knowledge on how these factor...

  1. A HYBRID APPROACH FOR RELIABILITY ANALYSIS BASED ON ANALYTIC HIERARCHY PROCESS (AHP) AND BAYESIAN NETWORK (BN)

    OpenAIRE

    Muhammad eZubair

    2014-01-01

    The investigation of the nuclear accidents reveals that the accumulation of various technical and nontechnical lapses compounded the nuclear disaster. By using Analytic Hierarchy Process (AHP) and Bayesian Network (BN) the present research signifies the technical and nontechnical issues of nuclear accidents. The study exposed that besides technical fixes such as enhanced engineering safety features and better siting choices, the critical ingredient for safe operation of nuclear reactors lie i...

  2. A Hybrid Approach for Reliability Analysis Based on Analytic Hierarchy Process and Bayesian Network

    OpenAIRE

    Zubair, Muhammad

    2014-01-01

    By using analytic hierarchy process (AHP) and Bayesian Network (BN) the present research signifies the technical and non-technical issues of nuclear accidents. The study exposed that the technical faults was one major reason of these accidents. Keep an eye on other point of view it becomes clearer that human behavior like dishonesty, insufficient training, and selfishness are also play a key role to cause these accidents. In this study, a hybrid approach for reliability analysis based on AHP ...

  3. Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

    Science.gov (United States)

    Mengshoel, Ole J.

    2010-01-01

    One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

  4. Learning Bayesian Network to Explore Connectivity of Risk Factors in Enterprise Risk Management

    OpenAIRE

    Paradee Namwongse; Yachai Limpiyakorn

    2012-01-01

    Enterprise Risk Management provides a holistic top-down view of key risks facing an organization. Developing techniques that can exhibit the inter-connectivity of risks are required to effectively manage risks on an enterprise-wide. This research thus proposed Bayesian Network learning technique to explore the correlated risks in portfolio risk management using the Expressway Authority of Thailand for empirical study. The comparisons of three Bayes Net algorithms for building the risk map wer...

  5. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism

    OpenAIRE

    Archana Venkataraman; Duncan, James S.; Daniel Y.-J. Yang; Pelphrey, Kevin A.

    2015-01-01

    Resting-state functional magnetic resonance imaging (rsfMRI) studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD). Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differ...

  6. Bayesian Belief Network Method for Predicting Asphaltene Precipitation in Light Oil Reservoirs

    OpenAIRE

    Jeffrey O. Oseh (M.Sc.); Olugbenga A. Falode (Ph.D)

    2015-01-01

    Asphaltene precipitation is caused by a number of factors including changes in pressure, temperature, and composition. The two most prevalent causes of asphaltene precipitation in light oil reservoirs are decreasing pressure and mixing oil with injected solvent in improved oil recovery processes. This study focused on predicting the amount of asphaltene precipitation with increasing Gas-Oil Ratio in a light oil reservoir using Bayesian Belief Network Method. These Artificial Intelligence-Baye...

  7. A Bayesian Network Methodology for Infrastructure Seismic Risk Assessment and Decision Support

    OpenAIRE

    Bensi, Michelle Terese

    2010-01-01

    A Bayesian network methodology is developed for performing infrastructure seismic risk assessment and providing decision support with an emphasis on immediate post-earthquake applications. The methodology consists of four major components: (1) a seismic demand model of ground motion intensity as a spatially distributed Gaussian random field accounting for multiple seismic sources with uncertain characteristics and including finite fault rupture and directivity effects; (2) a model of the perf...

  8. Bayesian network as a modelling tool for risk management in agriculture

    OpenAIRE

    Svend Rasmussen; Madsen, Anders L.; Mogens Lund

    2013-01-01

    The importance of risk management increases as farmers become more exposed to risk. But risk management is a difficult topic because income risk is the result of the complex interaction of multiple risk factors combined with the effect of an increasing array of possible risk management tools. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be e...

  9. Identification of information tonality based on Bayesian approach and neural networks

    OpenAIRE

    Lande, D. V.

    2008-01-01

    A model of the identification of information tonality, based on Bayesian approach and neural networks was described. In the context of this paper tonality means positive or negative tone of both the whole information and its parts which are related to particular concepts. The method, its application is presented in the paper, is based on statistic regularities connected with the presence of definite lexemes in the texts. A distinctive feature of the method is its simplicity and versatility. A...

  10. A Bayesian network to predict vulnerability to sea-level rise: data report

    Science.gov (United States)

    Gutierrez, Benjamin T.; Plant, Nathaniel G.; Thieler, E. Robert

    2011-01-01

    During the 21st century, sea-level rise is projected to have a wide range of effects on coastal environments, development, and infrastructure. Consequently, there has been an increased focus on developing modeling or other analytical approaches to evaluate potential impacts to inform coastal management. This report provides the data that were used to develop and evaluate the performance of a Bayesian network designed to predict long-term shoreline change due to sea-level rise. The data include local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline-change rate compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this project, the Bayesian network is used to define relationships among driving forces, geologic constraints, and coastal responses. Using this information, the Bayesian network is used to make probabilistic predictions of shoreline change in response to different future sea-level-rise scenarios.

  11. Software Delivery Risk Management: Application of Bayesian Networks in Agile Software Development

    Directory of Open Access Journals (Sweden)

    Ancveire Ieva

    2015-12-01

    Full Text Available The information technology industry cannot be imagined without large- or small-scale projects. They are implemented to develop systems enabling key business processes and improving performance and enterprise resource management. However, projects often experience various difficulties during their execution. These problems are usually related to the three objectives of the project – costs, quality and deadline. A way these challenges can be solved is project risk management. However, not always the main problems and their influencing factors can be easily identified. Usually there is a need for a more profound analysis of the problem situation. In this paper, we propose the use of a Bayesian Network concept for quantitative risk management in agile projects. The Bayesian Network is explored using a case study focusing on a project that faces difficulties during the software delivery process. We explain why an agile risk analysis is needed and assess the potential risk factors, which may occur during the project. Thereafter, we design the Bayesian Network to capture the actual problem situation and make suggestions how to improve the delivery process based on the measures to be taken to reduce the occurrence of project risks.

  12. Reliability estimation of safety-critical software-based systems using Bayesian networks

    International Nuclear Information System (INIS)

    Due to the nature of software faults and the way they cause system failures new methods are needed for the safety and reliability evaluation of software-based safety-critical automation systems in nuclear power plants. In the research project 'Programmable automation system safety integrity assessment (PASSI)', belonging to the Finnish Nuclear Safety Research Programme (FINNUS, 1999-2002), various safety assessment methods and tools for software based systems are developed and evaluated. The project is financed together by the Radiation and Nuclear Safety Authority (STUK), the Ministry of Trade and Industry (KTM) and the Technical Research Centre of Finland (VTT). In this report the applicability of Bayesian networks to the reliability estimation of software-based systems is studied. The applicability is evaluated by building Bayesian network models for the systems of interest and performing simulations for these models. In the simulations hypothetical evidence is used for defining the parameter relations and for determining the ability to compensate disparate evidence in the models. Based on the experiences from modelling and simulations we are able to conclude that Bayesian networks provide a good method for the reliability estimation of software-based systems. (orig.)

  13. Analyzing the effect of introducing a kurtosis parameter in Gaussian Bayesian networks

    Energy Technology Data Exchange (ETDEWEB)

    Main, P. [Dpto. Estadistica e I.O., Fac. Ciencias Matematicas, Univ. Complutense de Madrid, 28040 Madrid (Spain)], E-mail: pmain@mat.ucm.es; Navarro, H. [Dpto. de Estadistica, I.O. y Calc. Numerico, Fac. Ciencias, UNED, 28040 Madrid (Spain)

    2009-05-15

    Gaussian Bayesian networks are graphical models that represent the dependence structure of a multivariate normal random variable with a directed acyclic graph (DAG). In Gaussian Bayesian networks the output is usually the conditional distribution of some unknown variables of interest given a set of evidential nodes whose values are known. The problem of uncertainty about the assumption of normality is very common in applications. Thus a sensitivity analysis of the non-normality effect in our conclusions could be necessary. The aspect of non-normality to be considered is the tail behavior. In this line, the multivariate exponential power distribution is a family depending on a kurtosis parameter that goes from a leptokurtic to a platykurtic distribution with the normal as a mesokurtic distribution. Therefore a more general model can be considered using the multivariate exponential power distribution to describe the joint distribution of a Bayesian network, with a kurtosis parameter reflecting deviations from the normal distribution. The sensitivity of the conclusions to this perturbation is analyzed using the Kullback-Leibler divergence measure that provides an interesting formula to evaluate the effect.

  14. Reliability estimation of safety-critical software-based systems using Bayesian networks

    Energy Technology Data Exchange (ETDEWEB)

    Helminen, A. [VTT Automation, Espoo (Finland)

    2001-06-01

    Due to the nature of software faults and the way they cause system failures new methods are needed for the safety and reliability evaluation of software-based safety-critical automation systems in nuclear power plants. In the research project 'Programmable automation system safety integrity assessment (PASSI)', belonging to the Finnish Nuclear Safety Research Programme (FINNUS, 1999-2002), various safety assessment methods and tools for software based systems are developed and evaluated. The project is financed together by the Radiation and Nuclear Safety Authority (STUK), the Ministry of Trade and Industry (KTM) and the Technical Research Centre of Finland (VTT). In this report the applicability of Bayesian networks to the reliability estimation of software-based systems is studied. The applicability is evaluated by building Bayesian network models for the systems of interest and performing simulations for these models. In the simulations hypothetical evidence is used for defining the parameter relations and for determining the ability to compensate disparate evidence in the models. Based on the experiences from modelling and simulations we are able to conclude that Bayesian networks provide a good method for the reliability estimation of software-based systems. (orig.)

  15. Construction of monitoring model and algorithm design on passenger security during shipping based on improved Bayesian network.

    Science.gov (United States)

    Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

    2014-01-01

    A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.

  16. An estimation method for inference of gene regulatory net-work using Bayesian network with uniting of partial problems

    Directory of Open Access Journals (Sweden)

    Watanabe Yukito

    2012-01-01

    Full Text Available Abstract Background Bayesian networks (BNs have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures. Results In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures. Conclusions We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

  17. Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition

    OpenAIRE

    Stephenson, Todd Andrew; Magimai.-Doss, Mathew; Bourlard, Hervé

    2001-01-01

    Standard hidden Markov models (HMMs), as used in automatic speech recognition (ASR), calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned on the hidden state variable, considering the emissions independent of any other variable in the model. Recent work showed the benefit of conditioning the emission distributions on a discrete auxiliary variable, which is observed in training and hidden in recognition. Related work has shown the ...

  18. Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

    Science.gov (United States)

    Plant, Nathaniel G.; Holland, K. Todd

    2011-01-01

    Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

  19. Reconstruction of large-scale gene regulatory networks using Bayesian model averaging.

    Science.gov (United States)

    Kim, Haseong; Gelenbe, Erol

    2012-09-01

    Gene regulatory networks provide the systematic view of molecular interactions in a complex living system. However, constructing large-scale gene regulatory networks is one of the most challenging problems in systems biology. Also large burst sets of biological data require a proper integration technique for reliable gene regulatory network construction. Here we present a new reverse engineering approach based on Bayesian model averaging which attempts to combine all the appropriate models describing interactions among genes. This Bayesian approach with a prior based on the Gibbs distribution provides an efficient means to integrate multiple sources of biological data. In a simulation study with maximum of 2000 genes, our method shows better sensitivity than previous elastic-net and Gaussian graphical models, with a fixed specificity of 0.99. The study also shows that the proposed method outperforms the other standard methods for a DREAM dataset generated by nonlinear stochastic models. In brain tumor data analysis, three large-scale networks consisting of 4422 genes were built using the gene expression of non-tumor, low and high grade tumor mRNA expression samples, along with DNA-protein binding affinity information. We found that genes having a large variation of degree distribution among the three tumor networks are the ones that see most involved in regulatory and developmental processes, which possibly gives a novel insight concerning conventional differentially expressed gene analysis. PMID:22987132

  20. Bayesian adaptive combination of short-term wind speed forecasts from neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Li, Gong; Shi, Jing; Zhou, Junyi [Department of Industrial and Manufacturing Engineering, North Dakota State University, Dept. 2485, PO Box 6050, Fargo, ND 58108 (United States)

    2011-01-15

    Short-term wind speed forecasting is of great importance for wind farm operations and the integration of wind energy into the power grid system. Adaptive and reliable methods and techniques of wind speed forecasts are urgently needed in view of the stochastic nature of wind resource varying from time to time and from site to site. This paper presents a robust two-step methodology for accurate wind speed forecasting based on Bayesian combination algorithm, and three neural network models, namely, adaptive linear element network (ADALINE), backpropagation (BP) network, and radial basis function (RBF) network. The hourly average wind speed data from two North Dakota sites are used to demonstrate the effectiveness of the proposed approach. The results indicate that, while the performances of the neural networks are not consistent in forecasting 1-h-ahead wind speed for the two sites or under different evaluation metrics, the Bayesian combination method can always provide adaptive, reliable and comparatively accurate forecast results. The proposed methodology provides a unified approach to tackle the challenging model selection issue in wind speed forecasting. (author)

  1. Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

    Directory of Open Access Journals (Sweden)

    McMahon Andrew P

    2009-12-01

    Full Text Available Abstract Background The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge. Results We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL. We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12. Conclusions The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.

  2. Dynamic Bayesian Networks in Classification-and-Ranking Architecture of Response Generation

    Directory of Open Access Journals (Sweden)

    Aida Mustapha

    2011-01-01

    Full Text Available Problem statement: The first component in classification-and-ranking architecture is a Bayesian classifier that classifies user utterances into response classes based on their semantic and pragmatic interpretations. Bayesian networks are sufficient if data is limited to single user input utterance. However, if the classifier is able to collate features from a sequence of previous n-1 user utterances, the additional information may or may not improve the accuracy rate in response classification. Approach: This article investigates the use of dynamic Bayesian networks to include time-series information in the form of extended features from preceding utterances. The experiment was conducted on SCHISMA corpus, which is a mixed-initiative, transaction dialogue in theater reservation. Results: The results show that classification accuracy is improved, but rather insignificantly. The accuracy rate tends to deteriorate as time-span of dialogue is increased. Conclusion: Although every response utterance reflects form and behavior that are expected by the preceding utterance, influence of meaning and intentions diminishes throughout time as the conversation stretches to longer duration.

  3. An empirical Bayesian approach for model-based inference of cellular signaling networks

    Directory of Open Access Journals (Sweden)

    Klinke David J

    2009-11-01

    Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

  4. Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

    Directory of Open Access Journals (Sweden)

    Abdelkrim Moussaoui

    2006-01-01

    Full Text Available The authors discuss the combination of an Artificial Neural Network (ANN with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models.

  5. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees

    Directory of Open Access Journals (Sweden)

    Chen Xiaoyu

    2007-12-01

    Full Text Available Abstract Background In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. Results We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. Conclusion Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

  6. Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

    Science.gov (United States)

    Plant, Nathaniel G.; Holland, K. Todd

    2011-01-01

    A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

  7. A new approach for supply chain risk management: Mapping SCOR into Bayesian network

    Directory of Open Access Journals (Sweden)

    Mahdi Abolghasemi

    2015-01-01

    Full Text Available Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs and supply chain operations reference (SCOR in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some

  8. A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homog

  9. MATRIX-VECTOR ALGORITHMS OF LOCAL POSTERIORI INFERENCE IN ALGEBRAIC BAYESIAN NETWORKS ON QUANTA PROPOSITIONS

    Directory of Open Access Journals (Sweden)

    A. A. Zolotin

    2015-07-01

    Full Text Available Posteriori inference is one of the three kinds of probabilistic-logic inferences in the probabilistic graphical models theory and the base for processing of knowledge patterns with probabilistic uncertainty using Bayesian networks. The paper deals with a task of local posteriori inference description in algebraic Bayesian networks that represent a class of probabilistic graphical models by means of matrix-vector equations. The latter are essentially based on the use of tensor product of matrices, Kronecker degree and Hadamard product. Matrix equations for calculating posteriori probabilities vectors within posteriori inference in knowledge patterns with quanta propositions are obtained. Similar equations of the same type have already been discussed within the confines of the theory of algebraic Bayesian networks, but they were built only for the case of posteriori inference in the knowledge patterns on the ideals of conjuncts. During synthesis and development of matrix-vector equations on quanta propositions probability vectors, a number of earlier results concerning normalizing factors in posteriori inference and assignment of linear projective operator with a selector vector was adapted. We consider all three types of incoming evidences - deterministic, stochastic and inaccurate - combined with scalar and interval estimation of probability truth of propositional formulas in the knowledge patterns. Linear programming problems are formed. Their solution gives the desired interval values of posterior probabilities in the case of inaccurate evidence or interval estimates in a knowledge pattern. That sort of description of a posteriori inference gives the possibility to extend the set of knowledge pattern types that we can use in the local and global posteriori inference, as well as simplify complex software implementation by use of existing third-party libraries, effectively supporting submission and processing of matrices and vectors when

  10. Object-Oriented Bayesian Networks (OOBN) for Aviation Accident Modeling and Technology Portfolio Impact Assessment

    Science.gov (United States)

    Shih, Ann T.; Ancel, Ersin; Jones, Sharon M.

    2012-01-01

    The concern for reducing aviation safety risk is rising as the National Airspace System in the United States transforms to the Next Generation Air Transportation System (NextGen). The NASA Aviation Safety Program is committed to developing an effective aviation safety technology portfolio to meet the challenges of this transformation and to mitigate relevant safety risks. The paper focuses on the reasoning of selecting Object-Oriented Bayesian Networks (OOBN) as the technique and commercial software for the accident modeling and portfolio assessment. To illustrate the benefits of OOBN in a large and complex aviation accident model, the in-flight Loss-of-Control Accident Framework (LOCAF) constructed as an influence diagram is presented. An OOBN approach not only simplifies construction and maintenance of complex causal networks for the modelers, but also offers a well-organized hierarchical network that is easier for decision makers to exploit the model examining the effectiveness of risk mitigation strategies through technology insertions.

  11. Bayesian Regularization in a Neural Network Model to Estimate Lines of Code Using Function Points

    Directory of Open Access Journals (Sweden)

    K. K. Aggarwal

    2005-01-01

    Full Text Available It is a well known fact that at the beginning of any project, the software industry needs to know, how much will it cost to develop and what would be the time required ? . This paper examines the potential of using a neural network model for estimating the lines of code, once the functional requirements are known. Using the International Software Benchmarking Standards Group (ISBSG Repository Data (release 9 for the experiment, this paper examines the performance of back propagation feed forward neural network to estimate the Source Lines of Code. Multiple training algorithms are used in the experiments. Results demonstrate that the neural network models trained using Bayesian Regularization provide the best results and are suitable for this purpose.

  12. Forensic Signature Detection of Yersinia Pestis Culturing Practices Across Institutions Using a Bayesian Network

    Energy Technology Data Exchange (ETDEWEB)

    Webb-Robertson, Bobbie-Jo M.; Corley, Courtney D.; McCue, Lee Ann; Clowers, Brian H.; Dowling, Chase P.; Wahl, Karen L.; Wunschel, David S.; Kreuzer, Helen W.

    2014-03-21

    The field of bioforensics is focused on the analysis of evidence from a biocrime. Existing laboratory analyses can identify the specific strain of an organism in the evidence, as well signatures of the specific culture batch of organisms, such as low-frequency contaminants or indicators of growth and processing methods. To link these disparate types of physical data to potential suspects, investigators may need to identify institutions or individuals whose access to strains and culturing practices match those identified from the evidence. In this work we present a Bayesian statistical network to fuse different types of analytical measurements that predict the production environment of a Yersinia pestis sample under investigation with automated test processing of scientific publications to identify institutions with a history of growing Y. pestis under similar conditions. Furthermore, the textual and experimental signatures were evaluated recursively to determine the overall sensitivity of the network across all levels of false positives. We illustrate that institutions associated with several specific culturing practices can be accurately selected based on the experimental signature from only a few analytical measurements. These findings demonstrate that similar Bayesian networks can be generated generically for many organisms of interest and their deployment is not prohibitive due to either computational or experimental factors.

  13. OVERALL SENSITIVITY ANALYSIS UTILIZING BAYESIAN NETWORK FOR THE QUESTIONNAIRE INVESTIGATION ON SNS

    Directory of Open Access Journals (Sweden)

    Tsuyoshi Aburai

    2013-11-01

    Full Text Available Social Networking Service (SNS is prevailing rapidly in Japan in recent years. The most popular ones are Facebook, mixi, and Twitter, which are utilized in various fields of life together with the convenient tool such as smart-phone. In this work, a questionnaire investigation is carried out in order to clarify the current usage condition, issues and desired functions. More than 1,000 samples are gathered. Bayesian network is utilized for this analysis. Sensitivity analysis is carried out by setting evidence to all items. This enables overall analysis for each item. We analyzed them by sensitivity analysis and some useful results were obtained. We have presented the paper concerning this. But the volume becomes too large, therefore we have split them and this paper shows the latter half of the investigation result by setting evidence to Bayesian Network parameters. Differences in usage objectives and SNS sites are made clear by the attributes and preference of SNS users. They can be utilized effectively for marketing by clarifying the target customer through the sensitivity analysis.

  14. A Study of New Method for Weapon System Effectiveness Evaluation Based on Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    YAN Dai-wei; GU Liang-xian; PAN Lei

    2008-01-01

    As weapon system effectiveness is affected by many factors, its evaluation is essentially a multi-criterion decision making problem for its complexity. The evaluation model of the effectiveness is established on the basis of metrics architecture of the effectiveness. The Bayesian network, which is used to evaluate the effectiveness, is established based on the metrics architecture and the evaluation models. For getting the weights of the metrics by Bayesian network, subjective initial values of the weights are given, gradient ascent algorithm is adopted, and the reasonable values of the weights are achieved. And then the effectiveness of every weapon system project is gained. The weapon system, whose effectiveness is relative maximum, is the optimization system. The research result shows that this method can solve the problem of AHP method which evaluation results are not compatible to the practice results and overcome the shortcoming of neural network in multilayer and multi-criterion decision. The method offers a new approaeh for evaluating the effectiveness.

  15. Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

    Directory of Open Access Journals (Sweden)

    Gao Shouguo

    2011-08-01

    Full Text Available Abstract Background Bayesian Network (BN is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable. Results We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information. Conclusion our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.

  16. Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching

    CERN Document Server

    Smaili, Cherif; Charpillet, François

    2007-01-01

    This paper presents a multi-sensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driving-assistance systems. Managing multihypotheses is a useful strategy for the road-matching problem. The multi-sensor fusion and multi-modal estimation are realized using Dynamical Bayesian Network. Experimental results, using data from Antilock Braking System (ABS) sensors, a differential Global Positioning System (GPS) receiver and an accurate digital roadmap, illustrate the performances of this approach, especially in ambiguous situations.

  17. An object-oriented Bayesian network modeling the causes of leg disorders in finisher herds

    DEFF Research Database (Denmark)

    Jensen, Tina Birk; Kristensen, Anders Ringgaard; Toft, Nils;

    2009-01-01

    pigs (e.g. results from diagnostic tests) were used to estimate the most likely cause of leg disorders at herd level. As information to the model originated from two different levels, we used an object-oriented structure in order to ease the specification of the Bayesian network. Hence, a Herd class...... the behaviour of the model, we investigated the value of different levels of evidence in two fictitious herds with different herd characteristics related to the risk of leg disorders (e.g. purchase policy, production type and the stocking density in pens). The model enabled us to demonstrate the value...

  18. Identification of information tonality based on Bayesian approach and neural networks

    CERN Document Server

    Lande, D V

    2008-01-01

    A model of the identification of information tonality, based on Bayesian approach and neural networks was described. In the context of this paper tonality means positive or negative tone of both the whole information and its parts which are related to particular concepts. The method, its application is presented in the paper, is based on statistic regularities connected with the presence of definite lexemes in the texts. A distinctive feature of the method is its simplicity and versatility. At present ideologically similar approaches are widely used to control spam.

  19. The Many-Body Expansion Combined with Neural Networks

    CERN Document Server

    Yao, Kun; Parkhill, John

    2016-01-01

    Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force field approximations for applications beyond single-point energies. Alongside the MBE, empirical models of ab-initio potential energy surfaces have improved, especially non-linear models based on neural networks (NN) which can reproduce ab-initio potential energy surfaces rapidly and accurately. Although they are fast, NNs suffer from their own curse of dimensionality; they must be trained on a representative sample of chemical space. In this paper we examine the synergy of the MBE and NN's, and explore their complementarity. The MBE offers a systematic way to treat systems of arbitrary size and intelligently sample chemical space. NN's reduce, by a factor in excess of $10^6$ the computational ove...

  20. Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions.

    Science.gov (United States)

    Werhli, Adriano V; Husmeier, Dirk

    2008-06-01

    There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al. where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets, which does not provide any mechanism for uncovering differences between the network structures associated with the different experimental conditions. We have assessed the viability of all proposed methods on data related to the Raf signaling pathway, generated both synthetically and in cytometry experiments. PMID:18574862

  1. Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

    Directory of Open Access Journals (Sweden)

    Pascal Caillet

    Full Text Available Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach.EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences.Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density.Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

  2. Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

    Science.gov (United States)

    McDowell, Ian C.; Zhao, Shiwen; Brown, Christopher D.; Engelhardt, Barbara E.

    2016-01-01

    Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues. PMID:27467526

  3. Process monitoring using kernel density estimation and Bayesian networking with an industrial case study.

    Science.gov (United States)

    Gonzalez, Ruben; Huang, Biao; Lau, Eric

    2015-09-01

    Principal component analysis has been widely used in the process industries for the purpose of monitoring abnormal behaviour. The process of reducing dimension is obtained through PCA, while T-tests are used to test for abnormality. Some of the main contributions to the success of PCA is its ability to not only detect problems, but to also give some indication as to where these problems are located. However, PCA and the T-test make use of Gaussian assumptions which may not be suitable in process fault detection. A previous modification of this method is the use of independent component analysis (ICA) for dimension reduction combined with kernel density estimation for detecting abnormality; like PCA, this method points out location of the problems based on linear data-driven methods, but without the Gaussian assumptions. Both ICA and PCA, however, suffer from challenges in interpreting results, which can make it difficult to quickly act once a fault has been detected online. This paper proposes the use of Bayesian networks for dimension reduction which allows the use of process knowledge enabling more intelligent dimension reduction and easier interpretation of results. The dimension reduction technique is combined with multivariate kernel density estimation, making this technique effective for non-linear relationships with non-Gaussian variables. The performance of PCA, ICA and Bayesian networks are compared on data from an industrial scale plant. PMID:25930233

  4. Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification

    Science.gov (United States)

    Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

    2016-01-01

    Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures. PMID:27463975

  5. Assessment of successful smoking cessation by psychological factors using the Bayesian network approach.

    Science.gov (United States)

    Yang, Xiaorong; Li, Suyun; Pan, Lulu; Wang, Qiang; Li, Huijie; Han, Mingkui; Zhang, Nan; Jiang, Fan; Jia, Chongqi

    2016-07-01

    The association between psychological factors and smoking cessation is complicated and inconsistent in published researches, and the joint effect of psychological factors on smoking cessation is unclear. This study explored how psychological factors jointly affect the success of smoking cessation using a Bayesian network approach. A community-based case control study was designed with 642 adult male successful smoking quitters as the cases, and 700 adult male failed smoking quitters as the controls. General self-efficacy (GSE), trait coping style (positive-trait coping style (PTCS) and negative-trait coping style (NTCS)) and self-rating anxiety (SA) were evaluated by GSE Scale, Trait Coping Style Questionnaire and SA Scale, respectively. Bayesian network was applied to evaluate the relationship between psychological factors and successful smoking cessation. The local conditional probability table of smoking cessation indicated that different joint conditions of psychological factors led to different outcomes for smoking cessation. Among smokers with high PTCS, high NTCS and low SA, only 36.40% successfully quitted smoking. However, among smokers with low pack-years of smoking, high GSE, high PTCS and high SA, 63.64% successfully quitted smoking. Our study indicates psychological factors jointly influence smoking cessation outcome. According to different joint situations, different solutions should be developed to control tobacco in practical intervention.

  6. Risk analysis of emergent water pollution accidents based on a Bayesian Network.

    Science.gov (United States)

    Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

    2016-01-01

    To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents.

  7. Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

    Directory of Open Access Journals (Sweden)

    Fang Yan

    Full Text Available Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie analysis was proposed by mapping bow-tie analysis into Bayesian network (BN. Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.

  8. Evaluation of Bayesian Networks in Participatory Water Resources Management, Upper Guadiana Basin, Spain

    Directory of Open Access Journals (Sweden)

    John Bromley

    2010-09-01

    Full Text Available Stakeholder participation is becoming increasingly important in water resources management. In participatory processes, stakeholders contribute by putting forward their own perspective, and they benefit by enhancing their understanding of the factors involved in decision making. A diversity of modeling tools can be used to facilitate participatory processes. Bayesian networks are well suited to this task for a variety of reasons, including their ability to structure discussions and visual appeal. This research focuses on developing and testing a set of evaluation criteria for public participation. The advantages and limitations of these criteria are discussed in the light of a specific participatory modeling initiative. Modeling work was conducted in the Upper Guadiana Basin in central Spain, where uncontrolled groundwater extraction is responsible for wetland degradation and conflicts between farmers, water authorities, and environmentalists. Finding adequate solutions to the problem is urgent because the implementation of the EU Water Framework Directive requires all aquatic ecosystems to be in a “good ecological state” within a relatively short time frame. Stakeholder evaluation highlights the potential of Bayesian networks to support public participation processes.

  9. Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network

    Directory of Open Access Journals (Sweden)

    Zhipeng Fang

    2014-01-01

    Full Text Available Most classical search engines choose and rank advertisements (ads based on their click-through rates (CTRs. To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method.

  10. Fuzzy Bayesian Network-Bow-Tie Analysis of Gas Leakage during Biomass Gasification.

    Science.gov (United States)

    Yan, Fang; Xu, Kaili; Yao, Xiwen; Li, Yang

    2016-01-01

    Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures. PMID:27463975

  11. Risk analysis of emergent water pollution accidents based on a Bayesian Network.

    Science.gov (United States)

    Tang, Caihong; Yi, Yujun; Yang, Zhifeng; Sun, Jie

    2016-01-01

    To guarantee the security of water quality in water transfer channels, especially in open channels, analysis of potential emergent pollution sources in the water transfer process is critical. It is also indispensable for forewarnings and protection from emergent pollution accidents. Bridges above open channels with large amounts of truck traffic are the main locations where emergent accidents could occur. A Bayesian Network model, which consists of six root nodes and three middle layer nodes, was developed in this paper, and was employed to identify the possibility of potential pollution risk. Dianbei Bridge is reviewed as a typical bridge on an open channel of the Middle Route of the South to North Water Transfer Project where emergent traffic accidents could occur. Risk of water pollutions caused by leakage of pollutants into water is focused in this study. The risk for potential traffic accidents at the Dianbei Bridge implies a risk for water pollution in the canal. Based on survey data, statistical analysis, and domain specialist knowledge, a Bayesian Network model was established. The human factor of emergent accidents has been considered in this model. Additionally, this model has been employed to describe the probability of accidents and the risk level. The sensitive reasons for pollution accidents have been deduced. The case has also been simulated that sensitive factors are in a state of most likely to lead to accidents. PMID:26433361

  12. A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development

    Science.gov (United States)

    Wang, Q. J.; Robertson, D. E.; Haines, C. L.

    2009-02-01

    Irrigation is important to many agricultural businesses but also has implications for catchment health. A considerable body of knowledge exists on how irrigation management affects farm business and catchment health. However, this knowledge is fragmentary; is available in many forms such as qualitative and quantitative; is dispersed in scientific literature, technical reports, and the minds of individuals; and is of varying degrees of certainty. Bayesian networks allow the integration of dispersed knowledge into quantitative systems models. This study describes the development, validation, and application of a Bayesian network model of farm irrigation in the Shepparton Irrigation Region of northern Victoria, Australia. In this first paper we describe the process used to integrate a range of sources of knowledge to develop a model of farm irrigation. We describe the principal model components and summarize the reaction to the model and its development process by local stakeholders. Subsequent papers in this series describe model validation and the application of the model to assess the regional impact of historical and future management intervention.

  13. Interconnection between biological abnormalities in borderline personality disorder: use of the Bayesian networks model.

    Science.gov (United States)

    De la Fuente, José Manuel; Bengoetxea, Endika; Navarro, Felipe; Bobes, Julio; Alarcón, Renato Daniel

    2011-04-30

    There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD.

  14. Comprehensive Influence Model of Preschool Children’s Personality Development Based on the Bayesian Network

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2014-01-01

    Full Text Available It is crucial to ascertain the comprehensive influence factors on personality for making effective cultivating plan. However, most existing literatures focus on the effect of individual factor on the personality. In order to comprehensively investigate the causal influences of preschool children’s temperament, school factors (teacher expectation and peer acceptance, and family factors (parental coparenting style, parental education value, and parental parenting style on the personality and the probability of the dependencies among these influence factors, we constructed the influencing factor model of personality development based on the Bayesian network. The models not only reflect the influence on personality development as a whole, but also obtain the probability relationships among the factors. Compared with other influence factors including family and school factors, temperament has more effect on the personality. In addition, teacher expectation also has an important influence on the personality. The experimental results show that it is a valuable exploration to construct the Bayesian network for comprehensively investigating the causal relationships between preschool children’s personality and related influence factors. Further, these results will be helpful to the cultivation of healthy personality.

  15. Bayesian networks precipitation model based on hidden Markov analysis and its application

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Surface precipitation estimation is very important in hydrologic forecast. To account for the influence of the neighbors on the precipitation of an arbitrary grid in the network, Bayesian networks and Markov random field were adopted to estimate surface precipitation. Spherical coordinates and the expectation-maximization (EM) algorithm were used for region interpolation, and for estimation of the precipitation of arbitrary point in the region. Surface precipitation estimation of seven precipitation stations in Qinghai Lake region was performed. By comparing with other surface precipitation methods such as Thiessen polygon method, distance weighted mean method and arithmetic mean method, it is shown that the proposed method can judge the relationship of precipitation among different points in the area under complicated circumstances and the simulation results are more accurate and rational.

  16. Longitudinal Prediction of the Infant Gut Microbiome with Dynamic Bayesian Networks.

    Science.gov (United States)

    McGeachie, Michael J; Sordillo, Joanne E; Gibson, Travis; Weinstock, George M; Liu, Yang-Yu; Gold, Diane R; Weiss, Scott T; Litonjua, Augusto

    2016-01-01

    Sequencing of the 16S rRNA gene allows comprehensive assessment of bacterial community composition from human body sites. Previously published and publicly accessible data on 58 preterm infants in the Neonatal Intensive Care Unit who underwent frequent stool collection was used. We constructed Dynamic Bayesian Networks from the data and analyzed predictive performance and network characteristics. We constructed a DBN model of the infant gut microbial ecosystem, which explicitly captured specific relationships and general trends in the data: increasing amounts of Clostridia, residual amounts of Bacilli, and increasing amounts of Gammaproteobacteria that then give way to Clostridia. Prediction performance of DBNs with fewer edges were overall more accurate, although less so on harder-to-predict subjects (p = 0.045). DBNs provided quantitative likelihood estimates for rare abruptions events. Iterative prediction was less accurate (p analysis of those samples. PMID:26853461

  17. Analysis and assessment of injury risk in female gymnastics:Bayesian Network approach

    Directory of Open Access Journals (Sweden)

    Lyudmila Dimitrova

    2015-02-01

    Full Text Available This paper presents a Bayesian network (BN model for estimating injury risk in female artistic gymnastics. The model illustrates the connections betweenunderlying injury risk factorsthrough a series ofcausal dependencies. The quantitativepart of the model – the conditional probability tables, are determined using ТNormal distribution with parameters, derived by experts. The injury rates calculated by the network are in an agreement with injury statistic data and correctly reports the impact of various risk factors on injury rates. The model is designed to assist coaches and supporting teams in planning the training activity so that injuries are minimized. This study provides important background for further data collection and research necessary to improve the precision of the quantitative predictions of the model.

  18. Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization, was developed, which is suitable for power transmission network expansion planning, and requires less computer s memory. The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzed. Numerical simulation demonstrated the effectiveness and correctness of the method, This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

  19. Probabilistic age classification with Bayesian networks: A study on the ossification status of the medial clavicular epiphysis.

    Science.gov (United States)

    Sironi, Emanuele; Pinchi, Vilma; Taroni, Franco

    2016-01-01

    In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

  20. Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.

    Directory of Open Access Journals (Sweden)

    Matthieu Vignes

    Full Text Available Modern technologies and especially next generation sequencing facilities are giving a cheaper access to genotype and genomic data measured on the same sample at once. This creates an ideal situation for multifactorial experiments designed to infer gene regulatory networks. The fifth "Dialogue for Reverse Engineering Assessments and Methods" (DREAM5 challenges are aimed at assessing methods and associated algorithms devoted to the inference of biological networks. Challenge 3 on "Systems Genetics" proposed to infer causal gene regulatory networks from different genetical genomics data sets. We investigated a wide panel of methods ranging from Bayesian networks to penalised linear regressions to analyse such data, and proposed a simple yet very powerful meta-analysis, which combines these inference methods. We present results of the Challenge as well as more in-depth analysis of predicted networks in terms of structure and reliability. The developed meta-analysis was ranked first among the 16 teams participating in Challenge 3A. It paves the way for future extensions of our inference method and more accurate gene network estimates in the context of genetical genomics.

  1. Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks

    Directory of Open Access Journals (Sweden)

    Chen Yidong

    2011-10-01

    Full Text Available Abstract Background Transcriptional regulation by transcription factor (TF controls the time and abundance of mRNA transcription. Due to the limitation of current proteomics technologies, large scale measurements of protein level activities of TFs is usually infeasible, making computational reconstruction of transcriptional regulatory network a difficult task. Results We proposed here a novel Bayesian non-negative factor model for TF mediated regulatory networks. Particularly, the non-negative TF activities and sample clustering effect are modeled as the factors from a Dirichlet process mixture of rectified Gaussian distributions, and the sparse regulatory coefficients are modeled as the loadings from a sparse distribution that constrains its sparsity using knowledge from database; meantime, a Gibbs sampling solution was developed to infer the underlying network structure and the unknown TF activities simultaneously. The developed approach has been applied to simulated system and breast cancer gene expression data. Result shows that, the proposed method was able to systematically uncover TF mediated transcriptional regulatory network structure, the regulatory coefficients, the TF protein level activities and the sample clustering effect. The regulation target prediction result is highly coordinated with the prior knowledge, and sample clustering result shows superior performance over previous molecular based clustering method. Conclusions The results demonstrated the validity and effectiveness of the proposed approach in reconstructing transcriptional networks mediated by TFs through simulated systems and real data.

  2. Bayesian Belief Networks for predicting drinking water distribution system pipe breaks

    International Nuclear Information System (INIS)

    In this paper, we use Bayesian Belief Networks (BBNs) to construct a knowledge model for pipe breaks in a water zone. To the authors’ knowledge, this is the first attempt to model drinking water distribution system pipe breaks using BBNs. Development of expert systems such as BBNs for analyzing drinking water distribution system data is not only important for pipe break prediction, but is also a first step in preventing water loss and water quality deterioration through the application of machine learning techniques to facilitate data-based distribution system monitoring and asset management. Due to the difficulties in collecting, preparing, and managing drinking water distribution system data, most pipe break models can be classified as “statistical–physical” or “hypothesis-generating.” We develop the BBN with the hope of contributing to the “hypothesis-generating” class of models, while demonstrating the possibility that BBNs might also be used as “statistical–physical” models. Our model is learned from pipe breaks and covariate data from a mid-Atlantic United States (U.S.) drinking water distribution system network. BBN models are learned using a constraint-based method, a score-based method, and a hybrid method. Model evaluation is based on log-likelihood scoring. Sensitivity analysis using mutual information criterion is also reported. While our results indicate general agreement with prior results reported in pipe break modeling studies, they also suggest that it may be difficult to select among model alternatives. This model uncertainty may mean that more research is needed for understanding whether additional pipe break risk factors beyond age, break history, pipe material, and pipe diameter might be important for asset management planning. - Highlights: • We show Bayesian Networks for predictive and diagnostic management of water distribution systems. • Our model may enable system operators and managers to prioritize system

  3. An ecosystem service approach to support integrated pond management: a case study using Bayesian belief networks--highlighting opportunities and risks.

    Science.gov (United States)

    Landuyt, Dries; Lemmens, Pieter; D'hondt, Rob; Broekx, Steven; Liekens, Inge; De Bie, Tom; Declerck, Steven A J; De Meester, Luc; Goethals, Peter L M

    2014-12-01

    Freshwater ponds deliver a broad range of ecosystem services (ESS). Taking into account this broad range of services to attain cost-effective ESS delivery is an important challenge facing integrated pond management. To assess the strengths and weaknesses of an ESS approach to support decisions in integrated pond management, we applied it on a small case study in Flanders, Belgium. A Bayesian belief network model was developed to assess ESS delivery under three alternative pond management scenarios: intensive fish farming (IFF), extensive fish farming (EFF) and nature conservation management (NCM). A probabilistic cost-benefit analysis was performed that includes both costs associated with pond management practices and benefits associated with ESS delivery. Whether or not a particular ESS is included in the analysis affects the identification of the most preferable management scenario by the model. Assessing the delivery of a more complete set of ecosystem services tends to shift the results away from intensive management to more biodiversity-oriented management scenarios. The proposed methodology illustrates the potential of Bayesian belief networks. BBNs facilitate knowledge integration and their modular nature encourages future model expansion to more encompassing sets of services. Yet, we also illustrate the key weaknesses of such exercises, being that the choice whether or not to include a particular ecosystem service may determine the suggested optimal management practice. PMID:25005053

  4. Applications of Bayesian filtering in wireless networks: Clock synchronization, localization, and RF tomography

    Science.gov (United States)

    Hamilton, Benjamin Russell

    In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a new paradigm to sense the network environment and could greatly enhance existing network capabilities. While there are some existing works addressing these problems, they all suffer from limitations. Current time synchronization methods are not energy efficient on small wireless devices with low quality oscillators. Existing localization methods do not consider additional sources of information available to nodes in the network such as measurements from accelerometers or models of the shadowing environment in the network. RF tomography has only been examined briefly in such networks, and current algorithms can not handle node mobility and rely on shadowing models that have not been experimentally verified. We address the time synchronization problem by analyzing the characteristics of the clocks in small wireless devices, developing a model for it, and then applying a Kalman filter to track both clock offset and skew. In our investigation into RF tomography, we present a method using a Kalman filter which jointly estimates and tracks static and dynamic objects in the environment. We also use channel measurements collected from a field test of our RF tomography testbed to compare RF shadowing models. For the localization problem, we present two algorithms incorporating additional information for improved localization: one based on a distributed extended Kalman filter that

  5. Bayesian Graphical Models

    DEFF Research Database (Denmark)

    Jensen, Finn Verner; Nielsen, Thomas Dyhre

    2016-01-01

    Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...... and edges. The nodes represent variables, which may be either discrete or continuous. An edge between two nodes A and B indicates a direct influence between the state of A and the state of B, which in some domains can also be interpreted as a causal relation. The wide-spread use of Bayesian networks...... is largely due to the availability of efficient inference algorithms for answering probabilistic queries about the states of the variables in the network. Furthermore, to support the construction of Bayesian network models, learning algorithms are also available. We give an overview of the Bayesian network...

  6. Application of Bayesian network methodology to the probabilistic risk assessment of nuclear waste disposal facility

    International Nuclear Information System (INIS)

    The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If one takes various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering some stochastic passive systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, it is believed that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief. In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this study proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. In this study an approximate probabilistic inference program for the specific Bayesian network developed and verified using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a Monte-Carlo model for uncertainty propagation of relevant parameters, were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, this study could connect the results of probabilistic

  7. Improving Accuracy of Authentication Process via Short Free Text using Bayesian Network

    Directory of Open Access Journals (Sweden)

    Charoon Chantan

    2012-03-01

    Full Text Available The internet security problems are a crucial threat to all users in the cyber world. One of the important problems about internet security concerned with user classification and authentication. However, there are multiple components to classify and authenticate users. The first one is using username/password and the second method is OTP or Token. This paper presents a novel method which cans Classify User via Short-text and IP Model (CUSIM to grant or reject a user in authentication. CUSIM is a Bayesian network model which utilizes the Bayesian Inference to authenticate the user. The objective of this paper is to use the model based on conditional independent with the prior knowledge, i.e. Keystroke dynamics, location used to connect to the internet, and IP address. Finally, a numerical example is provided to illustrate the probability of incorrect authentication and use an algorithm of machine learning to test the efficiency and find out the accuracy, FAR, and FRR. The model results gave better value of accuracy, FAR, and FRR.

  8. Bayesian model selection applied to artificial neural networks used for water resources modeling

    Science.gov (United States)

    Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

    2008-04-01

    Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

  9. Software Development Effort Estimation using Fuzzy Bayesian Belief Network with COCOMO II

    Directory of Open Access Journals (Sweden)

    B.Chakraborty

    2015-01-01

    Full Text Available Software development has always been characterized by some metrics. One of the greatest challenges for software developers lies in predicting the development effort for a software system which is based on developer abilities, size, complexity and other metrics. Several algorithmic cost estimation models such as Boehm?s COCOMO, Albrecht's' Function Point Analysis, Putnam?s SLIM, ESTIMACS etc. are available but every model has its own pros and cons in estimating development cost and effort. Most common reason being project data which is available in the initial stages of project is often incomplete, inconsistent, uncertain and unclear. In this paper, Bayesian probabilistic model has been explored to overcome the problems of uncertainty and imprecision resulting in improved process of software development effort estimation. This paper considers a software estimation approach using six key cost drivers in COCOMO II model. The selected cost drivers are the inputs to systems. The concept of Fuzzy Bayesian Belief Network (FBBN has been introduced to improve the accuracy of the estimation. Results shows that the value of MMRE (Mean of Magnitude of Relative Error and PRED obtained by means of FBBN is much better as compared to the MMRE and PRED of Fuzzy COCOMO II models. The validation of results was carried out on NASA-93 dem COCOMO II dataset.

  10. Ranking Features on Psychological Dynamics of Cooperative Team Work through Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Pilar Fuster-Parra

    2016-05-01

    Full Text Available The aim of this study is to rank some features that characterize the psychological dynamics of cooperative team work in order to determine priorities for interventions and formation: leading positive feedback, cooperative manager and collaborative manager features. From a dataset of 20 cooperative sport teams (403 soccer players, the characteristics of the prototypical sports teams are studied using an average Bayesian network (BN and two special types of BNs, the Bayesian classifiers: naive Bayes (NB and tree augmented naive Bayes (TAN. BNs are selected as they are able to produce probability estimates rather than predictions. BN results show that the antecessors (the “top” features ranked are the team members’ expectations and their attraction to the social aspects of the task. The main node is formed by the cooperative behaviors, the consequences ranked at the BN bottom (ratified by the TAN trees and the instantiations made, the roles assigned to the members and their survival inside the same team. These results should help managers to determine contents and priorities when they have to face team-building actions.

  11. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

    KAUST Repository

    Zhang, Xuesong

    2011-11-01

    Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. © 2011 Elsevier B.V.

  12. A Measure of Systems Engineering Effectiveness in Government Acquisition of Complex Information Systems: A Bayesian Belief Network-Based Approach

    Science.gov (United States)

    Doskey, Steven Craig

    2014-01-01

    This research presents an innovative means of gauging Systems Engineering effectiveness through a Systems Engineering Relative Effectiveness Index (SE REI) model. The SE REI model uses a Bayesian Belief Network to map causal relationships in government acquisitions of Complex Information Systems (CIS), enabling practitioners to identify and…

  13. Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk; Edwards, Kieron D.; Ghazal, Peter; Millar, Andrew J.

    2008-01-01

    Method: The objective of the present article is to propose and evaluate a probabilistic approach based on Bayesian networks for modelling non-homogeneous and non-linear gene regulatory processes. The method is based on a mixture model, using latent variables to assign individual measurements to diff

  14. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes.

    Science.gov (United States)

    Chen, Cong; Zhang, Guohui; Tarefder, Rafiqul; Ma, Jianming; Wei, Heng; Guan, Hongzhi

    2015-07-01

    Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance. PMID:25888994

  15. Bayesian Networks Construction and Their Applications in Data Mining%贝叶斯学习、贝叶斯网络与数据采掘

    Institute of Scientific and Technical Information of China (English)

    林士敏; 田凤占; 陆玉昌

    2000-01-01

    Recently Bayesian networks(BN)become a noticeable research direction in Data Mining,ln this paper we introduce the structure of Bayesian networks ,and the process of constructing a BN ,with the emphasis on the basic methods of learning from prior knowledge and sample data,using Bayesian learning approach,to identify the structures and probabilities of BN. The merits of Bayesian networks are that prior knowledge can be combined with observed data,which is important'especially when data is scarce or expensive ,that causal relationships among data can be learned ,and incomplete data set can be readily handled,which other models are disable to do so. It can foresee that Bayesian networks will become a powerful tools in Data Mining.

  16. Team performance and collective efficacy in the dynamic psychology of competitive team: a Bayesian network analysis.

    Science.gov (United States)

    Fuster-Parra, P; García-Mas, A; Ponseti, F J; Leo, F M

    2015-04-01

    The purpose of this paper was to discover the relationships among 22 relevant psychological features in semi-professional football players in order to study team's performance and collective efficacy via a Bayesian network (BN). The paper includes optimization of team's performance and collective efficacy using intercausal reasoning pattern which constitutes a very common pattern in human reasoning. The BN is used to make inferences regarding our problem, and therefore we obtain some conclusions; among them: maximizing the team's performance causes a decrease in collective efficacy and when team's performance achieves the minimum value it causes an increase in moderate/high values of collective efficacy. Similarly, we may reason optimizing team collective efficacy instead. It also allows us to determine the features that have the strongest influence on performance and which on collective efficacy. From the BN two different coaching styles were differentiated taking into account the local Markov property: training leadership and autocratic leadership.

  17. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  18. Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model

    Directory of Open Access Journals (Sweden)

    Viet Anh Nguyen

    2012-12-01

    Full Text Available This paper represents a new approach to manage learner modeling in an adaptive learning system framework. It considers developing the basic components of an adaptive learning system such as the learner model, the course content model and the adaptation engine. We use the overlay model and Bayesian network to evaluate learners’ knowledge. In addition, we also propose a new content modeling method as well as adaptation engine to generate adaptive course based on learner’s knowledge. Based on this approach, we developed an adaptive learning system named is ACGS-II, that teaches students how to design an Entity Relationship model in a database system course. Empirical testing results for students who used the application indicate that our proposed model is very helpful as guidelines to develop adaptive learning system to meet learners’ demands.

  19. An urban flood risk assessment method using the Bayesian Network approach

    DEFF Research Database (Denmark)

    Åström, Helena Lisa Alexandra

    the Bayesian Network (BN) approach is developed, and the method is exemplified in an urban catchment. BNs have become an increasingly popular method for describing complex systems and aiding decision-making under uncertainty. In environmental management, BNs have mainly been utilized in ecological assessments...... decision and utility nodes, which are beneficial in decision-making problems. This thesis aims at addressing four specific challenges identified in FRA and showing how these challenges may be addressed using an ID. Firstly, this thesis presents how an ID can be utilized to describe the temporal dimension...... are connected with each other by connecting the adaptation nodes in the time slices. Secondly, this thesis recognizes the need for including a spatial dimension in FRA. An urban catchment is rarely homogenous, and there are areas that have a higher risk than others. From a decision-making point of view...

  20. Team performance and collective efficacy in the dynamic psychology of competitive team: a Bayesian network analysis.

    Science.gov (United States)

    Fuster-Parra, P; García-Mas, A; Ponseti, F J; Leo, F M

    2015-04-01

    The purpose of this paper was to discover the relationships among 22 relevant psychological features in semi-professional football players in order to study team's performance and collective efficacy via a Bayesian network (BN). The paper includes optimization of team's performance and collective efficacy using intercausal reasoning pattern which constitutes a very common pattern in human reasoning. The BN is used to make inferences regarding our problem, and therefore we obtain some conclusions; among them: maximizing the team's performance causes a decrease in collective efficacy and when team's performance achieves the minimum value it causes an increase in moderate/high values of collective efficacy. Similarly, we may reason optimizing team collective efficacy instead. It also allows us to determine the features that have the strongest influence on performance and which on collective efficacy. From the BN two different coaching styles were differentiated taking into account the local Markov property: training leadership and autocratic leadership. PMID:25546263

  1. Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    2012-01-01

    In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

  2. Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

    CERN Document Server

    Luqman, Muhammad Muzzamil; Ramel, Jean-Yves

    2010-01-01

    We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.

  3. Estimation of mutation rates from paternity cases using a Bayesian network

    DEFF Research Database (Denmark)

    Vicard, P.; Dawid, A.P.; Mortera, J.;

    We present a statistical model and methodology for making inferences about mutation rates from paternity casework. This takes proper account of a number of sources of potential bias, including hidden mutation, incomplete family triplets, uncertain paternity status and differing maternal...... and paternal mutation rates, while allowing a wide variety of mutation models. A Bayesian network is constructed to facilitate computation of the likelihood function for the mutation parameters. It can process both full and summary genotypic information, from both complete putative father-mother-child triplets...... and defective cases where only the child and one of its parents are observed. Detailed analysis of a specific dataset is used to illustrate the effects of the various types of biases, and of the assumed mutation model, on inferences about mutation parameters....

  4. Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks

    Directory of Open Access Journals (Sweden)

    Hamelryck Thomas

    2010-03-01

    Full Text Available Abstract Background Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs. It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations. Results The program package is freely available under the GNU General Public Licence (GPL from SourceForge http://sourceforge.net/projects/mocapy. The package contains the source for building the Mocapy++ library, several usage examples and the user manual. Conclusions Mocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.

  5. Development of Bayesian Network Models for Risk-Based Ship Design

    Institute of Scientific and Technical Information of China (English)

    Dimitris Konovessis; Wenkui Cai; Dracos Vassalos

    2013-01-01

    In the past fifteen years,the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry.The risk-based ship design (RBD) methodology,advocating systematic integration of risk assessment within the conventional design process has started to takeoff.Despite this wide recognition and increasing popularity,important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process.This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches.This method should help to generate appropriate risk models in the selected platform (Bayesian networks) which can be employed for decision making at design stage.

  6. Development of Bayesian network models for risk-based ship design

    Science.gov (United States)

    Konovessis, Dimitris; Cai, Wenkui; Vassalos, Dracos

    2013-06-01

    In the past fifteen years, the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry. The risk-based ship design (RBD) methodology, advocating systematic integration of risk assessment within the conventional design process has started to takeoff. Despite this wide recognition and increasing popularity, important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process. This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches. This method should help to generate appropriate risk models in the selected platform (Bayesian networks) which can be employed for decision making at design stage.

  7. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

    Directory of Open Access Journals (Sweden)

    Tsuda Koji

    2007-11-01

    Full Text Available Abstract Background Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge – such as sparse connectivity – and that allows to design experiments such that maximal information is gained from each one. Results Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiments substantially. We employ sparse linear models, and show how to perform full Bayesian inference for these. We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method. The representation of uncertainty enables us to do effective experimental design in a standard statistical setting: experiments are selected such that the experiments are maximally informative. Conclusion Few methods have addressed the design issue so far. Compared to the most well-known one, our method is more transparent, and is shown to perform qualitatively superior. In the former, hard and unrealistic constraints have to be placed on the network structure for mere computational tractability, while such are not required in our method. We demonstrate reconstruction and optimal experimental design capabilities on tasks generated from realistic non-linear network simulators. The methods described in the paper are available as a Matlab package at http://www.kyb.tuebingen.mpg.de/sparselinearmodel.

  8. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology

    OpenAIRE

    Grzegorczyk, M.; Husmeier, D.

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint pr...

  9. Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

    Science.gov (United States)

    Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

    2014-01-01

    Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features

  10. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Science.gov (United States)

    Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

    2015-01-01

    Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors. PMID:26571415

  11. Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

    Directory of Open Access Journals (Sweden)

    Bojan eMihaljević

    2014-11-01

    Full Text Available Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neurocientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neurocientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs, and developed a method to predict them. This method predicts a LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels and that the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and therefore might serve as objective counterparts to the subjective

  12. Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project

    International Nuclear Information System (INIS)

    The sound development of marine resource usage relies on a strong maritime engineering industry. The perilous marine environment poses the highest risk to all maritime work. It is therefore imperative to reduce the risk associated with maritime work by using some analytical methods other than engineering techniques. This study addresses this issue by using an integrated interpretive structure modeling (ISM) and Bayesian network (BN) approach in a risk assessment context. Mitigating or managing maritime risk relies primarily on domain expert experience and knowledge. ISM can be used to incorporate expert knowledge in a systematic manner and helps to impose order and direction on complex relationships that exist among system elements. Working with experts, this research used ISM to clearly specify an engineering risk factor relationship represented by a cause–effect diagram, which forms the structure of the BN. The expert subjective judgments were further transformed into a prior and conditional probability set to be embedded in the BN. We used the BN to evaluate the risks of two offshore pipeline projects in Taiwan. The results indicated that the BN can provide explicit risk information to support better project management. - Highlights: • We adopt an integrated method for risk assessment of offshore pipeline projects. • We conduct semi-structural interview with the experts for risk factor identification. • Interpretive structural modeling helps to form the digraph of Bayesian network (BN) • We perform the risk analysis with the experts by building a BN. • Risk evaluations of two case studies using the BN show effectiveness of the methods

  13. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    Full Text Available Aberrant activation of sonic Hegdehog (SHH signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs. To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays. We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

  14. Reliability Analysis of I and C Architecture of Research Reactors Using Bayesian Networks

    International Nuclear Information System (INIS)

    The objective of this research project is to identify a configuration of architecture which gives highest availability with maintaining low cost of manufacturing. In this regard, two configurations of a single channel of RPS are formulated in the current article and BN models were constructed. Bayesian network analysis was performed to find the reliability features. This is a continuation of study towards the standardization of I and C architecture for low and medium power research reactors. This research is the continuation of study to analyze the reliability of single channel of Reactor Protection System (RPS) using Bayesian networks. The focus of research was on the development of architecture for low power research reactors. What level of reliability is sufficient for protection, safety and control systems in case of low power research reactors? There should be a level which should satisfy all the regulatory requirements as well as operational demands with optimized cost of construction. Scholars, researchers and material investigators from educational and research institutes are demanding for construction of more research reactors. In order to meet this demand and construct more units, it is necessary to do more research in various areas. The research is also needed to make a standardization of research reactor I and C architectures on the same lines of commercial power plants. The research reactors are categorized into two broad categories, Low power research reactors and medium to high power research reactors. According to IAEA TECDOC-1234, Research reactors with 0.250-2.0 MW power rating or 2.5-10 Χ 1011 n/cm2.s. flux are termed low power reactor whereas research reactors ranging from 2-10 MW power rating or 0.1-10 Χ 1013 n/cm2.s. are considered as Medium to High power research reactors. Some other standards (IAEA NP-T-5.1) define multipurpose research reactor ranging from power few hundred KW to 10 MW as low power research reactor

  15. Use of dynamic Bayesian networks for life extension assessment of ageing systems

    International Nuclear Information System (INIS)

    Extending the operating lifetime of ageing technical systems is of great interest for industrial applications. Life extension requires identifying and selecting decision alternatives which allow for a safe and economic operation of the system beyond its design lifetime. This article proposes a dynamic Bayesian network for assessing the life extension of ageing repairable systems. The main objective of the model is to provide decision support based on the system performance during a finite time horizon, which is defined by the life extension period. The model has three main applications: (i) assessing and selecting optimal decision alternatives for the life extension at present time, based on historical data; (ii) identifying and minimizing the factors that have a negative impact on the system performance; and (iii) reassessing and optimizing the decision alternatives during operation throughout the life extension period, based on updating the model with new operational data gathered. A case study illustrates the application of the model for life extension of a real firewater pump system in an oil and gas facility. The case study analyzes three decision alternatives, where preventive maintenance and functional test policies are optimized, and the uncertainty involved in each alternative is computed. - Highlights: • A dynamic Bayesian network is used for predicting the system performance. • The performance is measured with relevant variables: cost; unavailability; safety. • The model can be used when scarce data is available, no degradation data is needed. • The uncertainty associated to each alternative is computed in the model. • A detailed case study of a real safety system shows the applicability of the model

  16. A dynamic Bayesian network based approach to safety decision support in tunnel construction

    International Nuclear Information System (INIS)

    This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

  17. Comparison of two different stochastic models for extracting protein regulatory pathways using Bayesian networks.

    Science.gov (United States)

    Grzegorczyk, Marco

    2008-01-01

    Toxicoproteomics integrates traditional toxicology and systems biology and seeks to infer the architecture of biochemical pathways in biological systems that are affected by and respond to chemical and environmental exposures. Different reverse engineering methods for extracting biochemical regulatory networks from data have been proposed and it is important to understand their relative strengths and weaknesses. To shed some light onto this problem, Werhli et al. (2006) cross-compared three widely used methodologies, relevance networks, graphical Gaussian models, and Bayesian networks (BN), on real cytometric and synthetic expression data. This study continues with the evaluation and compares the learning performances of two different stochastic models (BGe and BDe) for BN. Cytometric protein expression data from the RAF-signaling pathway were used for the cross-method comparison. Understanding this pathway is an important task, as it is known that RAF is a critical signaling protein whose deregulation leads to carcinogenesis. When the more flexible BDe model is employed, a data discretization, which usually incurs an inevitable information loss, is needed. However, the results of the study reveal that the BDe model is preferable to the BGe model when a sufficiently large number of observations from the pathway are available. PMID:18569581

  18. Bayesian spatial joint modeling of traffic crashes on an urban road network.

    Science.gov (United States)

    Zeng, Qiang; Huang, Helai

    2014-06-01

    This study proposes a Bayesian spatial joint model of crash prediction including both road segments and intersections located in an urban road network, through which the spatial correlations between heterogeneous types of entities could be considered. A road network in Hillsborough, Florida, with crash, road, and traffic characteristics data for a three-year period was selected in order to compare the proposed joint model with three site-level crash prediction models, that is, the Poisson, negative binomial (NB), and conditional autoregressive (CAR) models. According to the results, the CAR and Joint models outperform the Poisson and NB models in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-entity spatial correlations. Although the goodness-of-fit and predictive performance of the CAR and Joint models are equivalent in this case study, spatial correlations between segments and the connected intersections are found to be more significant than those solely between segments or between intersections, which supports the employment of the Joint model as an alternative in road-network-level safety modeling.

  19. The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

    Directory of Open Access Journals (Sweden)

    Yihua Zhong

    2016-01-01

    Full Text Available Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.

  20. Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Saini, Lalit Mohan [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119 (India)

    2008-07-15

    Up to 7 days ahead electrical peak load forecasting has been done using feed forward neural network based on Steepest descent, Bayesian regularization, Resilient and adaptive backpropagation learning methods, by incorporating the effect of eleven weather parameters and past peak load information. To avoid trapping of network into a state of local minima, the optimization of user-defined parameters viz., learning rate and error goal has been performed. The sliding window concept has been incorporated for selection of training data set. It was then reduced as per relevant selection according to the day type and season for which the forecast is made. To reduce the dimensionality of input matrix, the Principal Component Analysis method of factor extraction or correlation analysis technique has been used and their performance has been compared. The resultant data set was used for training of three-layered neural network. In order to increase the learning speed, the weights and biases were initialized according to Nguyen and Widrow method. To avoid over fitting, early stopping of training was done at the minimum validation error. (author)

  1. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism

    Directory of Open Access Journals (Sweden)

    Archana Venkataraman

    2015-01-01

    Full Text Available Resting-state functional magnetic resonance imaging (rsfMRI studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD. Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of “language” and “comprehension” as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes “social” and “person”. The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder.

  2. An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism.

    Science.gov (United States)

    Venkataraman, Archana; Duncan, James S; Yang, Daniel Y-J; Pelphrey, Kevin A

    2015-01-01

    Resting-state functional magnetic resonance imaging (rsfMRI) studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD). Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE) and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of "language" and "comprehension" as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes "social" and "person". The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder.

  3. Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

    Directory of Open Access Journals (Sweden)

    Ramoni Marco F

    2007-05-01

    Full Text Available Abstract Background Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. Results We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. Conclusion The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between

  4. A Bayesian network approach for modeling local failure in lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam, E-mail: elnaqa@wustl.edu [Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110 (United States)

    2011-03-21

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

  5. Research on Factors Influencing Municipal Household Solid Waste Separate Collection: Bayesian Belief Networks

    Directory of Open Access Journals (Sweden)

    Zhujie Chu

    2016-02-01

    Full Text Available Municipal household solid waste (MHSW has become a serious problem in China over the course of the last two decades, resulting in significant side effects to the environment. Therefore, effective management of MHSW has attracted wide attention from both researchers and practitioners. Separate collection, the first and crucial step to solve the MHSW problem, however, has not been thoroughly studied to date. An empirical survey has been conducted among 387 households in Harbin, China in this study. We use Bayesian Belief Networks model to determine the influencing factors on separate collection. Four types of factors are identified, including political, economic, social cultural and technological based on the PEST (political, economic, social and technological analytical method. In addition, we further analyze the influential power of different factors, based on the network structure and probability changes obtained by Netica software. Results indicate that technological dimension has the greatest impact on MHSW separate collection, followed by the political dimension and economic dimension; social cultural dimension impacts MHSW the least.

  6. Exploring the utility of Bayesian Networks for modelling cultural ecosystem services: A canoeing case study.

    Science.gov (United States)

    Shaw, Edward; Kumar, Vikas; Lange, Eckart; Lerner, David N

    2016-01-01

    Modelling cultural ecosystem services is challenging as they often involve subjective and intangible concepts. As a consequence they have been neglected in ecosystem service studies, something that needs remedying if environmental decision making is to be truly holistic. We suggest Bayesian Networks (BNs) have a number of qualities that may make them well-suited for dealing with cultural services. For example, they define relationships between variables probabilistically, enabling conceptual and physical variables to be linked, and therefore the numerical representation of stakeholder opinions. We assess whether BNs are a good method for modelling cultural services by building one collaboratively with canoeists to predict how the subjective concepts of fun and danger are impacted on by weir modification. The BN successfully captured the relationships between the variables, with model output being broadly consistent with verbal descriptions by the canoeists. There were however a number of discrepancies indicating imperfect knowledge capture. This is likely due to the structure of the network and the abstract and laborious nature of the probability elicitation stage. New techniques should be developed to increase the intuitiveness and efficiency of probability elicitation. The limitations we identified with BNs are avoided if their structure can be kept simple, and it is in such circumstances that BNs can offer a good method for modelling cultural ecosystem services.

  7. Exploring the utility of Bayesian Networks for modelling cultural ecosystem services: A canoeing case study.

    Science.gov (United States)

    Shaw, Edward; Kumar, Vikas; Lange, Eckart; Lerner, David N

    2016-01-01

    Modelling cultural ecosystem services is challenging as they often involve subjective and intangible concepts. As a consequence they have been neglected in ecosystem service studies, something that needs remedying if environmental decision making is to be truly holistic. We suggest Bayesian Networks (BNs) have a number of qualities that may make them well-suited for dealing with cultural services. For example, they define relationships between variables probabilistically, enabling conceptual and physical variables to be linked, and therefore the numerical representation of stakeholder opinions. We assess whether BNs are a good method for modelling cultural services by building one collaboratively with canoeists to predict how the subjective concepts of fun and danger are impacted on by weir modification. The BN successfully captured the relationships between the variables, with model output being broadly consistent with verbal descriptions by the canoeists. There were however a number of discrepancies indicating imperfect knowledge capture. This is likely due to the structure of the network and the abstract and laborious nature of the probability elicitation stage. New techniques should be developed to increase the intuitiveness and efficiency of probability elicitation. The limitations we identified with BNs are avoided if their structure can be kept simple, and it is in such circumstances that BNs can offer a good method for modelling cultural ecosystem services. PMID:26345252

  8. Asymptotic expansions for large closed and loss queueing networks

    Directory of Open Access Journals (Sweden)

    Kogan Yaakov

    2002-01-01

    Full Text Available Loss and closed queueing network models have long been of interest to telephone and computer engineers and becoming increasingly important as models of data transmission networks. This paper describes a uniform approach that has been developed during the last decade for asymptotic analysis of large capacity networks with product form of the stationary probability distribution. Such a distribution has an explicit form up to the normalization constant, or the partition function. The approach is based on representing the partition function as a contour integral in complex space and evaluating the integral using the saddle point method and theory of residues. This paper provides an introduction to the area and a review of recent work.

  9. A Bayesian network to predict coastal vulnerability to sea level rise

    Science.gov (United States)

    Gutierrez, B.T.; Plant, N.G.; Thieler, E.R.

    2011-01-01

    Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (-1 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions. Copyright ?? 2011 by the American Geophysical Union.

  10. Network and guest dependent thermal stability and thermal expansion in a trigonal host

    Indian Academy of Sciences (India)

    Viswanadha G Saraswatula; Mukhtar Ahmad Bhat; Suman Bhattacharya; Binoy K Saha

    2014-09-01

    Thermal stability and thermal expansion of bromo trimer synthon mediated hexagonal inclusion compounds of 2,4,6-tris(4-bromophenoxy)-1,3,5-triazine (BrPOT) with dichloromethane (DCM), tetrahydrofuran (THF) and hexamethyl benzene (HMB) and also the guest-free form of BrPOT are reported. Each of these three guests produced two concomitant inclusion compounds with BrPOT. The thermal stability of the solvate lattice increases with decreasing cavity size. The channel network of the DCM inclusion compound is stable only for a few seconds at room temperature outside the mother liquor, whereas the cage network of the DCM solvate is stable for months under similar conditions. Thermal expansions of the lattices depend upon the network, guest content as well as the type of guest molecules. The guest-free form exhibits the least thermal expansion in this series of systems.

  11. Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Yue Zhao

    2012-12-01

    Full Text Available Audio‐visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi‐stream Dynamic Bayesian Network and coupled HMM are widely used for audio‐visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN to perform unsupervised extraction of spatial‐temporal multimodal features from Tibetan audio‐visual speech data and build an accurate audio‐visual speech recognition model under a no frame‐independency assumption. The experiment results on Tibetan speech data from some real‐world environments showed the proposed DDBN outperforms the state‐of‐art methods in word recognition accuracy.

  12. 3D mapping of buried underworld infrastructure using dynamic Bayesian network based multi-sensory image data fusion

    Science.gov (United States)

    Dutta, Ritaban; Cohn, Anthony G.; Muggleton, Jen M.

    2013-05-01

    The successful operation of buried infrastructure within urban environments is fundamental to the conservation of modern living standards. In this paper a novel multi-sensor image fusion framework has been proposed and investigated using dynamic Bayesian network for automatic detection of buried underworld infrastructure. Experimental multi-sensors images were acquired for a known buried plastic water pipe using Vibro-acoustic sensor based location methods and Ground Penetrating Radar imaging system. Computationally intelligent conventional image processing techniques were used to process three types of sensory images. Independently extracted depth and location information from different images regarding the target pipe were fused together using dynamic Bayesian network to predict the maximum probable location and depth of the pipe. The outcome from this study was very encouraging as it was able to detect the target pipe with high accuracy compared with the currently existing pipe survey map. The approach was also applied successfully to produce a best probable 3D buried asset map.

  13. Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search

    CERN Document Server

    Malone, Brandon; Hansen, Eric A; Bridges, Susan

    2012-01-01

    Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memory-efficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadth-first branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.

  14. Dynamical Bayesian Inference of Time-evolving Interactions: From a Pair of Coupled Oscillators to Networks of Oscillators

    CERN Document Server

    Duggento, Andrea; McClintock, Peter V E; Stefanovska, Aneta

    2012-01-01

    Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. (Phys. Rev. Lett. 109 024101, 2012) introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time- evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically-generated data, data from an analog electronic circuit, and cardio-respiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.

  15. A Novel Prediction Algorithm of DR Position Error Based on Bayesian Regularization Back-propagation Neural Network

    OpenAIRE

    Li Honglian; Fang Hong; Tang Ju; Zhang Jun; Zhang Jing

    2013-01-01

    It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS) during GPS outages, a novel prediction algorithm of dead reckon (DR) position error is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. DR, GPS position data are first de-noised and compared at different stationary wavelet transformation (SWT) decomposition level, and DR position error data are acquired after the SWT coefficients differences are reconstructed. A...

  16. Risk-Based Analysis of Drilling Waste Handling Operations. Bayesian Network, Cost-effectiveness, and Operational Conditions

    OpenAIRE

    Ayele, Yonas Zewdu

    2016-01-01

    The papers of this thesis are not available in Munin. Paper I. Ayele YZ, Barabadi A, Barabady J.: A methodology for identification of a suitable drilling waste handling system in the Arctic region. (Manuscript). Paper II. Ayele YZ, Barabady J, Droguett EL.: Dynamic Bayesian network based risk assessment for Arctic offshore drilling waste handling practices. (Manuscript). Published version available in Journal of Offshore Mechanics and Arctic Engineering 138(5), 051302 (Jun 17, 2016) ...

  17. The use of Bayesian Belief Networks for combining disparate sources of information in the safety assessment of software based systems

    OpenAIRE

    Gran, Bjørn Axel

    2002-01-01

    The objective of the research has been to investigate the possibility to transfer the requirements of a software safety standard into Bayesian belief networks (BBNs). The BBN methodology has mainly been developed and applied in the AI society, but more recently it has been proposed to apply it to the assessment of programmable systems. The relation to AI application is relevant in the sense that the method reflects the way of an assessor's thinking during the assessment process. Conceptually,...

  18. The Analysis about the Network Expansion of Enterprise Viral Marketing

    OpenAIRE

    Shourong Tang

    2014-01-01

    There are a lot of unexpected changes taking place in the online world. Viral marketing is a typical efficient network marketing method which births at the background of the Web2.0 era. It is high concerned with its clipping and comprehensive effect on society. Studying its mechanism, in addition to the spanking speed of the Internet, the rapid transmission method and the wide range of audience, it mostly depends on idiographic interactive involvement and relationship. This article mainly sum...

  19. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

    Science.gov (United States)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-01-01

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment. PMID:22850067

  20. A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

    Science.gov (United States)

    Grzegorczyk, Marco; Husmeier, Dirk

    2012-07-12

    An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment.

  1. Using Bayesian network and AHP method as a marketing approach tools in defining tourists’ preferences

    Directory of Open Access Journals (Sweden)

    Nataša Papić-Blagojević

    2012-04-01

    Full Text Available Marketing approach is associated to market conditions and achieving long term profitability of a company by satisfying consumers’ needs. This approach in tourism does not have to be related only to promoting one touristic destination, but is associated to relation between travel agency and its clients too. It considers that travel agencies adjust their offers to their clients’ needs. In that sense, it is important to analyze the behavior of tourists in the earlier periods with consideration of their preferences. Using Bayesian network, it could be graphically displayed the connection between tourists who have similar taste and relationships between them. On the other hand, the analytic hierarchy process (AHP is used to rank tourist attractions, with also relying on past experience. In this paper we examine possible applications of these two models in tourism in Serbia. The example is hypothetical, but it will serve as a base for future research. Three types of tourism are chosen as a representative in Vojvodina: Cultural, Rural and Business tourism, because they are the bright spot of touristic development in this area. Applied on these forms, analytic hierarchy process has shown its strength in predicting tourists’ preferences.

  2. Using Bayesian Belief Network (BBN) modelling for Rapid Source Term Prediction. RASTEP Phase 1

    Energy Technology Data Exchange (ETDEWEB)

    Knochenhauer, M.; Swaling, V.H.; Alfheim, P. [Scandpower AB, Sundbyberg (Sweden)

    2012-09-15

    The project is connected to the development of RASTEP, a computerized source term prediction tool aimed at providing a basis for improving off-site emergency management. RASTEP uses Bayesian belief networks (BBN) to model severe accident progression in a nuclear power plant in combination with pre-calculated source terms (i.e., amount, timing, and pathway of released radio-nuclides). The output is a set of possible source terms with associated probabilities. In the NKS project, a number of complex issues associated with the integration of probabilistic and deterministic analyses are addressed. This includes issues related to the method for estimating source terms, signal validation, and sensitivity analysis. One major task within Phase 1 of the project addressed the problem of how to make the source term module flexible enough to give reliable and valid output throughout the accident scenario. Of the alternatives evaluated, it is recommended that RASTEP is connected to a fast running source term prediction code, e.g., MARS, with a possibility of updating source terms based on real-time observations. (Author)

  3. A Bayesian network model for assessing natural estrogen fate and transport in a swine waste lagoon.

    Science.gov (United States)

    Lee, Boknam; Kullman, Seth W; Yost, Erin; Meyer, Michael T; Worley-Davis, Lynn; Williams, C Michael; Reckhow, Kenneth H

    2014-10-01

    Commercial swine waste lagoons are regarded as a major reservoir of natural estrogens, which have the potential to produce adverse physiological effects on exposed aquatic organisms and wildlife. However, there remains limited understanding of the complex mechanisms of physical, chemical, and biological processes that govern the fate and transport of natural estrogens within an anaerobic swine lagoon. To improve lagoon management and ultimately help control the offsite transport of these compounds from swine operations, a probabilistic Bayesian network model was developed to assess natural estrogen fate and budget and then compared against data collected from a commercial swine field site. In general, the model was able to describe the estrogen fate and budget in both the slurry and sludge stores within the swine lagoon. Sensitivity analysis within the model demonstrated that the estrogen input loading from the associated barn facility was the most important factor in controlling estrogen concentrations within the lagoon slurry storage, whereas the settling rate was the most significant factor in the lagoon sludge storage. The degradation reactions were shown to be minor in both stores based on prediction of average total estrogen concentrations. Management scenario evaluations demonstrated that the best possible management options to reduce estrogen levels in the lagoon are either to adjust the estrogen input loading from swine barn facilities or to effectively enhance estrogen bonding with suspended solids through the use of organic polymers or inorganic coagulants.

  4. Modeling of Radiation Pneumonitis after Lung Stereotactic Body Radiotherapy: A Bayesian Network Approach

    CERN Document Server

    Lee, Sangkyu; Jeyaseelan, Krishinima; Faria, Sergio; Kopek, Neil; Brisebois, Pascale; Vu, Toni; Filion, Edith; Campeau, Marie-Pierre; Lambert, Louise; Del Vecchio, Pierre; Trudel, Diane; El-Sokhn, Nidale; Roach, Michael; Robinson, Clifford; Naqa, Issam El

    2015-01-01

    Background and Purpose: Stereotactic body radiotherapy (SBRT) for lung cancer accompanies a non-negligible risk of radiation pneumonitis (RP). This study presents a Bayesian network (BN) model that connects biological, dosimetric, and clinical RP risk factors. Material and Methods: 43 non-small-cell lung cancer patients treated with SBRT with 5 fractions or less were studied. Candidate RP risk factors included dose-volume parameters, previously reported clinical RP factors, 6 protein biomarkers at baseline and 6 weeks post-treatment. A BN ensemble model was built from a subset of the variables in a training cohort (N=32), and further tested in an independent validation cohort (N=11). Results: Key factors identified in the BN ensemble for predicting RP risk were ipsilateral V5, lung volume receiving more than 105% of prescription, and decrease in angiotensin converting enzyme (ACE) from baseline to 6 weeks. External validation of the BN ensemble model yielded an area under the curve of 0.8. Conclusions: The BN...

  5. Novel dynamic Bayesian networks for facial action element recognition and understanding

    Science.gov (United States)

    Zhao, Wei; Park, Jeong-Seon; Choi, Dong-You; Lee, Sang-Woong

    2011-12-01

    In daily life, language is an important tool of communication between people. Besides language, facial action can also provide a great amount of information. Therefore, facial action recognition has become a popular research topic in the field of human-computer interaction (HCI). However, facial action recognition is quite a challenging task due to its complexity. In a literal sense, there are thousands of facial muscular movements, many of which have very subtle differences. Moreover, muscular movements always occur simultaneously when the pose is changed. To address this problem, we first build a fully automatic facial points detection system based on a local Gabor filter bank and principal component analysis. Then, novel dynamic Bayesian networks are proposed to perform facial action recognition using the junction tree algorithm over a limited number of feature points. In order to evaluate the proposed method, we have used the Korean face database for model training. For testing, we used the CUbiC FacePix, facial expressions and emotion database, Japanese female facial expression database, and our own database. Our experimental results clearly demonstrate the feasibility of the proposed approach.

  6. Composite behavior analysis for video surveillance using hierarchical dynamic Bayesian networks

    Science.gov (United States)

    Cheng, Huanhuan; Shan, Yong; Wang, Runsheng

    2011-03-01

    Analyzing composite behaviors involving objects from multiple categories in surveillance videos is a challenging task due to the complicated relationships among human and objects. This paper presents a novel behavior analysis framework using a hierarchical dynamic Bayesian network (DBN) for video surveillance systems. The model is built for extracting objects' behaviors and their relationships by representing behaviors using spatial-temporal characteristics. The recognition of object behaviors is processed by the DBN at multiple levels: features of objects at low level, objects and their relationships at middle level, and event at high level, where event refers to behaviors of a single type object as well as behaviors consisting of several types of objects such as ``a person getting in a car.'' Furthermore, to reduce the complexity, a simple model selection criterion is addressed, by which the appropriated model is picked out from a pool of candidate models. Experiments are shown to demonstrate that the proposed framework could efficiently recognize and semantically describe composite object and human activities in surveillance videos.

  7. Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

    Institute of Scientific and Technical Information of China (English)

    郑建军; 刘玉树; 陈立潮

    2003-01-01

    The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.

  8. Design of Korean nuclear reliability data-base network using a two-stage Bayesian concept

    International Nuclear Information System (INIS)

    In an analysis of probabilistic risk, safety, and reliability of a nuclear power plant, the reliability data base (DB) must be established first. As the importance of the reliability data base increases, event reporting systems such as the US Nuclear Regulatory Commission's Licensee Event Report and the International Atomic Energy Agency's Incident Reporting System have been developed. In Korea, however, the systematic reliability data base is not yet available. Therefore, foreign data bases have been directly quoted in reliability analyses of Korean plants. In order to develop a reliability data base for Korean plants, the problem is which methodology is to be used, and the application limits of the selected method must be solved and clarified. After starting the commercial operation of Korea Nuclear Unit-1 (KNU-1) in 1978, six nuclear power plants have begun operation. Of these, only KNU-3 is a Canada Deuterium Uranium pressurized heavy-water reactor, and the others are all pressurized water reactors. This paper describes the proposed reliability data-base network (KNRDS) for Korean nuclear power plants in the context of two-stage Bayesian (TSB) procedure of Kaplan. It describes the concept of TSB to obtain the Korean-specific plant reliability data base, which is updated with the incorporation of both the reported generic reliability data and the operation experiences of similar plants

  9. Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays

    Directory of Open Access Journals (Sweden)

    Huawei Wang

    2013-01-01

    Full Text Available Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN have been used to build the aviation operation safety assessment model based on flight delay. The structure and parameters learning of the model have been researched. By using BN model, some airline in China has been selected to assess safety risk of civil aviation. The civil aviation safety risk of BN model has been assessed by GeNIe software. The research results show that flight delay, which increases the safety risk of civil aviation, can be seen as incremental safety risk. The effectiveness and correctness of the model have been tested and verified.

  10. A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis

    International Nuclear Information System (INIS)

    Bayesian network (BN) is a powerful tool for human reliability analysis (HRA) as it can characterize the dependency among different human performance shaping factors (PSFs) and associated actions. It can also quantify the importance of different PSFs that may cause a human error. Data required to fully quantify BN for HRA in offshore emergency situations are not readily available. For many situations, there is little or no appropriate data. This presents significant challenges to assign the prior and conditional probabilities that are required by the BN approach. To handle the data scarcity problem, this paper presents a data collection methodology using a virtual environment for a simplified BN model of offshore emergency evacuation. A two-level, three-factor experiment is used to collect human performance data under different mustering conditions. Collected data are integrated in the BN model and results are compared with a previous study. The work demonstrates that the BN model can assess the human failure likelihood effectively. Besides, the BN model provides the opportunities to incorporate new evidence and handle complex interactions among PSFs and associated actions

  11. A Bayesian network model for assessing natural estrogen fate and transport in a swine waste lagoon.

    Science.gov (United States)

    Lee, Boknam; Kullman, Seth W; Yost, Erin; Meyer, Michael T; Worley-Davis, Lynn; Williams, C Michael; Reckhow, Kenneth H

    2014-10-01

    Commercial swine waste lagoons are regarded as a major reservoir of natural estrogens, which have the potential to produce adverse physiological effects on exposed aquatic organisms and wildlife. However, there remains limited understanding of the complex mechanisms of physical, chemical, and biological processes that govern the fate and transport of natural estrogens within an anaerobic swine lagoon. To improve lagoon management and ultimately help control the offsite transport of these compounds from swine operations, a probabilistic Bayesian network model was developed to assess natural estrogen fate and budget and then compared against data collected from a commercial swine field site. In general, the model was able to describe the estrogen fate and budget in both the slurry and sludge stores within the swine lagoon. Sensitivity analysis within the model demonstrated that the estrogen input loading from the associated barn facility was the most important factor in controlling estrogen concentrations within the lagoon slurry storage, whereas the settling rate was the most significant factor in the lagoon sludge storage. The degradation reactions were shown to be minor in both stores based on prediction of average total estrogen concentrations. Management scenario evaluations demonstrated that the best possible management options to reduce estrogen levels in the lagoon are either to adjust the estrogen input loading from swine barn facilities or to effectively enhance estrogen bonding with suspended solids through the use of organic polymers or inorganic coagulants. PMID:24798317

  12. Prediction of near-term breast cancer risk using a Bayesian belief network

    Science.gov (United States)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (pbreast cancer risk than with a single feature.

  13. A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks.

    Science.gov (United States)

    Yue, Kun; Fang, Qiyu; Wang, Xiaoling; Li, Jin; Liu, Weiyi

    2015-12-01

    Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.

  14. An Efficient Method for Assessing Water Quality Based on Bayesian Belief Networks

    Directory of Open Access Journals (Sweden)

    Khalil Shihab

    2014-08-01

    Full Text Available A new methodo logy is developed to analyse existing water quality monitoring networks. This methodology incorporates different aspects of monitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy redu ction. The work starts with a formulation of a conceptual framework for groundwater quality monitoring to represent the methodology’s context . This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of al l available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of upd ating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available

  15. Sensitivity Study on Availability of I&C Components Using Bayesian Network

    Directory of Open Access Journals (Sweden)

    Rahman Khalil Ur

    2013-01-01

    Full Text Available The objective of this study is to find out the impact of instrumentation and control (I&C components on the availability of I&C systems in terms of sensitivity analysis using Bayesian network. The analysis has been performed on I&C architecture of reactor protection system. The analysis results would be applied to develop I&C architecture which will meet the desire reliability features and save cost. RPS architecture unavailability P(x=0 and availability P(x=1 were estimated to 6.1276E-05 and 9.9994E-01 for failure (0 and perfect (1 states, respectively. The impact of I&C components on overall system risk has been studied in terms of risk achievement worth (RAW and risk reduction worth (RRW. It is found that circuit breaker failure (TCB, bi-stable processor (BP, sensor transmitter (TR, and pressure transmitter (PT have high impact on risk. The study concludes and recommends that circuit breaker bi-stable processor should be given more consideration while designing I&C architecture.

  16. Bayesian-network-based safety risk assessment for steel construction projects.

    Science.gov (United States)

    Leu, Sou-Sen; Chang, Ching-Miao

    2013-05-01

    There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites.

  17. A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

    Science.gov (United States)

    Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

    2016-06-01

    Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

  18. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. PMID:27183516

  19. Text Dependent Speaker Identification Using a Bayesian network and Mel Frequency Cepstrum Coefficient

    Directory of Open Access Journals (Sweden)

    Mohd. Manjur Alam

    2014-12-01

    Full Text Available Speaker identification is a biometric technique. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Speaker Recognition technology has recently been used in large number of commercial areas successfully such as in voice based biometrics; voice controlled appliances, security control for confidential information, remote access to computers and many more interesting areas. A speaker identification system has two phases which are the training phase and the testing phase. Feature extraction is the first step for each phase in speaker recognition. Many algorithms are suggested by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC feature has been used for designing a text dependent speaker identification system. While, in the identification phase, the existing reference templates are compared with the unknown voice input. In this thesis, a Bayesian network is used as the training/recognition algorithm which makes the final decision about the specification of the speaker by comparing unknown features to all models in the database and selecting the best matching model. i, e. the highest scored model. The speaker who obtains the highest score is selected as the target speaker.

  20. Combining Bayesian Networks and Agent Based Modeling to develop a decision-support model in Vietnam

    Science.gov (United States)

    Nong, Bao Anh; Ertsen, Maurits; Schoups, Gerrit

    2016-04-01

    Complexity and uncertainty in natural resources management have been focus themes in recent years. Within these debates, with the aim to define an approach feasible for water management practice, we are developing an integrated conceptual modeling framework for simulating decision-making processes of citizens, in our case in the Day river area, Vietnam. The model combines Bayesian Networks (BNs) and Agent-Based Modeling (ABM). BNs are able to combine both qualitative data from consultants / experts / stakeholders, and quantitative data from observations on different phenomena or outcomes from other models. Further strengths of BNs are that the relationship between variables in the system is presented in a graphical interface, and that components of uncertainty are explicitly related to their probabilistic dependencies. A disadvantage is that BNs cannot easily identify the feedback of agents in the system once changes appear. Hence, ABM was adopted to represent the reaction among stakeholders under changes. The modeling framework is developed as an attempt to gain better understanding about citizen's behavior and factors influencing their decisions in order to reduce uncertainty in the implementation of water management policy.

  1. An Emergency Unit Support System to Diagnose Chronic Heart Failure Embedded with SWRL and Bayesian Network

    Directory of Open Access Journals (Sweden)

    Baydaa Al-Hamadani

    2016-07-01

    Full Text Available In all the regions of the world, heart failure is common and on raise caused by several aetiologies. Although the development of the treatment is fast, there are still lots of cases that lose their lives in emergence sections because of slow response to treat these cases. In this paper we propose an expert system that can help the practitioners in the emergency rooms to fast diagnose the disease and advise them with the appropriate operations that should be taken to save the patient’s life. Based on the mostly binary information given to the system, Bayesian Network model was selected to support the process of reasoning under uncertain or missing information. The domain concepts and the relations between them were building by using ontology supported by the Semantic Web Rule Language to code the rules. The system was tested on 105 patients and several classification functions were tested and showed remarkable results in the accuracy and sensitivity of the system.

  2. A Bayesian Approach to Service Selection for Secondary Users in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Elaheh Homayounvala

    2015-10-01

    Full Text Available In cognitive radio networks where secondary users (SUs use the time-frequency gaps of primary users' (PUs licensed spectrum opportunistically, the experienced throughput of SUs depend not only on the traffic load of the PUs but also on the PUs' service type. Each service has its own pattern of channel usage, and if the SUs know the dominant pattern of primary channel usage, then they can make a better decision on choosing which service is better to be used at a specific time to get the best advantage of the primary channel, in terms of higher achievable throughput. However, it is difficult to inform directly SUs of PUs' dominant used services in each area, for practical reasons. This paper proposes a learning mechanism embedded in SUs to sense the primary channel for a specific length of time. This algorithm recommends the SUs upon sensing a free primary channel, to choose the best service in order to get the best performance, in terms of maximum achieved throughput and the minimum experienced delay. The proposed learning mechanism is based on a Bayesian approach that can predict the performance of a requested service for a given SU. Simulation results show that this service selection method outperforms the blind opportunistic SU service selection, significantly.

  3. Using Bayesian networks to analyze occupational stress caused by work demands: preventing stress through social support.

    Science.gov (United States)

    García-Herrero, Susana; Mariscal, M A; Gutiérrez, J M; Ritzel, Dale O

    2013-08-01

    Occupational stress is a major health hazard and a serious challenge to the effective operation of any company and represents a major problem for both individuals and organizations. Previous researches have shown that high demands (e.g. workload, emotional) combined with low resources (e.g. support, control, rewards) are associated with adverse health (e.g. psychological, physical) and organizational impacts (e.g. reduced job satisfaction, sickness absence). The objective of the present work is to create a model to analyze how social support reduces the occupational stress caused by work demands. This study used existing Spanish national data on working conditions collected by the Spanish Ministry of Labour and Immigration in 2007, where 11,054 workers were interviewed by questionnaire. A probabilistic model was built using Bayesian networks to explain the relationships between work demands and occupational stress. The model also explains how social support contributes positively to reducing stress levels. The variables studied were intellectually demanding work, overwork, workday, stress, and social support. The results show the importance of social support and of receiving help from supervisors and co-workers in preventing occupational stress. The study provides a new methodology that explains and quantifies the effects of intellectually demanding work, overwork, and workday in occupational stress. Also, the study quantifies the importance of social support to reduce occupational stress.

  4. Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

    Science.gov (United States)

    Halloran, John T; Bilmes, Jeff A; Noble, William S

    2016-08-01

    A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine learning approach. At the heart of this toolkit is a DBN for Rapid Identification (DRIP), which can be trained from collections of high-confidence peptide-spectrum matches (PSMs). DRIP's score function considers fragment ion matches using Gaussians rather than fixed fragment-ion tolerances and also finds the optimal alignment between the theoretical and observed spectrum by considering all possible alignments, up to a threshold that is controlled using a beam-pruning algorithm. This function not only yields state-of-the art database search accuracy but also can be used to generate features that significantly boost the performance of the Percolator postprocessor. The DRIP software is built upon a general purpose DBN toolkit (GMTK), thereby allowing a wide variety of options for user-specific inference tasks as well as facilitating easy modifications to the DRIP model in future work. DRIP is implemented in Python and C++ and is available under Apache license at http://melodi-lab.github.io/dripToolkit . PMID:27397138

  5. User-Adapted Recommendation of Content on Mobile Devices Using Bayesian Networks

    Science.gov (United States)

    Iwasaki, Hirotoshi; Mizuno, Nobuhiro; Hara, Kousuke; Motomura, Yoichi

    Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

  6. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

    Science.gov (United States)

    Mani-Varnosfaderani, Ahmad; Kanginejad, Atefeh; Gilany, Kambiz; Valadkhani, Abolfazl

    2016-10-12

    The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets. PMID:27662759

  7. Combining Volcano Monitoring Timeseries Analyses with Bayesian Belief Networks to Update Hazard Forecast Estimates

    Science.gov (United States)

    Odbert, Henry; Hincks, Thea; Aspinall, Willy

    2015-04-01

    Volcanic hazard assessments must combine information about the physical processes of hazardous phenomena with observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We show how our method

  8. Competition and network expansion in the electricity market: an analysis of producers' strategic behavior

    International Nuclear Information System (INIS)

    Expansion of the transmission capacity is ODe of the most efficient means of enhancing competition in electricity markets. The issue is extremely relevant far the Italian electricity market, where competition in generation has not Jet been achieved. In order to study the effects on competition of a network expansion project, a description of the influence of transmission constraints on the strategic behavior of generators is necessary. The problem was addressed in the literature far a limited number only of simplified models. This work presents an original methodology (MIXEL), based on non-cooperative game theory, far the study of a rather broad set of electricity market models. The case study illustrated in this article, shows that the effects on competition of an expansion of the network is not always positive (or as positive) as expected, given the cases illustrated in the literature. The effects on competition vary with the market structure, the ratio between demand and supply and, above all, the size of the transmission capacity expansion. For these reasons, policy provisions mandating or encouraging expansion of the transmission system with the objective of promoting competition, should take into careful consideration the underlying market structure; in a similar way, provision encouraging divestiture of generation capacity should take into account the effects of the network

  9. Bayesian Inference Reveals Host-Specific Contributions to the Epidemic Expansion of Influenza A H5N1.

    Science.gov (United States)

    Trovão, Nídia Sequeira; Suchard, Marc A; Baele, Guy; Gilbert, Marius; Lemey, Philippe

    2015-12-01

    Since its first isolation in 1996 in Guangdong, China, the highly pathogenic avian influenza virus (HPAIV) H5N1 has circulated in avian hosts for almost two decades and spread to more than 60 countries worldwide. The role of different avian hosts and the domestic-wild bird interface has been critical in shaping the complex HPAIV H5N1 disease ecology, but remains difficult to ascertain. To shed light on the large-scale H5N1 transmission patterns and disentangle the contributions of different avian hosts on the tempo and mode of HPAIV H5N1 dispersal, we apply Bayesian evolutionary inference techniques to comprehensive sets of hemagglutinin and neuraminidase gene sequences sampled between 1996 and 2011 throughout Asia and Russia. Our analyses demonstrate that the large-scale H5N1 transmission dynamics are structured according to different avian flyways, and that the incursion of the Central Asian flyway specifically was driven by Anatidae hosts coinciding with rapid rate of spread and an epidemic wavefront acceleration. This also resulted in long-distance dispersal that is likely to be explained by wild bird migration. We identify a significant degree of asymmetry in the large-scale transmission dynamics between Anatidae and Phasianidae, with the latter largely representing poultry as an evolutionary sink. A joint analysis of host dynamics and continuous spatial diffusion demonstrates that the rate of viral dispersal and host diffusivity is significantly higher for Anatidae compared with Phasianidae. These findings complement risk modeling studies and satellite tracking of wild birds in demonstrating a continental-scale structuring into areas of H5N1 persistence that are connected through migratory waterfowl.

  10. 75 FR 6560 - Financial Crimes Enforcement Network; Expansion of Special Information Sharing Procedures To...

    Science.gov (United States)

    2010-02-10

    ... importance of providing financial institutions information to assist them in identifying and reporting... 31 CFR Part 103 RIN 1506-AB04 Financial Crimes Enforcement Network; Expansion of Special Information... facilitation of information sharing among governmental entities and financial institutions for the purpose...

  11. Altered network connectivity in frontotemporal dementia with C9orf72 hexanucleotide repeat expansion.

    Science.gov (United States)

    Lee, Suzee E; Khazenzon, Anna M; Trujillo, Andrew J; Guo, Christine C; Yokoyama, Jennifer S; Sha, Sharon J; Takada, Leonel T; Karydas, Anna M; Block, Nikolas R; Coppola, Giovanni; Pribadi, Mochtar; Geschwind, Daniel H; Rademakers, Rosa; Fong, Jamie C; Weiner, Michael W; Boxer, Adam L; Kramer, Joel H; Rosen, Howard J; Miller, Bruce L; Seeley, William W

    2014-11-01

    Hexanucleotide repeat expansion in C9orf72 represents the most common genetic cause of familial and sporadic behavioural variant frontotemporal dementia. Previous studies show that some C9orf72 carriers with behavioural variant frontotemporal dementia exhibit distinctive atrophy patterns whereas others show mild or undetectable atrophy despite severe behavioural impairment. To explore this observation, we examined intrinsic connectivity network integrity in patients with or without the C9orf72 expansion. We studied 28 patients with behavioural variant frontotemporal dementia, including 14 C9orf72 mutation carriers (age 58.3 ± 7.7 years, four females) and 14 non-carriers (age 60.8 ± 6.9 years, four females), and 14 age- and sex-matched healthy controls. Both patient groups included five patients with comorbid motor neuron disease. Neuropsychological data, structural brain magnetic resonance imaging, and task-free functional magnetic resonance imaging were obtained. Voxel-based morphometry delineated atrophy patterns, and seed-based intrinsic connectivity analyses enabled group comparisons of the salience, sensorimotor, and default mode networks. Single-patient analyses were used to explore network imaging as a potential biomarker. Despite contrasting atrophy patterns in C9orf72 carriers versus non-carriers, patient groups showed topographically similar connectivity reductions in the salience and sensorimotor networks. Patients without C9orf72 expansions exhibited increases in default mode network connectivity compared to controls and mutation carriers. Across all patients, behavioural symptom severity correlated with diminished salience network connectivity and heightened default mode network connectivity. In C9orf72 carriers, salience network connectivity reduction correlated with atrophy in the left medial pulvinar thalamic nucleus, and this region further showed diminished connectivity with key salience network hubs. Single-patient analyses revealed salience

  12. Bayesian prediction and adaptive sampling algorithms for mobile sensor networks online environmental field reconstruction in space and time

    CERN Document Server

    Xu, Yunfei; Dass, Sarat; Maiti, Tapabrata

    2016-01-01

    This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive di...

  13. Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

    Science.gov (United States)

    Toroody, Ahmad Bahoo; Abaiee, Mohammad Mahdi; Gholamnia, Reza; Ketabdari, Mohammad Javad

    2016-09-01

    Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.

  14. Epistemic-Based Investigation of the Probability of Hazard Scenarios Using Bayesian Network for the Lifting Operation of Floating Objects

    Institute of Scientific and Technical Information of China (English)

    Ahmad Bahoo Toroody; Mohammad Mahdi Abaiee; Reza Gholamnia; Mohammad Javad Ketabdari

    2016-01-01

    Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types:the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.

  15. Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

    Science.gov (United States)

    Toroody, Ahmad Bahoo; Abaiee, Mohammad Mahdi; Gholamnia, Reza; Ketabdari, Mohammad Javad

    2016-07-01

    Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.

  16. Using Bayesian networks to assess the vulnerability of Hawaiian terrestrial biota to climate change

    Science.gov (United States)

    Fortini, L.; Jacobi, J.; Price, J.; Vorsino, A.; Paxton, E.; Amidon, F.; 'Ohukani'ohi'a Gon, S., III; Koob, G.; Brink, K.; Burgett, J.; Miller, S.

    2012-12-01

    As the effects of climate change on individual species become increasingly apparent, there is a clear need for effective adaptation planning to prevent an increase in species extinctions worldwide. Given the limited understanding of species responses to climate change, vulnerability assessments and species distribution models (SDMs) have been two common tools used to jump-start climate change adaptation efforts. However, although these two approaches generally serve the same purpose of understanding species future responses to climate change, they have rarely mixed. In collaboration with research and management partners from federal, state and non-profit organizations, we are conducting a climate change vulnerability assessment for hundreds of plant and forest bird species of the Main Hawaiian Islands. This assessment is the first to comprehensively consider the potential threats of climate change to a significant portion of Hawaii's fauna and flora (over one thousand species considered) and thus fills a critical gap defined by natural resource scientists and managers in the region. We have devised a flexible approach that effectively integrates species distribution models into a vulnerability assessment framework that can be easily updated with improved models and data. This tailors our assessment approach to the Pacific Island reality of often limited and fragmented information on species and large future climate uncertainties, This vulnerability assessment is based on a Bayesian network-based approach that integrates multiple landscape (e.g., topographic diversity, dispersal barriers), species trait (e.g., generation length, fecundity) and expert-knowledge based information (e.g., capacity to colonize restored habitat) relevant to long-term persistence of species under climate change. Our presentation will highlight some of the results from our assessment but will mainly focus on the utility of the flexible approach we have developed and its potential

  17. Using Bayesian Belief Network (BBN) modelling for rapid source term prediction. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Knochenhauer, M.; Swaling, V.H.; Dedda, F.D.; Hansson, F.; Sjoekvist, S.; Sunnegaerd, K. [Lloyd' s Register Consulting AB, Sundbyberg (Sweden)

    2013-10-15

    The project presented in this report deals with a number of complex issues related to the development of a tool for rapid source term prediction (RASTEP), based on a plant model represented as a Bayesian belief network (BBN) and a source term module which is used for assigning relevant source terms to BBN end states. Thus, RASTEP uses a BBN to model severe accident progression in a nuclear power plant in combination with pre-calculated source terms (i.e., amount, composition, timing, and release path of released radio-nuclides). The output is a set of possible source terms with associated probabilities. One major issue has been associated with the integration of probabilistic and deterministic analyses are addressed, dealing with the challenge of making the source term determination flexible enough to give reliable and valid output throughout the accident scenario. The potential for connecting RASTEP to a fast running source term prediction code has been explored, as well as alternative ways of improving the deterministic connections of the tool. As part of the investigation, a comparison of two deterministic severe accident analysis codes has been performed. A second important task has been to develop a general method where experts' beliefs can be included in a systematic way when defining the conditional probability tables (CPTs) in the BBN. The proposed method includes expert judgement in a systematic way when defining the CPTs of a BBN. Using this iterative method results in a reliable BBN even though expert judgements, with their associated uncertainties, have been used. It also simplifies verification and validation of the considerable amounts of quantitative data included in a BBN. (Author)

  18. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

    Science.gov (United States)

    Thomsen, Nanna I.; Binning, Philip J.; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul L.; Troldborg, Mads

    2016-05-01

    A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

  19. Bayesian belief networks for human reliability analysis: A review of applications and gaps

    International Nuclear Information System (INIS)

    The use of Bayesian Belief Networks (BBNs) in risk analysis (and in particular Human Reliability Analysis, HRA) is fostered by a number of features, attractive in fields with shortage of data and consequent reliance on subjective judgments: the intuitive graphical representation, the possibility of combining diverse sources of information, the use the probabilistic framework to characterize uncertainties. In HRA, BBN applications are steadily increasing, each emphasizing a different BBN feature or a different HRA aspect to improve. This paper aims at a critical review of these features as well as at suggesting research needs. Five groups of BBN applications are analysed: modelling of organizational factors, analysis of the relationships among failure influencing factors, BBN-based extensions of existing HRA methods, dependency assessment among human failure events, assessment of situation awareness. Further, the paper analyses the process for building BBNs and in particular how expert judgment is used in the assessment of the BBN conditional probability distributions. The gaps identified in the review suggest the need for establishing more systematic frameworks to integrate the different sources of information relevant for HRA (cognitive models, empirical data, and expert judgment) and to investigate algorithms to avoid elicitation of many relationships via expert judgment. - Highlights: • We analyze BBN uses for HRA applications; but some conclusions can be generalized. • Special review focus on BBN building approaches, key for model acceptance. • Gaps relate to the transparency of the BBN building and quantification phases. • Need for more systematic framework to integrate different sources of information. • Need of ways to avoid elicitation of many relationships via expert judgment

  20. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites.

    Science.gov (United States)

    Thomsen, Nanna I; Binning, Philip J; McKnight, Ursula S; Tuxen, Nina; Bjerg, Poul L; Troldborg, Mads

    2016-05-01

    A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

  1. A decision-making framework to model environmental flow requirements in oasis areas using Bayesian networks

    Science.gov (United States)

    Xue, Jie; Gui, Dongwei; Zhao, Ying; Lei, Jiaqiang; Zeng, Fanjiang; Feng, Xinlong; Mao, Donglei; Shareef, Muhammad

    2016-09-01

    The competition for water resources between agricultural and natural oasis ecosystems has become an increasingly serious problem in oasis areas worldwide. Recently, the intensive extension of oasis farmland has led to excessive exploitation of water discharge, and consequently has resulted in a lack of water supply in natural oasis. To coordinate the conflicts, this paper provides a decision-making framework for modeling environmental flows in oasis areas using Bayesian networks (BNs). Three components are included in the framework: (1) assessment of agricultural economic loss due to meeting environmental flow requirements; (2) decision-making analysis using BNs; and (3) environmental flow decision-making under different water management scenarios. The decision-making criterion is determined based on intersection point analysis between the probability of large-level total agro-economic loss and the ratio of total to maximum agro-economic output by satisfying environmental flows. An application in the Qira oasis area of the Tarim Basin, Northwest China indicates that BNs can model environmental flow decision-making associated with agricultural economic loss effectively, as a powerful tool to coordinate water-use conflicts. In the case study, the environmental flow requirement is determined as 50.24%, 49.71% and 48.73% of the natural river flow in wet, normal and dry years, respectively. Without further agricultural economic loss, 1.93%, 0.66% and 0.43% of more river discharge can be allocated to eco-environmental water demands under the combined strategy in wet, normal and dry years, respectively. This work provides a valuable reference for environmental flow decision-making in any oasis area worldwide.

  2. A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites.

    Science.gov (United States)

    Thomsen, Nanna I; Binning, Philip J; McKnight, Ursula S; Tuxen, Nina; Bjerg, Poul L; Troldborg, Mads

    2016-05-01

    A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

  3. An Investigation Into Bayesian Networks for Modeling National Ignition Facility Capsule Implosions

    Energy Technology Data Exchange (ETDEWEB)

    Mitrani, J

    2008-08-18

    Bayesian networks (BN) are an excellent tool for modeling uncertainties in systems with several interdependent variables. A BN is a directed acyclic graph, and consists of a structure, or the set of directional links between variables that depend on other variables, and conditional probabilities (CP) for each variable. In this project, we apply BN's to understand uncertainties in NIF ignition experiments. One can represent various physical properties of National Ignition Facility (NIF) capsule implosions as variables in a BN. A dataset containing simulations of NIF capsule implosions was provided. The dataset was generated from a radiation hydrodynamics code, and it contained 120 simulations of 16 variables. Relevant knowledge about the physics of NIF capsule implosions and greedy search algorithms were used to search for hypothetical structures for a BN. Our preliminary results found 6 links between variables in the dataset. However, we thought there should have been more links between the dataset variables based on the physics of NIF capsule implosions. Important reasons for the paucity of links are the relatively small size of the dataset, and the sampling of the values for dataset variables. Another factor that might have caused the paucity of links is the fact that in the dataset, 20% of the simulations represented successful fusion, and 80% didn't, (simulations of unsuccessful fusion are useful for measuring certain diagnostics) which skewed the distributions of several variables, and possibly reduced the number of links. Nevertheless, by illustrating the interdependencies and conditional probabilities of several parameters and diagnostics, an accurate and complete BN built from an appropriate simulation set would provide uncertainty quantification for NIF capsule implosions.

  4. Analysis of prognostic factors for survival after hepatectomy for hepatocellular carcinoma based on a bayesian network.

    Directory of Open Access Journals (Sweden)

    Zhi-Qiang Cai

    Full Text Available The prognosis of hepatocellular carcinoma (HCC after hepatectomy involves many factors. Previous studies have evaluated the separate influences of single factors; few have considered the combined influence of various factors. This paper combines the Bayesian network (BN with importance measures to identify key factors that have significant effects on survival time.A dataset of 299 patients with HCC after hepatectomy was studied to establish a BN using a tree-augmented naïve Bayes algorithm that could mine relationships between factors. The composite importance measure was applied to rank the impact of factors on survival time.124 patients (>10 months and 77 patients (≤10 months were correctly classified. The accuracy of BN model was 67.2%. For patients with long survival time (>10 months, the true-positive rate of the model was 83.22% and the false-positive rate was 48.67%. According to the model, the preoperative alpha fetoprotein (AFP level and postoperative performance of transcatheter arterial chemoembolization (TACE were independent factors for survival of HCC patients. The grade of preoperative liver function reflected the tendency for postoperative complications. Intraoperative blood loss, tumor size, portal vein tumor thrombosis (PVTT, time of clamping the porta hepatis, tumor number, operative method, and metastasis were dependent variables in survival time prediction. PVTT was considered the most significant for the prognosis of survival time.Using the BN and importance measures, PVTT was identified as the most significant predictor of survival time for patients with HCC after hepatectomy.

  5. A Bayesian Network Approach for Offshore Risk Analysis Through Linguistic Variables

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks (BNs). In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the α-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading (FPSO) unit and the authorised vessels due to human elements during operation is used to illustrate the application of the proposed model.

  6. The influence of interference networks in QoS parameters in a WLAN 802.11g: a Bayesian approach

    Science.gov (United States)

    Araújo, Jasmine P. L.; Rodrigues, Josiane C.; Fraiha, Simone G. C.; Gomes, Hermínio S.; Reis, Jacklyn; Vijaykumar, Nandamudi L.; Cavalcante, Gervásio P. S.; Francês, Carlos R. L.

    2007-09-01

    In spite of the significant increase of the use of Wireless Local Area Network (WLAN) experienced in the last years, design aspects and capacity planning of the network are still systematically neglected during the network implementation. For instance, to determine the location of the access point (AP), important factors of the environment are not considered in the project. These factors become more important when several APs are installed, sometimes without a frequency planning, to cover a unique building. Faults such as these can cause interference among the cells generated by each AP. Therefore, the network will not obtain the QoS patterns required for each service. This paper proposes a strategy to determine how much a given network can affect the QoS parameters of another network, by interference. In order to achieve this, a measurement campaign was carried out in two stages: firstly with a single AP and later with two APs using the same channel. A VoIP application was used in the experiment and a protocol analyzer collected the QoS metrics. In each stage 46 points were measured , that are insufficient for statistically characterize the environment. For expanding this data, an Artificial Neural Network (ANN) was used. After the measurement, an analysis of the results and a set of inferences were made by using Bayesian Networks, whose inputs were the experimental data, i.e., QoS metrics like throughput, delay, jitter, packet loss, PMOS and physical metrics like power and distance.

  7. Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network.

    Science.gov (United States)

    Murakami, Yohei; Takada, Shoji

    2013-01-01

    When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC) is a useful method. Conventional MCMC needs likelihood to evaluate a posterior distribution of acceptable parameters, while the approximate Bayesian computation (ABC) MCMC evaluates posterior distribution with use of qualitative fitness measure. However, none of these algorithms can deal with mixture of quantitative, i.e., likelihood, and qualitative fitness measures simultaneously. Here, to deal with this mixture, we formulated Bayesian formula for hybrid fitness measures (HFM). Then we implemented it to MCMC (MCMC-HFM). We tested MCMC-HFM first for a kinetic toy model with a positive feedback. Inferring kinetic parameters mainly related to the positive feedback, we found that MCMC-HFM reliably infer them using both qualitative and quantitative fitness measures. Then, we applied the MCMC-HFM to an apoptosis signal transduction network previously proposed. For kinetic parameters related to implicit positive feedbacks, which are important for bistability and irreversibility of the output, the MCMC-HFM reliably inferred these kinetic parameters. In particular, some kinetic parameters that have experimental estimates were inferred without using these data and the results were consistent with experiments. Moreover, for some parameters, the mixed use of quantitative and qualitative fitness measures narrowed down the acceptable range of parameters.

  8. Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies

    Directory of Open Access Journals (Sweden)

    Karacaören Burak

    2011-05-01

    Full Text Available Abstract Background It has been shown that if genetic relationships among individuals are not taken into account for genome wide association studies, this may lead to false positives. To address this problem, we used Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification analyses. To account for linkage disequilibrium among the significant markers, principal components loadings obtained from top markers can be included as covariates. Estimation of Bayesian networks may also be useful to investigate linkage disequilibrium among SNPs and their relation with environmental variables. For the quantitative trait we first estimated residuals while taking polygenic effects into account. We then used a single SNP approach to detect the most significant SNPs based on the residuals and applied principal component regression to take linkage disequilibrium among these SNPs into account. For the categorical trait we used principal component stratification methodology to account for background effects. For correction of linkage disequilibrium we used principal component logit regression. Bayesian networks were estimated to investigate relationship among SNPs. Results Using the Genome-wide Rapid Association using Mixed Model and Regression and principal component stratification approach we detected around 100 significant SNPs for the quantitative trait (p Conclusions GRAMMAR could efficiently incorporate the information regarding random genetic effects. Principal component stratification should be cautiously used with stringent multiple hypothesis testing correction to correct for ancestral stratification and association analyses for binary traits when there are systematic genetic effects such as half sib family structures. Bayesian networks are useful to investigate relationships among SNPs and environmental variables.

  9. Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM

    International Nuclear Information System (INIS)

    Highlights: • Dynamic Bayesian network is used to diagnose and predict accident progress in HTR-PM. • Dynamic Bayesian network model of HTR-PM is built based on detailed system analysis. • LOCA Simulations validate the above model even if part monitors are lost or false. - Abstract: The first high-temperature-reactor pebble-bed demonstration module (HTR-PM) is under construction currently in China. At the same time, development of a system that is used to support nuclear emergency response is in progress. The supporting system is expected to complete two tasks. The first one is diagnostics of the fault in the reactor based on abnormal sensor measurements obtained. The second one is prognostic of the accident progression based on sensor measurements obtained and operator actions. Both tasks will provide valuable guidance for emergency staff to take appropriate protective actions. Traditional method for the two tasks relies heavily on expert judgment, and has been proven to be inappropriate in some cases, such as Three Mile Island accident. To better perform the two tasks, dynamic Bayesian networks (DBN) is introduced in this paper and a pilot study based on the approach is carried out. DBN is advantageous in representing complex dynamic systems and taking full consideration of evidences obtained to perform diagnostics and prognostics. Pearl's loopy belief propagation (LBP) algorithm is recommended for diagnostics and prognostics in DBN. The DBN model of HTR-PM is created based on detailed system analysis and accident progression analysis. A small break loss of coolant accident (SBLOCA) is selected to illustrate the application of the DBN model of HTR-PM in fault diagnostics (FD) and accident progression prognostics (APP). Several advantages of DBN approach compared with other techniques are discussed. The pilot study lays the foundation for developing the nuclear emergency response supporting system (NERSS) for HTR-PM

  10. Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression.

    Science.gov (United States)

    Morrissey, Edward R; Juárez, Miguel A; Denby, Katherine J; Burroughs, Nigel J

    2011-10-01

    We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

  11. Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

    OpenAIRE

    Ozgur Baskan

    2013-01-01

    During the last two decades, Continuous Network Design Problem (CNDP) has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use ava...

  12. A Novel Prediction Algorithm of DR Position Error Based on Bayesian Regularization Back-propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Li Honglian

    2013-07-01

    Full Text Available It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS during GPS outages, a novel prediction algorithm of dead reckon (DR position error is put forward, which based on Bayesian regularization back-propagation (BRBP neural network. DR, GPS position data are first de-noised and compared at different stationary wavelet transformation (SWT decomposition level, and DR position error data are acquired after the SWT coefficients differences are reconstructed. A neural network to mimic position error property is trained with back-propagation algorithm, and the algorithm is improved for improving its generalization by Bayesian regularization theory. During GPS outages, the established prediction algorithm predictes DR position errors, and provides precise position for VNS through DR position error data updating DR position data. The simulation results show the positioning precision of the BRBP algorithm is best among the presented prediction algorithms such as simple DR and adaptive linear network, and a precise mathematical model of navigation sensors isn’t established.

  13. Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

    Directory of Open Access Journals (Sweden)

    Gang Li

    2015-10-01

    Full Text Available As a novel recurrent neural network (RNN, an echo state network (ESN that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN is proposed for short-term power production forecasting of small hydropower (SHP plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.

  14. A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs

    DEFF Research Database (Denmark)

    Jensen, Kasper Lynge; Toftum, Jørn; Friis-Hansen, Peter

    2009-01-01

    building design. In this paper, focus will be on the effects of temperature on mental performance and not on other indoor climate factors. A total economic comparison of six different building designs, four located in northern Europe and two in Los Angeles, USA, was performed. The results indicate...... that investments in improved indoor thermal conditions can be justified economically in most cases. The Bayesian Network provides a reliable platform using probabilities for modelling the complexity while estimating the effect of indoor climate factors on human beings, due to the different ways in which humans...

  15. Application of Bayesian Neural Networks to Energy Reconstruction in EAS Experiments for ground-based TeV Astrophysics

    CERN Document Server

    Bai, Ying; Lan, JieQin; Gao, WeiWei

    2016-01-01

    A toy detector array has been designed to simulate the detection of cosmic rays in Extended Air Shower(EAS) Experiments for ground-based TeV Astrophysics. The primary energies of protons from the Monte-Carlo simulation have been reconstructed by the algorithm of Bayesian neural networks (BNNs) and a standard method like the LHAASO experiment\\cite{lhaaso-ma}, respectively. The result of the energy reconstruction using BNNs has been compared with the one using the standard method. Compared to the standard method, the energy resolutions are significantly improved using BNNs. And the improvement is more obvious for the high energy protons than the low energy ones.

  16. Application of Bayesian neural networks to energy reconstruction in EAS experiments for ground-based TeV astrophysics

    Science.gov (United States)

    Bai, Y.; Xu, Y.; Pan, J.; Lan, J. Q.; Gao, W. W.

    2016-07-01

    A toy detector array is designed to detect a shower generated by the interaction between a TeV cosmic ray and the atmosphere. In the present paper, the primary energies of showers detected by the detector array are reconstructed with the algorithm of Bayesian neural networks (BNNs) and a standard method like the LHAASO experiment [1], respectively. Compared to the standard method, the energy resolutions are significantly improved using the BNNs. And the improvement is more obvious for the high energy showers than the low energy ones.

  17. Bayesian inference on dynamic linear models of day-to-day origin-destination flows in transportation networks

    OpenAIRE

    Pitombeira-Neto, Anselmo Ramalho; Loureiro, Carlos Felipe Grangeiro; Carvalho, Luis Eduardo

    2016-01-01

    Estimation of origin-destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLM's are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships b...

  18. Market-based valuation of transmission network expansion. A heuristic application in GB

    International Nuclear Information System (INIS)

    Transmission investments are currently needed to meet an increasing electricity demand while enhancing security of supply, promoting competition, and integrating renewable energy. A key issue when assessing the benefits from an expanded grid concerns the valuation of the uncertain cash flows that result from the expansion. We develop a valuation model which combines optimization techniques, Monte Carlo simulation over the expansion project lifetime, and market data from futures contracts on commodities. Our multivariate stochastic model accounts for uncertain dynamics on both the demand side and the supply side. It can assist decision makers when confronted with challenging strategic portfolio choices regarding generation and transmission infrastructures. Though our ultimate goal is to develop a generalizable model, to demonstrate the model by example we consider a simplified network with two nodes. We then focus on the proposed Western HVDC subsea link between England/Wales and Scotland, and simulate the whole distribution of effects on major system variables over its useful life. -- Highlights: ► We assess the expected profits from electricity network expansions. ► Load, renewable generation, fuel prices, and emission allowance price are stochastic. ► We infer future behavior of economic variables from markets (risk-neutral valuation). ► We consider the performance of the time-varying system over the project lifetime. ► We simulate whole distribution of effects of the expansion project on key variables.

  19. Rainfall-Runoff Forecast and Model Parameter Estimation: a Dynamic Bayesian Networks Approach

    Science.gov (United States)

    Canon Barriga, J. E.; Morillo Leon, F. C.

    2013-12-01

    The suggested climate-driven non-stationarities and intrinsic uncertainties of hydrological processes such as precipitation (P) and runoff (R), represent a fruitful context to develop new methods that may be able to detect parametric variations in time series and incorporate them into forecasts. In this research, we developed a method to forecast runoff from precipitation time series based on Dynamic Bayesian Networks (DBN). The purpose of the research was to determine an appropriate structure of the DBN and the optimal lengths of hydrological time series required to establish statistical parameters (i.e., first two moments) of P and optimal fits of forecasted R at daily and weekly intervals. A DBN can be briefly interpreted as a set of nodes (representing conditional probabilistic variables) connected by arrows that establish a causal, time-oriented, relationship among them. A DBN is defined by two components: a static network (structure) and a transition probability matrix between consecutive stages. Similarly to neural networks, DBN must be trained in order to learn about the subjacent process and make useful predictions. To determine the ability of the DBN to forecast R from P we initially generated long synthetic P series and run a deterministic model (HEC-HMS) to generate R. The DBN were then trained with different lengths of these synthetic series to forecast R (using smoothing and filtering methods). Two structures were considered: 1) DBN with P(t), P(t-1) and R(t-1) and 2) DBN with P(t), P(t-1), R(t-1) and ΔR=[R(t-1)-R(t-2)]. Both smoothing and filtering methods were appropriate to make predictions on a daily and weekly basis (filtration performing better). Setting the complexity (number of states of the random variables) in a DBN proves to be a critical issue, since an increase in the number of states, which implies larger training sets, does not always mean an improvement in the prediction. We found that acceptable results could be obtained from DBN

  20. From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support

    Science.gov (United States)

    Constantinou, Anthony Costa; Fenton, Norman; Marsh, William; Radlinski, Lukasz

    2016-01-01

    Objectives 1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; 2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; 3) To ensure the BN model can be used for interventional analysis; 4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. Method The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. Results When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. Conclusions This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of

  1. Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

    International Nuclear Information System (INIS)

    Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0

  2. Linking hydrology, ecosystem function, and livelihood sustainability in African papyrus wetlands using a Bayesian Network Model

    Science.gov (United States)

    van Dam, A.; Gettel, G. M.; Kipkemboi, J.; Rahman, M. M.

    2011-12-01

    Papyrus wetlands in East Africa provide ecosystem services supporting the livelihoods of millions but are rapidly degrading due to economic development. For ecosystem conservation, an integrated understanding of the natural and social processes driving ecosystem change is needed. This research focuses on integrating the causal relationships between hydrology, ecosystem function, and livelihood sustainability in Nyando wetland, western Kenya. Livelihood sustainability is based on ecosystem services that include plant and animal harvest for building material and food, conversion of wetlands to crop and grazing land, water supply, and water quality regulation. Specific objectives were: to integrate studies of hydrology, ecology, and livelihood activities using a Bayesian Network (BN) model and include stakeholder involvement in model development. The BN model (Netica 4.16) had 35 nodes with seven decision nodes describing demography, economy, papyrus market, and rainfall, and two target nodes describing ecosystem function (defined by groundwater recharge, nutrient and sediment retention, and biodiversity) and livelihood sustainability (drinking water supply, crop production, livestock production, and papyrus yield). The conditional probability tables were populated using results of ecohydrological and socio-economic field work and consultations with stakeholders. The model was evaluated for an average year with decision node probabilities set according to data from research, expert opinion, and stakeholders' views. Then, scenarios for dry and wet seasons and for economic development (low population growth and unemployment) and policy development (more awareness of wetland value) were evaluated. In an average year, the probability for maintaining a "good" level of sediment and nutrient retention functions, groundwater recharge, and biodiversity was about 60%. ("Good" is defined by expert opinion based on ongoing field research.) In the dry season, the probability was

  3. Integration of Geophysical Data into Structural Geological Modelling through Bayesian Networks

    Science.gov (United States)

    de la Varga, Miguel; Wellmann, Florian; Murdie, Ruth

    2016-04-01

    Structural geological models are widely used to represent the spatial distribution of relevant geological features. Several techniques exist to construct these models on the basis of different assumptions and different types of geological observations (e.g. Jessell et al., 2014). However, two problems are prevalent when constructing models: (i) observations and assumptions, and therefore also the constructed model, are subject to uncertainties, and (ii) additional information, such as geophysical data, is often available, but cannot be considered directly in the geological modelling step. In our work, we propose the integration of all available data into a Bayesian network including the generation of the implicit geological method by means of interpolation functions (Mallet, 1992; Lajaunie et al., 1997; Mallet, 2004; Carr et al., 2001; Hillier et al., 2014). As a result, we are able to increase the certainty of the resultant models as well as potentially learn features of our regional geology through data mining and information theory techniques. MCMC methods are used in order to optimize computational time and assure the validity of the results. Here, we apply the aforementioned concepts in a 3-D model of the Sandstone Greenstone Belt in the Archean Yilgarn Craton in Western Australia. The example given, defines the uncertainty in the thickness of greenstone as limited by Bouguer anomaly and the internal structure of the greenstone as limited by the magnetic signature of a banded iron formation. The incorporation of the additional data and specially the gravity provides an important reduction of the possible outcomes and therefore the overall uncertainty. References Carr, C. J., K. R. Beatson, B. J. Cherrie, J. T. Mitchell, R. W. Fright, C. B. McCallum, and R. T. Evans, 2001, Reconstruction and representation of 3D objects with radial basis functions: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 67-76. Jessell, M

  4. Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sangkyu, E-mail: sangkyu.lee@mail.mcgill.ca; Ybarra, Norma; Jeyaseelan, Krishinima; Seuntjens, Jan; El Naqa, Issam [Medical Physics Unit, McGill University, Montreal, Quebec H3G1A4 (Canada); Faria, Sergio; Kopek, Neil; Brisebois, Pascale [Department of Radiation Oncology, Montreal General Hospital, Montreal, H3G1A4 (Canada); Bradley, Jeffrey D.; Robinson, Clifford [Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri 63110 (United States)

    2015-05-15

    Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cell lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0

  5. A Bayesian Belief Network approach to assess the potential of non wood forest products for small scale forest owners

    Science.gov (United States)

    Vacik, Harald; Huber, Patrick; Hujala, Teppo; Kurtilla, Mikko; Wolfslehner, Bernhard

    2015-04-01

    It is an integral element of the European understanding of sustainable forest management to foster the design and marketing of forest products, non-wood forest products (NWFPs) and services that go beyond the production of timber. Despite the relevance of NWFPs in Europe, forest management and planning methods have been traditionally tailored towards wood and wood products, because most forest management models and silviculture techniques were developed to ensure a sustained production of timber. Although several approaches exist which explicitly consider NWFPs as management objectives in forest planning, specific models are needed for the assessment of their production potential in different environmental contexts and for different management regimes. Empirical data supporting a comprehensive assessment of the potential of NWFPs are rare, thus making development of statistical models particularly problematic. However, the complex causal relationships between the sustained production of NWFPs, the available ecological resources, as well as the organizational and the market potential of forest management regimes are well suited for knowledge-based expert models. Bayesian belief networks (BBNs) are a kind of probabilistic graphical model that have become very popular to practitioners and scientists mainly due to the powerful probability theory involved, which makes BBNs suitable to deal with a wide range of environmental problems. In this contribution we present the development of a Bayesian belief network to assess the potential of NWFPs for small scale forest owners. A three stage iterative process with stakeholder and expert participation was used to develop the Bayesian Network within the frame of the StarTree Project. The group of participants varied in the stages of the modelling process. A core team, consisting of one technical expert and two domain experts was responsible for the entire modelling process as well as for the first prototype of the network

  6. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks

    DEFF Research Database (Denmark)

    Skare, Øivind; Møller, Jesper; Vedel Jensen, Eva B.

    A model for an inhomogeneous Poisson process with high intensity near the edges of a Voronoi tessellation in 2D or 3D is proposed. The model is analysed in a Bayesian setting with priors on nuclei of the Voronoi tessellation and other model parameters. An MCMC algorithm is constructed to sample...

  7. Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks

    DEFF Research Database (Denmark)

    Skare, Øivind; Møller, Jesper; Jensen, Eva B. Vedel

    2007-01-01

    A model for an inhomogeneous Poisson process with high intensity near the edges of a Voronoi tessellation in 2D or 3D is proposed. The model is analysed in a Bayesian setting with priors on nuclei of the Voronoi tessellation and other model parameters. An MCMC algorithm is constructed to sample...

  8. Using of bayesian networks to estimate the probability of "NATECH" scenario occurrence

    Science.gov (United States)

    Dobes, Pavel; Dlabka, Jakub; Jelšovská, Katarína; Polorecká, Mária; Baudišová, Barbora; Danihelka, Pavel

    2015-04-01

    In the twentieth century, implementation of Bayesian statistics and probability was not much used (may be it wasn't a preferred approach) in the area of natural and industrial risk analysis and management. Neither it was used within analysis of so called NATECH accidents (chemical accidents triggered by natural events, such as e.g. earthquakes, floods, lightning etc.; ref. E. Krausmann, 2011, doi:10.5194/nhess-11-921-2011). Main role, from the beginning, played here so called "classical" frequentist probability (ref. Neyman, 1937), which rely up to now especially on the right/false results of experiments and monitoring and didn't enable to count on expert's beliefs, expectations and judgements (which is, on the other hand, one of the once again well known pillars of Bayessian approach to probability). In the last 20 or 30 years, there is possible to observe, through publications and conferences, the Renaissance of Baysssian statistics into many scientific disciplines (also into various branches of geosciences). The necessity of a certain level of trust in expert judgment within risk analysis is back? After several decades of development on this field, it could be proposed following hypothesis (to be checked): "We couldn't estimate probabilities of complex crisis situations and their TOP events (many NATECH events could be classified as crisis situations or emergencies), only by classical frequentist approach, but also by using of Bayessian approach (i.e. with help of prestaged Bayessian Network including expert belief and expectation as well as classical frequentist inputs). Because - there is not always enough quantitative information from monitoring of historical emergencies, there could be several dependant or independant variables necessary to consider and in generally - every emergency situation always have a little different run." In this topic, team of authors presents its proposal of prestaged typized Bayessian network model for specified NATECH scenario

  9. Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data

    International Nuclear Information System (INIS)

    Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability. The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle length and number of axles. Because of the nature of traffic configuration, the quantities measured are evidently regarded as random variables. The dependence structure of the data of such complex systems as the traffic systems is also very complex. It is desirable to be able to represent the complex multidimensional-distribution with models where the dependence may be explained in a clear way and different locations where the system operates may be treated simultaneously. Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper we discuss BN models and results concerning their ability to adequately represent the data. The paper places attention on the construction and use of the models. We discuss applications of the proposed BNs in reliability analysis. In particular we show how the proposed BNs may be used for computing design values for individual axles, vehicle weight and maximum bending moments of bridges in certain time intervals. These estimates have been used to advise authorities with respect to bridge reliability. Directions as to how the model may be extended to include locations where the WIM system does not operate are given whenever possible. These ideas benefit from structured expert judgment techniques previously used to quantify Hybrid Bayesian Networks (HBNs) with success

  10. Capturing ecosystem services, stakeholders' preferences and trade-offs in coastal aquaculture decisions: a Bayesian belief network application.

    Science.gov (United States)

    Schmitt, Laetitia Helene Marie; Brugere, Cecile

    2013-01-01

    Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development.

  11. Bayesian networks and agent-based modeling approach for urban land-use and population density change: a BNAS model

    Science.gov (United States)

    Kocabas, Verda; Dragicevic, Suzana

    2013-10-01

    Land-use change models grounded in complexity theory such as agent-based models (ABMs) are increasingly being used to examine evolving urban systems. The objective of this study is to develop a spatial model that simulates land-use change under the influence of human land-use choice behavior. This is achieved by integrating the key physical and social drivers of land-use change using Bayesian networks (BNs) coupled with agent-based modeling. The BNAS model, integrated Bayesian network-based agent system, presented in this study uses geographic information systems, ABMs, BNs, and influence diagram principles to model population change on an irregular spatial structure. The model is parameterized with historical data and then used to simulate 20 years of future population and land-use change for the City of Surrey, British Columbia, Canada. The simulation results identify feasible new urban areas for development around the main transportation corridors. The obtained new development areas and the projected population trajectories with the“what-if” scenario capabilities can provide insights into urban planners for better and more informed land-use policy or decision-making processes.

  12. Capturing ecosystem services, stakeholders' preferences and trade-offs in coastal aquaculture decisions: a Bayesian belief network application.

    Directory of Open Access Journals (Sweden)

    Laetitia Helene Marie Schmitt

    Full Text Available Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12 and primary farm data on the economics of shrimp farming (n = 20 were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development.

  13. Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes.

    Science.gov (United States)

    Gonzalez-Redin, Julen; Luque, Sandra; Poggio, Laura; Smith, Ron; Gimona, Alessandro

    2016-01-01

    An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas. PMID:26597639

  14. Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

    Science.gov (United States)

    Solakiewiz, Richard; Koshak, William

    2008-01-01

    Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian

  15. A study of anthropogenic and climatic disturbance of the New River Estuary using a Bayesian belief network.

    Science.gov (United States)

    Nojavan A, Farnaz; Qian, Song S; Paerl, Hans W; Reckhow, Kenneth H; Albright, Elizabeth A

    2014-06-15

    The present paper utilizes a Bayesian Belief Network (BBN) approach to intuitively present and quantify our current understanding of the complex physical, chemical, and biological processes that lead to eutrophication in an estuarine ecosystem (New River Estuary, North Carolina, USA). The model is further used to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The BBN, through visualizing the structure of the network, facilitates knowledge communication with managers/stakeholders who might not be experts in the underlying scientific disciplines. Moreover, the developed structure of the BBN is transferable to other comparable estuaries. The BBN nodes are discretized exploring a new approach called moment matching method. The conditional probability tables of the variables are driven by a large dataset (four years). Our results show interaction among various predictors and their impact on water quality indicators. The synergistic effects caution future management actions. PMID:24814252

  16. Generalized radial basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision.

    Science.gov (United States)

    Albrecht, S; Busch, J; Kloppenburg, M; Metze, F; Tavan, P

    2000-12-01

    By adding reverse connections from the output layer to the central layer it is shown how a generalized radial basis functions (GRBF) network can self-organize to form a Bayesian classifier, which is also capable of novelty detection. For this purpose, three stochastic sequential learning rules are introduced from biological considerations which pertain to the centers, the shapes, and the widths of the receptive fields of the neurons and allow ajoint optimization of all network parameters. The rules are shown to generate maximum-likelihood estimates of the class-conditional probability density functions of labeled data in terms of multivariate normal mixtures. Upon combination with a hierarchy of deterministic annealing procedures, which implement a multiple-scale approach, the learning process can avoid the convergence problems hampering conventional expectation-maximization algorithms. Using an example from the field of speech recognition, the stages of the learning process and the capabilities of the self-organizing GRBF classifier are illustrated.

  17. Dendritic cells control fibroblastic reticular network tension and lymph node expansion.

    Science.gov (United States)

    Acton, Sophie E; Farrugia, Aaron J; Astarita, Jillian L; Mourão-Sá, Diego; Jenkins, Robert P; Nye, Emma; Hooper, Steven; van Blijswijk, Janneke; Rogers, Neil C; Snelgrove, Kathryn J; Rosewell, Ian; Moita, Luis F; Stamp, Gordon; Turley, Shannon J; Sahai, Erik; Reis e Sousa, Caetano

    2014-10-23

    After immunogenic challenge, infiltrating and dividing lymphocytes markedly increase lymph node cellularity, leading to organ expansion. Here we report that the physical elasticity of lymph nodes is maintained in part by podoplanin (PDPN) signalling in stromal fibroblastic reticular cells (FRCs) and its modulation by CLEC-2 expressed on dendritic cells. We show in mouse cells that PDPN induces actomyosin contractility in FRCs via activation of RhoA/C and downstream Rho-associated protein kinase (ROCK). Engagement by CLEC-2 causes PDPN clustering and rapidly uncouples PDPN from RhoA/C activation, relaxing the actomyosin cytoskeleton and permitting FRC stretching. Notably, administration of CLEC-2 protein to immunized mice augments lymph node expansion. In contrast, lymph node expansion is significantly constrained in mice selectively lacking CLEC-2 expression in dendritic cells. Thus, the same dendritic cells that initiate immunity by presenting antigens to T lymphocytes also initiate remodelling of lymph nodes by delivering CLEC-2 to FRCs. CLEC-2 modulation of PDPN signalling permits FRC network stretching and allows for the rapid lymph node expansion--driven by lymphocyte influx and proliferation--that is the critical hallmark of adaptive immunity.

  18. Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

    CERN Document Server

    Everitt, Richard G

    2012-01-01

    Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian comput...

  19. A Bayesian network approach to linear and nonlinear acoustic echo cancellation

    Science.gov (United States)

    Huemmer, Christian; Maas, Roland; Hofmann, Christian; Kellermann, Walter

    2015-12-01

    This article provides a general Bayesian approach to the tasks of linear and nonlinear acoustic echo cancellation (AEC). We introduce a state-space model with latent state vector modeling all relevant information of the unknown system. Based on three cases for defining the state vector (to model a linear or nonlinear echo path) and its mathematical relation to the observation, it is shown that the normalized least mean square algorithm (with fixed and adaptive stepsize), the Hammerstein group model, and a numerical sampling scheme for nonlinear AEC can be derived by applying fundamental techniques for probabilistic graphical models. As a consequence, the major contribution of this Bayesian approach is a unifying graphical-model perspective which may serve as a powerful framework for future work in linear and nonlinear AEC.

  20. A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Network

    CERN Document Server

    Saad, Walid; Poor, H Vincent; Başar, Tamer; Song, Ju Bin

    2012-01-01

    This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users(PUs), to cooperate and use \\emph{Bayesian nonparametric} techniques to estimate the distributions of the PUs' activity pattern, assumed to be completely unknown. In the proposed model, each cognitive node may have its own individual view on each PU's distribution, and, hence, seeks to find partners having a correlated perception. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use a combination of Bayesian nonparametric models such as the Dirichlet process and statistical goodness of fit techniques ...

  1. Application of Multivariate Probabilistic (Bayesian) Networks to Substance Use Disorder Risk Stratification and Cost Estimation

    OpenAIRE

    Weinstein, Lawrence; Radano, Todd A; Jack, Timothy; Kalina, Philip; Eberhardt, John S.

    2009-01-01

    Introduction: This paper explores the use of machine learning and Bayesian classification models to develop broadly applicable risk stratification models to guide disease management of health plan enrollees with substance use disorder (SUD). While the high costs and morbidities associated with SUD are understood by payers, who manage it through utilization review, acute interventions, coverage and cost limitations, and disease management, the literature shows mixed results for these modalitie...

  2. Transport link scanner: simulating geographic transport network expansion through individual investments

    Science.gov (United States)

    Jacobs-Crisioni, C.; Koopmans, C. C.

    2016-07-01

    This paper introduces a GIS-based model that simulates the geographic expansion of transport networks by several decision-makers with varying objectives. The model progressively adds extensions to a growing network by choosing the most attractive investments from a limited choice set. Attractiveness is defined as a function of variables in which revenue and broader societal benefits may play a role and can be based on empirically underpinned parameters that may differ according to private or public interests. The choice set is selected from an exhaustive set of links and presumably contains those investment options that best meet private operator's objectives by balancing the revenues of additional fare against construction costs. The investment options consist of geographically plausible routes with potential detours. These routes are generated using a fine-meshed regularly latticed network and shortest path finding methods. Additionally, two indicators of the geographic accuracy of the simulated networks are introduced. A historical case study is presented to demonstrate the model's first results. These results show that the modelled networks reproduce relevant results of the historically built network with reasonable accuracy.

  3. Risk Assessment for Mobile Systems Through a Multilayered Hierarchical Bayesian Network.

    Science.gov (United States)

    Li, Shancang; Tryfonas, Theo; Russell, Gordon; Andriotis, Panagiotis

    2016-08-01

    Mobile systems are facing a number of application vulnerabilities that can be combined together and utilized to penetrate systems with devastating impact. When assessing the overall security of a mobile system, it is important to assess the security risks posed by each mobile applications (apps), thus gaining a stronger understanding of any vulnerabilities present. This paper aims at developing a three-layer framework that assesses the potential risks which apps introduce within the Android mobile systems. A Bayesian risk graphical model is proposed to evaluate risk propagation in a layered risk architecture. By integrating static analysis, dynamic analysis, and behavior analysis in a hierarchical framework, the risks and their propagation through each layer are well modeled by the Bayesian risk graph, which can quantitatively analyze risks faced to both apps and mobile systems. The proposed hierarchical Bayesian risk graph model offers a novel way to investigate the security risks in mobile environment and enables users and administrators to evaluate the potential risks. This strategy allows to strengthen both app security as well as the security of the entire system.

  4. Dynamic Bayesian Networks as a Decision Support tool for assessing Climate Change impacts on highly stressed groundwater systems

    Science.gov (United States)

    Molina, José-Luis; Pulido-Velázquez, David; García-Aróstegui, José Luis; Pulido-Velázquez, Manuel

    2013-02-01

    SummaryBayesian Networks (BNs) are powerful tools for assessing and predicting consequences of water management scenarios and uncertain drivers like climate change, integrating available scientific knowledge with the interests of the multiple stakeholders. However, among their major limitations, the non-transient treatment of the cause-effect relationship stands out. A Decision Support System (DSS) based on Dynamic Bayesian Networks (DBNs) is proposed here aimed to palliate that limitation through time slicing technique. The DSS comprises several classes (Object-Oriented BN networks), especially designed for future 5 years length time steps (time slices), covering a total control period of 30 years (2070-2100). The DSS has been developed for assessing impacts generated by different Climate Change (CC) scenarios (generated from several Regional Climatic Models (RCMs) under two emission scenarios, A1B and A2) in an aquifer system (Serral-Salinas) affected by intensive groundwater use over the last 30 years. A calibrated continuous water balance model was used to generate hydrological CC scenarios, and then a groundwater flow model (MODFLOW) was employed in order to analyze the aquifer behavior under CC conditions. Results obtained from both models were used as input for the DSS, considering rainfall, aquifer recharge, variation of piezometric levels and temporal evolution of aquifer storage as the main hydrological components of the aquifer system. Results show the evolution of the aquifer storage for each future time step under different climate change conditions and under controlled water management interventions. This type of applications would allow establishing potential adaptation strategies for aquifer systems as the CC comes into effect.

  5. 贝叶斯分类器的判别式参数学习%Discriminative parameter learning of Bayesian network classifier

    Institute of Scientific and Technical Information of China (English)

    石洪波; 柳亚琴; 李爱军

    2011-01-01

    为了提高贝叶斯分类器的分类性能,针对贝叶斯网络分类器的构成特征,提出一种基于参数集成的贝叶斯分类器判别式参数学习算法PEBNC.该算法将贝叶斯分类器的参数学习视为回归问题,将加法回归模型应用于贝叶斯网络分类器的参数学习,实现贝叶斯分类器的判别式参数学习.实验结果表明,在大多数实验数据上,PEBNC能够明显提高贝叶斯分类器的分类准确率.此外,与一般的贝叶斯集成分类器相比,PEBNC不必存储成员分类器的参数.空间复杂度大大降低.%Concerning the characteristics of Bayesian networks classifier, a discriminative parameter learning algorithm of Bayesian networks classifier based on parameters ensemble named PEBNC was proposed to improve the classification performance of Bayesian classifier. This algorithm regarded the parameter learning as a regression problem, applied the additive regression model to the parameter learning of Bayesian networks classifier, and realized a discriminative parameter learning of Bayesian networks classifier. The experimental results indicate that the PEBNC classifier can improve the classification performance in most cases. Furthermore, compared with the general Bayesian classifier ensemble, PEBNC requires less space because there is no need to save parameters of individual classifiers.

  6. Parameter Learning of Boolean Bayesian Networks%布尔型贝叶斯网络参数学习

    Institute of Scientific and Technical Information of China (English)

    吴永广; 周兴旺

    2015-01-01

    布尔型贝叶斯网络是一类由布尔型变量组成的网络,它能够以线性多变量函数描述,使计算和处理上灵活高效。通过运用连接树算法对络进行分块化处理的方法,可以提高算法的效率,然后以传统的最大似然估计方法对布尔型网络的参数进行学习。服从同一分布律的贝叶斯网络参数学习算法发展比较成熟,这类以狄利克雷或者高斯分布为基础的算法在应用领域中难以发挥其应有的价值。相比之下,基于布尔型贝叶斯网络下的参数学习更贴近于应用,在人工智能和数据挖掘等领域有很好的发展前景。%Boolean Bayesian network is a class of Bayesian networks which are made up of Boolean varia-bles. The method to describe the network with a multi-linear function is flexible and efficient to compute and process variables. By introducing Junction Tree algorithm,the network can be divided into blocks which can make it easy to calculate. Then the traditional maximum likelihood estimation method was used for learning Boolean networks. Parameter learning algorithm following the same distribution is more ma-ture,but this kind of algorithm based on Dirichlet or Gaussian distribution is difficult to play its proper val-ue in practice. In contrast,parameter learning based on Boolean networks gets close to applications. It has good prospects for development in areas such as artificial intelligence and data mining.

  7. Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

    OpenAIRE

    Salvador Dura-Bernal; Thomas Wennekers; DENHAM, SUSAN L.

    2012-01-01

    Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very co...

  8. Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses

    Science.gov (United States)

    Galbraith, Craig S.; Merrill, Gregory B.; Kline, Doug M.

    2012-01-01

    In this study we investigate the underlying relational structure between student evaluations of teaching effectiveness (SETEs) and achievement of student learning outcomes in 116 business related courses. Utilizing traditional statistical techniques, a neural network analysis and a Bayesian data reduction and classification algorithm, we find…

  9. ACO Algorithm Applied to Multi-Objectives Optimization of Capacity Expansion in Next Generation Wireless Network

    Directory of Open Access Journals (Sweden)

    Dac-Nhuong Le

    2013-09-01

    Full Text Available The optimal capacity expansion of base station subsystems in Next Generation Wireless Network (NGWN problem with respect to multi-demand type and system capacity constraints is known to be NP-complete. In this paper, we propose a novel ant colony optimization algorithm to solve a network topology has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. There are two important aspects of upgrading to NGWN. The first importance of backward compatibility with pre-existing networks, and the second is the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. Our objective function is the sources to concentrators connectivity costas well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We evaluate the performance of our algorithm with a set of real world and data randomly generated. Numerical results show that our algorithms is a promising approach to solve this problem.

  10. Design and Evaluation of the User-Adapted Program Scheduling system based on Bayesian Network and Constraint Satisfaction

    Science.gov (United States)

    Iwasaki, Hirotoshi; Sega, Shinichiro; Hiraishi, Hironori; Mizoguchi, Fumio

    In recent years, lots of music content can be stored in mobile computing devices, such as a portable digital music player and a car navigation system. Moreover, various information content like news or traffic information can be acquired always anywhere by a cellular communication and a wireless LAN. However, usability issues arise from the simple interfaces of mobile computing devices. Moreover, retrieving and selecting such content poses safety issues, especially while driving. Thus, it is important for the mobile system to recommend content automatically adapted to user's preference and situation. In this paper, we present the user-adapted program scheduling that generates sequences of content (Program) suiting user's preference and situation based on the Bayesian network and the Constraint Satisfaction Problem (CSP) technique. We also describe the design and evaluation of its realization system, the Personal Program Producer (P3). First, preference such as a genre ratio of content in a program is learned as a Bayesian network model using simple operations such as a skip behavior. A model including each content tends to become large-scale. In order to make it small, we present the model separation method that carries out losslessly compression of the model. Using the model, probabilistic distributions of preference to generate constraints are inferred. Finally satisfying the constraints, a program is produced. This kind of CSP has an issue of which the number of variables is not fixedness. In order to make it variable, we propose a method using metavariables. To evaluate the above methods, we applied them to P3 on a car navigation system. User evaluations helped us clarify that the P3 can produce the program that a user prefers and adapt it to the user.

  11. Dynamic traffic splitting to parallel wireless networks with partial information: a Bayesian approach.

    NARCIS (Netherlands)

    Bhulai, S.; Hoekstra, G.J.; Bosman, J.W.; Mei, R.D. van der

    2012-01-01

    Contemporary wireless networks are based on a wide range of different technologies providing overlapping coverage. This offers users a seamless integration of connectivity by allowing to switch between networks, and opens up a promising area for boosting the performance of wireless networks. Motivat

  12. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

    NARCIS (Netherlands)

    Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim

    2016-01-01

    Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as i

  13. Current trends in Bayesian methodology with applications

    CERN Document Server

    Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia

    2015-01-01

    Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on

  14. Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

    Directory of Open Access Journals (Sweden)

    Ozgur Baskan

    2013-01-01

    Full Text Available During the last two decades, Continuous Network Design Problem (CNDP has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use available budget economically and to mitigate traffic congestion. The CNDP can generally be formulated as bilevel programming model in which the upper level deals with finding optimal link capacity expansions, whereas at the lower level, User Equilibrium (UE link flows are determined by Wardrop’s first principle. In this paper, cuckoo search (CS algorithm with Lévy flights is introduced for finding optimal link capacity expansions because of its recent successful applications in solving such complex problems. CS is applied to the 16-link and Sioux Falls networks and compared with available methods in the literature. Results show the potential of CS for finding optimal or near optimal link capacity expansions in a given road network.

  15. Spatial spread of Eurasian beavers in river networks: a comparison of range expansion rates.

    Science.gov (United States)

    Barták, Vojtěch; Vorel, Aleš; Símová, Petra; Puš, Vladimír

    2013-05-01

    1. Accurately measuring the rate of spread for expanding populations is important for reliably predicting their future spread, as well as for evaluating the effect of different conditions and management activities on that rate of spread. 2. Although a number of methods have been developed for such measurement, all these are designed only for one- or two-dimensional spread. Species dispersing along rivers, however, require specific methods due to the distinctly branching structure of river networks. 3. In this study, we analyse data regarding Eurasian beavers' modern recolonization of the Czech Republic. We developed a new methodology for quantifying spread of species dispersing along streams based on representation of the river network by means of a weighted graph. 4. We defined two different network-based spread rate measures, one estimating the rate of range expansion, with the range defined as the total length of occupied streams, and the second, named range diameter, quantifying the progress along one or several main streams. In addition, we estimated the population growth rates, and, dividing the population size by the range size, we measured the density of beaver records within their overall range. Using linear regression, we compared four beaver populations under different environmental conditions in terms of each of these measures. Finally, we discuss the differences between our method and the classical approaches. 5. Our method provided substantially higher spread rate values than did the classical methods. Both population growth and range expansion were found to follow logistic growth. In cases of there being no considerable barriers in dispersal routes, the rate of progress along main streams did not differ significantly among populations. In homogeneous environments, population densities remained relatively constant over time even though overall population sizes increased. This indicates that at large spatial scales, the population growth of beavers

  16. A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

    Science.gov (United States)

    Humphrey, Greer B.; Gibbs, Matthew S.; Dandy, Graeme C.; Maier, Holger R.

    2016-09-01

    Monthly streamflow forecasts are needed to support water resources decision making in the South East of South Australia, where baseflow represents a significant proportion of the total streamflow and soil moisture and groundwater are important predictors of runoff. To address this requirement, the utility of a hybrid monthly streamflow forecasting approach is explored, whereby simulated soil moisture from the GR4J conceptual rainfall-runoff model is used to represent initial catchment conditions in a Bayesian artificial neural network (ANN) statistical forecasting model. To assess the performance of this hybrid forecasting method, a comparison is undertaken of the relative performances of the Bayesian ANN, the GR4J conceptual model and the hybrid streamflow forecasting approach for producing 1-month ahead streamflow forecasts at three key locations in the South East of South Australia. Particular attention is paid to the quantification of uncertainty in each of the forecast models and the potential for reducing forecast uncertainty by using the hybrid approach is considered. Case study results suggest that the hybrid models developed in this study are able to take advantage of the complementary strengths of both the ANN models and the GR4J conceptual models. This was particularly the case when forecasting high flows, where the hybrid models were shown to outperform the two individual modelling approaches in terms of the accuracy of the median forecasts, as well as reliability and resolution of the forecast distributions. In addition, the forecast distributions generated by the hybrid models were up to 8 times more precise than those based on climatology; thus, providing a significant improvement on the information currently available to decision makers.

  17. How Good Is Crude MDL for Solving the Bias-Variance Dilemma? An Empirical Investigation Based on Bayesian Networks

    Science.gov (United States)

    Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

    2014-01-01

    The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size. PMID:24671204

  18. Intelligent Sensor Based Bayesian Neural Network for Combined Parameters and States Estimation of a Brushed DC Motor

    Directory of Open Access Journals (Sweden)

    Hacene MELLAH

    2016-07-01

    Full Text Available The objective of this paper is to develop an Artificial Neural Network (ANN model to estimate simultaneously, parameters and state of a brushed DC machine. The proposed ANN estimator is novel in the sense that his estimates simultaneously temperature, speed and rotor resistance based only on the measurement of the voltage and current inputs. Many types of ANN estimators have been designed by a lot of researchers during the last two decades. Each type is designed for a specific application. The thermal behavior of the motor is very slow, which leads to large amounts of data sets. The standard ANN use often Multi-Layer Perceptron (MLP with Levenberg-Marquardt Backpropagation (LMBP, among the limits of LMBP in the case of large number of data, so the use of MLP based on LMBP is no longer valid in our case. As solution, we propose the use of Cascade-Forward Neural Network (CFNN based Bayesian Regulation backpropagation (BRBP. To test our estimator robustness a random white-Gaussian noise has been added to the sets. The proposed estimator is in our viewpoint accurate and robust.

  19. BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yan Gao

    2014-01-01

    Full Text Available With the rapid development and application of medical sensor networks, the security has become a big challenge to be resolved. Trust mechanism as a method of “soft security” has been proposed to guarantee the network security. Trust models to compute the trustworthiness of single node and each path are constructed, respectively, in this paper. For the trust relationship between nodes, trust value in every interval is quantified based on Bayesian inference. A node estimates the parameters of prior distribution by using the collected recommendation information and obtains the posterior distribution combined with direct interactions. Further, the weights of trust values are allocated through using the ordered weighted vector twice and overall trust degree is represented. With the associated properties of Tsallis entropy, the definition of path Tsallis entropy is put forward, which can comprehensively measure the uncertainty of each path. Then a method to calculate the credibility of each path is derived. The simulation results show that the proposed models can correctly reflect the dynamic of node behavior, quickly identify the malicious attacks, and effectively avoid such path containing low-trust nodes so as to enhance the robustness.

  20. Plate Boundary Observatory Southwest Region Network Operations, Expansion and Communications Hardening

    Science.gov (United States)

    Mann, D.; Walls, C. P.; Basset, A.; Turner, R.; Lawrence, S.; Feaux, K.; Mattioli, G. S.

    2014-12-01

    The Southwest Region of the Plate Boundary Observatory manages 480 continuously operating GPS stations located principally along the transform system of the San Andreas fault, Eastern California Shear Zone and the northern Baja peninsula. In the past year, network uptime averaged 98% with greater than 99% data acquisition. In an effort to modernize the network, we have started to replace Trimble NETRS receivers with GNSS capable NETR9 receivers. Currently, we have 431 NetRS receivers deployed in the region, and 48 NetR9 receivers. In addition, 82 stations (17%) stream 1 Hz data over the VRS3Net typically with 92%. Based on their typical data download rates, approximately 252 (53%) of all stations are capable of streaming 1 Hz, but have not yet been added to the real time network because of lack of resources. In the immediate aftermath of the M8.2 Chile earthquake in April 2014, high rate data downloads from the entire SW network had a success rate of 95% and 71% for 1 Hz and 5 Hz data downloads respectively. We have continued to upgrade critical radio networks, including the San Francisco Bay Area, Anza Borrego, and Santa Barbara networks. These efforts are ongoing, but they have already significantly improved data download rates and dependability. We are also converting cell modem to radio communications whenever possible for increased reliability and cost savings. In December 2013 the 13-station Edison network expansion was completed through cost recovery contracts. These stations span coastal southern California in Orange, San Diego and Los Angeles counties including a hybrid site on the Elly oil platform. The primary purpose of the stations is to aid in the seismic source characterization of the San Onofre Nuclear Generating Station and assess the strain field associated with the Oceanside Blind Thrust and Newport Inglewood fault. The new stations fill a gap between SCIGN and PBO. Three sites have WXT520 Vaisala metpacks and twelve stations stream 1Hz data via

  1. Defining Biological Networks for Noise Buffering and Signaling Sensitivity Using Approximate Bayesian Computation

    Directory of Open Access Journals (Sweden)

    Shuqiang Wang

    2014-01-01

    Full Text Available Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC method based on sequential Monte Carlo (SMC to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

  2. Software Failure Predication Model Based on Bayesian Network%基于Bayes网的软件失效预测模型

    Institute of Scientific and Technical Information of China (English)

    白成刚; 俞蒙槐; 胡上序; 蔡开元

    2003-01-01

    Owing to the complexity of software development,the software reliability model should not only have the capability of dealing with multiple complex factors,but also provide the function of flexibility in construction. So far, no software reliability model is universally applicable. The main reason for this is of too many conditions, thus making software reliability models introvert. Bayesian network is a powerful tool for solving this problem, which exhibits strong adaptability in dealing with problems revolving complex variant factors. In the paper,software failure predication model based on Markov. Bayesian network is established and analyzed thoroughly. Then a method of solving the model is given. Finally,through an example the validity of the model is validated.

  3. High-tension electricity network expansions in Argentina: decision mechanisms and willingness-to-pay revelation

    Energy Technology Data Exchange (ETDEWEB)

    Chisari, O.O. [Departamento de Economia y Finanzas, Universidad Argentina de la Empresa, Chile 1142 lo piso, 1073 Buenos Aires (Argentina); Dal-Bo, P. [Department of Economics, University of California, Los Angeles, CA (United States); Romero, C.A. [Departamento de Economia y Finanzas and Centro de Estudios Economicos de la Regulacion, Universidad Argentina de la Empresa, Buenos Aires (Argentina)

    2001-11-01

    This paper describes the procedures established by the present regulation for high-tension network expansions, studies its optimality and points its main problems. According to the present regulatory scheme, the decision to expand the high-tension network system is on the hands of the private sector. A simple model of the Argentine electricity system and its regulation allowed the simulation of cases in which the present 'Public Contest' method could result in the rejection of socially desirable projects and the acceptance of undesirable ones. Three main reasons for the existence of wrong incentives to investment are found. First, since consumers are not included in the mechanism, they have no way to reveal their willingness-to-pay for the investment. Second, the approximation of the future use of the line is done in such a way that it leaves important nodes under represented, thereby affecting the optimality of the mechanism. Third, the profit agents obtain from a line may not be related to their use, e.g. the generators profits depend also on generation costs.

  4. High-tension electricity network expansions in Argentina: decision mechanisms and willingness-to-pay revelation

    International Nuclear Information System (INIS)

    This paper describes the procedures established by the present regulation for high-tension network expansions, studies its optimality and points its main problems. According to the present regulatory scheme, the decision to expand the high-tension network system is on the hands of the private sector. A simple model of the Argentine electricity system and its regulation allowed the simulation of cases in which the present 'Public Contest' method could result in the rejection of socially desirable projects and the acceptance of undesirable ones. Three main reasons for the existence of wrong incentives to investment are found. First, since consumers are not included in the mechanism, they have no way to reveal their willingness-to-pay for the investment. Second, the approximation of the future use of the line is done in such a way that it leaves important nodes under represented, thereby affecting the optimality of the mechanism. Third, the profit agents obtain from a line may not be related to their use, e.g. the generators profits depend also on generation costs

  5. Characterizing Species at Risk II: Using Bayesian Belief Networks as Decision Support Tools to Determine Species Conservation Categories Under the Northwest Forest Plan

    OpenAIRE

    Mark H. Huff; Turley, Marianne C.; Randy Molina; Russ Holmes; Steve Morey; Hohenlohe, Paul A.; Bruce G. Marcot; John A. Laurence

    2006-01-01

    We developed a set of decision-aiding models as Bayesian belief networks (BBNs) that represented a complex set of evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive management process that evaluated new scientific information under the Northwest Forest Plan. The models wer...

  6. Governance mechanisms, managerial’s commitment bias and firm’s investment decision escalation: Failure of firm?s crises communication: Bayesian network method

    OpenAIRE

    Hamza Fadhila; Azouzi Mohamed Ali; Jarboui Anis

    2014-01-01

    This paper studies the role of governance mechanisms, CEO‟s cognitive characteristics and firms‟ financial features in justifying the CEO‟s escalatory behavior in firm‟s investment decision. This study aims to provide evidence as to whether managers consider the persuasive influence of governance mechanisms and the firm‟s financial indicators to persevere his initial investment decision while he notes a high level of commitment bias. The proposed model of this paper uses Bayesian Network Meth...

  7. Efficacy of the Additional Neoadjuvant Chemotherapy to Concurrent Chemoradiotherapy for Patients with Locoregionally Advanced Nasopharyngeal Carcinoma: a Bayesian Network Meta-analysis of Randomized Controlled Trials

    OpenAIRE

    Chen, Yu-Pei; Guo, Rui; Liu, Na; Liu, Xu; Mao, Yan-Ping; Tang, Ling-Long; Zhou, Guan-qun; Lin, Ai-Hua; Sun, Ying; Ma, Jun

    2015-01-01

    Background: Due to the lack of studies, it remains unclear whether the additional neoadjuvant chemotherapy (NACT) to concurrent chemoradiotherapy (CCRT) is superior to CCRT alone for locoregionally advanced nasopharyngeal carcinoma (NPC). The main objective of this Bayesian network meta-analysis was to determine the efficacy of NACT+CCRT as compared with CCRT alone. Methods: We comprehensively searched databases and extracted data from randomized controlled trials involving NPC patients who r...

  8. 基于共轭先验分布的贝叶斯网络分类模型%Bayesian network classifier based on conjugate prior distribution

    Institute of Scientific and Technical Information of China (English)

    杨颖涛; 王跃钢; 邓卫强; 徐洪涛

    2012-01-01

    针对贝叶斯网络后验概率需计算样本边际分布,计算代价大的问题,将共轭先验分布思想引入贝叶斯分类,提出了基于共轭先验分布的贝叶斯网络分类模型.针对非区间离散样本,提出一种自适应的样本离散方法,将小波包提取模拟电路故障特征离散化作为分类模型属性.仿真验证表明,模型分类效果较好,算法运行速度得以提高,也可应用于连续样本和多分类的情况,扩展了贝叶斯网络分类的应用范围.%In order to reducing calculate costs of Bayesian network,when calculating posterior probability of samples that need the marginal distribution,an approach of Bayesian network classifier based on conjugate prior distribution is proposed. An adaptive discretization method is also proposed to discrete non-interval samples. The fault feature of analog circuit extracted by wavelet packet is taken as a discrete property of Bayesian network classification model. The simulation result shows that,this classifier has high accuracy and efficiency of analog circuit fault diagnosis,and can be applied to continuous and multi-classification case,which extends the scope of application of Bayesian network classification.

  9. A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species

    Directory of Open Access Journals (Sweden)

    Goodacre Royston

    2011-01-01

    Full Text Available Abstract Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA. The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA.

  10. Integrating indigenous ecological and scientific hydro-geological knowledge using a Bayesian Network in the context of water resource development

    Science.gov (United States)

    Liedloff, A. C.; Woodward, E. L.; Harrington, G. A.; Jackson, S.

    2013-08-01

    The contributions indigenous ecological knowledge can make to better inform water management decisions are currently undervalued leading to an underrepresentation of indigenous values in water planning and policy. This paper outlines a novel approach in which indigenous ecological knowledge informs cause and effect relationships between species and aquatic habitats to promote broader ecosystem understanding. A Bayesian Network was developed to synthesise the seasonal aquatic knowledge of a group of Gooniyandi Aboriginal language speakers, including fish species’ availability, condition and required habitat, and integrate it with hydrogeological understanding obtained from research undertaken in a stretch of the Fitzroy River, Western Australia. This river system, like most in northern Australia, is highly seasonal and entirely dependent upon groundwater for maintaining flow during prolonged dry seasons. We found that potential changes in river flow rates caused by future water resource development, such as groundwater extraction and surface water diversion, may have detrimental effects on the ability to catch the high value aquatic food species such as Barramundi and Sawfish, but also that species such as Black Bream may benefit. These findings result from changes in availability of habitats at times when Gooniyandi understanding shows they are important for providing aquatic resources in good condition. This study raises awareness of the potential outcomes of future water management and stimulates communication between indigenous people, the scientific community and water managers by developing a model of indigenous understanding from which to predict eco-hydrological change.

  11. Low Energy Wireless Body-Area Networks for Fetal ECG Telemonitoring via the Framework of Block Sparse Bayesian Learning

    CERN Document Server

    Zhang, Zhilin; Makeig, Scott; Rao, Bhaskar D

    2012-01-01

    Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a low-power wireless body-area network for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing data with low power consumption. However, due to some specific characteristics of FECG recordings such as non-sparsity and strong noise contamination, current CS algorithms generally fail in this application. In this work we utilize the block sparse Bayesian learning (bSBL) framework, a recently developed framework solving the CS problems. To illustrate the ability of the bSBL methods, we apply it to two representative FECG datasets. In one dataset the fetal heartbeat signals are visible, while in the other dataset are barely visible. The experiment results show that the bSBL framework is capable of compressing FECG raw recordings and successfully reconstructing them. These successes rely on two unique features of the bSBL framework; on...

  12. Extracting recurrent scenarios from narrative texts using a Bayesian network: application to serious occupational accidents with movement disturbance.

    Science.gov (United States)

    Abdat, F; Leclercq, S; Cuny, X; Tissot, C

    2014-09-01

    A probabilistic approach has been developed to extract recurrent serious Occupational Accident with Movement Disturbance (OAMD) scenarios from narrative texts within a prevention framework. Relevant data extracted from 143 accounts was initially coded as logical combinations of generic accident factors. A Bayesian Network (BN)-based model was then built for OAMDs using these data and expert knowledge. A data clustering process was subsequently performed to group the OAMDs into similar classes from generic factor occurrence and pattern standpoints. Finally, the Most Probable Explanation (MPE) was evaluated and identified as the associated recurrent scenario for each class. Using this approach, 8 scenarios were extracted to describe 143 OAMDs in the construction and metallurgy sectors. Their recurrent nature is discussed. Probable generic factor combinations provide a fair representation of particularly serious OAMDs, as described in narrative texts. This work represents a real contribution to raising company awareness of the variety of circumstances, in which these accidents occur, to progressing in the prevention of such accidents and to developing an analysis framework dedicated to this kind of accident.

  13. Developing best-practice Bayesian Belief Networks in ecological risk assessments for freshwater and estuarine ecosystems: a quantitative review.

    Science.gov (United States)

    McDonald, K S; Ryder, D S; Tighe, M

    2015-05-01

    Bayesian Belief Networks (BBNs) are being increasingly used to develop a range of predictive models and risk assessments for ecological systems. Ecological BBNs can be applied to complex catchment and water quality issues, integrating multiple spatial and temporal variables within social, economic and environmental decision making processes. This paper reviews the essential components required for ecologists to design a best-practice predictive BBN in an ecological risk assessment (ERA) framework for aquatic ecosystems, outlining: (1) how to create a BBN for an aquatic ERA?; (2) what are the challenges for aquatic ecologists in adopting the best-practice applications of BBNs to ERAs?; and (3) how can BBNs in ERAs influence the science/management interface into the future? The aims of this paper are achieved using three approaches. The first is to demonstrate the best-practice development of BBNs in aquatic sciences using a simple nutrient model. The second is to discuss the limitations and challenges aquatic ecologists encounter when applying BBNs to ERAs. The third is to provide a framework for integrating best-practice BBNs into ERAs and the management of aquatic ecosystems. A quantitative review of the application and development of BBNs in aquatic science from 2002 to 2014 was conducted to identify areas where continued best-practice development is required. We outline a best-practice framework for the integration of BBNs into ERAs and study of complex aquatic systems. PMID:25733196

  14. A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

    Science.gov (United States)

    Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

    2016-06-01

    This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

  15. A General Bayesian Network Approach to Analyzing Online Game Item Values and Its Influence on Consumer Satisfaction and Purchase Intention

    Science.gov (United States)

    Lee, Kun Chang; Park, Bong-Won

    Many online game users purchase game items with which to play free-to-play games. Because of a lack of research into which there is no specified framework for categorizing the values of game items, this study proposes four types of online game item values based on an analysis of literature regarding online game characteristics. It then proposes to investigate how online game users perceive satisfaction and purchase intention from the proposed four types of online game item values. Though regression analysis has been used frequently to answer this kind of research question, we propose a new approach, a General Bayesian Network (GBN), which can be performed in an understandable way without sacrificing predictive accuracy. Conventional techniques, such as regression analysis, do not provide significant explanation for this kind of problem because they are fixed to a linear structure and are limited in explaining why customers are likely to purchase game items and if they are satisfied with their purchases. In contrast, the proposed GBN provides a flexible underlying structure based on questionnaire survey data and offers robust decision support on this kind of research question by identifying its causal relationships. To illustrate the validity of GBN in solving the research question in this study, 327 valid questionnaires were analyzed using GBN with what-if and goal-seeking approaches. The experimental results were promising and meaningful in comparison with regression analysis results.

  16. Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents

    International Nuclear Information System (INIS)

    During the last three decades, several techniques have been developed for the quantitative study of human reliability. In the 1980s, techniques were developed to model systems by means of binary trees, which did not allow for the representation of the context in which human actions occur. Thus, these techniques cannot model the representation of individuals, their interrelationships, and the dynamics of a system. These issues make the improvement of methods for Human Reliability Analysis (HRA) a pressing need. To eliminate or at least attenuate these limitations, some authors have proposed modeling systems using Bayesian Belief Networks (BBNs). The application of these tools is expected to address many of the deficiencies in current approaches to modeling human actions with binary trees. This paper presents a methodology based on BBN for analyzing human reliability and applies this method to the operation of an oil tanker, focusing on the risk of collision accidents. The obtained model was used to determine the most likely sequence of hazardous events and thus isolate critical activities in the operation of the ship to study Internal Factors (IFs), Skills, and Management and Organizational Factors (MOFs) that should receive more attention for risk reduction.

  17. Assessing the roles of environmental factors in coastal fish production in the northern Baltic Sea: a Bayesian network application.

    Science.gov (United States)

    Uusitalo, Laura; Kuikka, Sakari; Kauppila, Pirkko; Söderkultalahti, Pirkko; Bäck, Saara

    2012-07-01

    Environmental conditions play a crucial role in the distribution and abundance of fish species in any area. Much research has been attributed to the requirements and tolerance limits of commercially exploited fish species. It is rare, however, that studies have been able to address the relative importance of potentially restrictive environmental factors; extensive enough to allow for estimation of the effect of several environmental factors through the fishes' life span. The coastline of Finland in the northern Baltic Sea offers a unique natural experimental setting that can be used to assess the relative importance of various environmental factors for the species occupying it. The area includes major variations in several crucial environmental factors: salinity, temperature regime, represented by winter ice duration, coastline characteristics, and eutrophic status. Furthermore, Finland has collected extensive and spatially representative data of water quality and environmental factors, as well as a long and extraordinarily spatially detailed data set of commercial catches of several fish species. In this article, we make an attempt to correlate the environmental data to the commercial catches of fish species, assuming that the commercial catches reflect, to some reasonable degree, the productivity of that species in that area (compared to other areas and combinations of environmental factors, not to other species). We use a Bayesian network approach to examine the sensitivity of the species to the environmental factors. PMID:21309077

  18. Use of a Bayesian Belief Network to Predict the Impacts of Commercializing Non-timber Forest Products on Livelihoods

    Directory of Open Access Journals (Sweden)

    Dirk W. te Velde

    2006-12-01

    Full Text Available Commercialization of non-timber forest products (NTFPs has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed.

  19. Bayesian networks: a new method for the modeling of bibliographic knowledge: application to fall risk assessment in geriatric patients.

    Science.gov (United States)

    Lalande, Laure; Bourguignon, Laurent; Carlier, Chloé; Ducher, Michel

    2013-06-01

    Falls in geriatry are associated with important morbidity, mortality and high healthcare costs. Because of the large number of variables related to the risk of falling, determining patients at risk is a difficult challenge. The aim of this work was to validate a tool to detect patients with high risk of fall using only bibliographic knowledge. Thirty articles corresponding to 160 studies were used to modelize fall risk. A retrospective case-control cohort including 288 patients (88 ± 7 years) and a prospective cohort including 106 patients (89 ± 6 years) from two geriatric hospitals were used to validate the performances of our model. We identified 26 variables associated with an increased risk of fall. These variables were split into illnesses, medications, and environment. The combination of the three associated scores gives a global fall score. The sensitivity and the specificity were 31.4, 81.6, 38.5, and 90 %, respectively, for the retrospective and the prospective cohort. The performances of the model are similar to results observed with already existing prediction tools using model adjustment to data from numerous cohort studies. This work demonstrates that knowledge from the literature can be synthesized with Bayesian networks.

  20. Conditions for the adoption of conservation agriculture in Central Morocco: an approach based on Bayesian network modelling

    Directory of Open Access Journals (Sweden)

    Laura Bonzanigo

    2016-03-01

    Full Text Available Research in Central Morocco, proves that conservation agriculture increases yields, reduces labour requirements, and erosion, and improves soil fertility. However, after nearly two decades of demonstration and advocacy, adoption is still limited. This paper investigates the critical constraints and potential opportunities for the adoption of conservation agriculture for different typologies of farms. We measured the possible pathways of adoption via a Bayesian decision network (BDN. BDNs allow the inclusion of stakeholders’ knowledge where data is scant, whilst at the same time they are supported by a robust mathematical background. We first developed a conceptual map of the elements affecting the decision about tillage, which we refined in a workshop with farmers and researchers from the Settat area. We then involved experts in the elicitation of conditional probabilities tables, to quantify the cascade of causal links that determine (or not the adoption. Via BDNs, we could categorise under which specific technical and socio-economic conditions no tillage agriculture is best suited to which farmers. We, by identifying the main constraints and running sensitivity analyses, were able to convey clear messages on how policy- makers may facilitate the conversion. As new evidence is collected, the BDN can be updated to obtain evidence more targeted and fine tuned to the adoption contexts.

  1. Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating

    OpenAIRE

    Mas, Massimiliano Dal

    2010-01-01

    It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as uncertain updating times, while achieving acceptable service levels at minimum computational cost. This paper proposes an application-independent merging ontologies for any open interaction system. A solution that uses Multi-Entity Bayesan Networks with SWRL rules, and a Java program is presented to dynamically monitor Exogenous and Endogenous temporal evolution on updating merging ontologies...

  2. Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating

    CERN Document Server

    Mas, Massimiliano Dal

    2010-01-01

    It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as uncertain updating times, while achieving acceptable service levels at minimum computational cost. This paper proposes an application-independent merging ontologies for any open interaction system. A solution that uses Multi-Entity Bayesan Networks with SWRL rules, and a Java program is presented to dynamically monitor Exogenous and Endogenous temporal evolution on updating merging ontologies on a probabilistic framework for the Semantic Web.

  3. Updating Parameters for Volcanic Hazard Assessment Using Multi-parameter Monitoring Data Streams And Bayesian Belief Networks

    Science.gov (United States)

    Odbert, Henry; Aspinall, Willy

    2014-05-01

    Evidence-based hazard assessment at volcanoes assimilates knowledge about the physical processes of hazardous phenomena and observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We discuss

  4. Probabilistic neural networks using Bayesian decision strategies and a modified Gompertz model for growth phase classification in the batch culture of Bacillus subtilis.

    Science.gov (United States)

    Simon; Nazmul Karim M

    2001-01-01

    Probabilistic neural networks (PNNs) were used in conjunction with the Gompertz model for bacterial growth to classify the lag, logarithmic, and stationary phases in a batch process. Using the fermentation time and the optical density of diluted cell suspensions, sampled from a culture of Bacillus subtilis, PNNs enabled a reliable determination of the growth phases. Based on a Bayesian decision strategy, the Gompertz based PNN used newly proposed definition of the lag and logarithmic phases to estimate the latent, logarithmic and stationary phases. This network topology has the potential for use with on-line turbidimeter for the automation and control of cultivation processes.

  5. Study of Single Top Quark Production Using Bayesian Neural Networks With D0 Detector at the Tevatron

    Energy Technology Data Exchange (ETDEWEB)

    Joshi, Jyoti [Panjab Univ., Chandigarh (India)

    2012-01-01

    Top quark, the heaviest and most intriguing among the six known quarks, can be created via two independent production mechanisms in {\\pp} collisions. The primary mode, strong {\\ttbar} pair production from a $gtt$ vertex, was used by the {\\d0} and CDF collaborations to establish the existence of the top quark in March 1995. The second mode is the electroweak production of a single top quark or antiquark, which has been observed recently in March 2009. Since single top quarks are produced at hadron colliders through a $Wtb$ vertex, thereby provide a direct probe of the nature of $Wtb$ coupling and of the Cabibbo-Kobayashi-Maskawa matrix element, $V_{tb}$. So this mechanism provides a sensitive probe for several, standard model and beyond standard model, parameters such as anomalous $Wtb$ couplings. In this thesis, we measure the cross section of the electroweak produced top quark in three different production modes, $s+t$, $s$ and $t$-channels using a technique based on the Bayesian neural networks. This technique is applied for analysis of the 5.4 $fb^{-1}$ of data collected by the {\\d0} detector. From a comparison of the Bayesian neural networks discriminants between data and the signal-background model using Bayesian statistics, the cross sections of the top quark produced through the electroweak mechanism have been measured as: \\[\\sigma(p\\bar{p}→tb+X,tqb+X) = 3.11^{+0.77}_{-0.71}\\;\\rm pb\\] \\[\\sigma(p\\bar{p}→tb+X) = 0.72^{+0.44}_{-0.43}\\;\\rm pb\\] \\[\\sigma(p\\bar{p}→tqb+X) = 2.92^{+0.87}_{-0.73}\\;\\rm pb\\] % The $s+t$-channel has a gaussian significance of $4.7\\sigma$, the $s$-channel $0.9\\sigma$ and the $t$-channel~$4.7\\sigma$. The results are consistent with the standard model predictions within one standard deviation. By combining these results with the results for two other analyses (using different MVA techniques) improved results \\[\\sigma(p\\bar{p}→tb+X,tqb+X) = 3.43^{+0.73}_{-0.74}\\;\\rm pb\\] \\[\\sigma

  6. Bayesian community detection

    DEFF Research Database (Denmark)

    Mørup, Morten; Schmidt, Mikkel N

    2012-01-01

    Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....

  7. Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

    Science.gov (United States)

    Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

    2016-01-01

    We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

  8. Bayesian Dark Knowledge

    NARCIS (Netherlands)

    A. Korattikara; V. Rathod; K. Murphy; M. Welling

    2015-01-01

    We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple ap

  9. Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA.

    Science.gov (United States)

    Herring, Carlie E; Stinson, Jonah; Landis, Wayne G

    2015-10-01

    Many coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN-RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN-RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water. PMID:25845995

  10. Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

    Science.gov (United States)

    Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

    2014-05-01

    Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of

  11. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

    Directory of Open Access Journals (Sweden)

    Salvador Dura-Bernal

    Full Text Available Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance. Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom

  12. 基于人工蜂群算法的贝叶斯网络结构学习%Structure learning of Bayesian networks by use of the artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    张平; 刘三阳; 朱明敏

    2014-01-01

    从数据集中学习贝叶斯网络结构是一个NP难问题。针对此问题提出基于遗传算子的人工蜂群算法。首先,将贝叶斯网络结构映射为一种二进制编码;其次,根据贝叶斯网络的结构特点,设计了蜜源的更新策略,从而将学习贝叶斯网络结构的过程转化为蜂群寻找最优蜜源的过程。实验结果表明,该算法应用于贝叶斯网络结构学习中的有效性。%The learning structure of Bayesian networks from a data set is an NP-hard problem .To deal with this problem , an artificial bee colony algorithm based on genetic operators is proposed in this paper .The structure of the Bayesian network is mapped to binary encoding , and the updated strategy of nectar is designed according to the characteristics of the Bayesian network structure .Thus the process of structure learning of the Bayesian network is transformed into the process of the bee colony finding the optimal nectar .The experimental results show that the al-gorithm is valid in the structure learning of Bayesian networks .

  13. Bayesian biostatistics

    CERN Document Server

    Lesaffre, Emmanuel

    2012-01-01

    The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd

  14. 基于贝叶斯网络的住宅防水风险研究%Housing Waterproof Risk Based on Bayesian Network

    Institute of Scientific and Technical Information of China (English)

    贺兆泽; 莫俊文

    2015-01-01

    针对目前住宅小区经常出现漏水事故而没有成熟的方法找到其根源的问题,基于贝叶斯网络理论对住宅防水问题进行了研究。经过分析漏水事故的发生机理,结合相关领域专家的施工经验,确定主要的致险因素。在此基础上建立住宅防水风险贝叶斯网络结构,通过贝叶斯网络正向推理和反向推理,分别得出住宅漏水的概率和各个根节点的后验概率,经过排序得到关键致险因素。结合故障树理论,将各个底事件的重要度排序与贝叶斯网络得出的关键致险因素取交集,得到最终的关键致险因素。应用该方法可以对住宅漏水的问题进行有效的施工控制,指导性强。%In view of the current residential problem of water leakage accident,there is no mature method to find its origin. Based on Bayesian network theory, the problem of housing waterproof is studied. Through mechanism analysis of water leakage accident, combing with experience of related experts,main risk factors are determined. Bayesian network of housing waterproof risk is established. Through the forward and backward reasoning of Bayesian network, the probability of residential water leakage and each posterior probability of root node can be obtained. At last, the key risk factors can be determined. Using the fault tree theory, the same assembly from Bayesian Network can be obtained. Finally the key risk factors are gotten. The method can be applied to residential water leakage problem.

  15. Planning the expansion of electrical transmission networks with evolutionary programming; Planeacion de la expansion de redes de transmision electrica con programacion evolucionaria

    Energy Technology Data Exchange (ETDEWEB)

    Ceciliano Meza, Jose Luis

    1997-12-31

    In this work it is presented for the first time in the literature the solution of the problem of the planning the expansion of an electrical transmission network (PERTE) with the use of the Evolutionary Programming. The evolutionary programming is a stochastic method of optimization with similar characteristics to the generic algorithms, but different. During the development of this work it is shown what each one of these two heuristic methods of optimization consist of. Additionally, the main characteristics of other two heuristic methods known in the literature are described (Tabu Searching and Simulated Annealing). These two methods together with the genetic algorithms and decomposition of Benders have also been used to solve the problem of planning PERTE. The operation of the proposed algorithm of evolutionary programming was tested in two networks of electrical transmission. The first case of test is a system which is known in the literature. The second case is a representative system of the electrical transmission network of Central America. The results obtained improve all the results shown when applying different heuristic methods of optimization (genetic algorithms, simulated annealing and Tabu searching) to solve the same problem. [Espanol] En este trabajo se presenta por primera vez en la literatura, la solucion del problema de la planeacion de expansion de una red de transmision electrica (PERTE) con el uso de la Programacion Evolucionaria. La programacion evolucionaria es un metodo estocastico de optimizacion con caracteristicas similares a los algoritmos geneticos, pero diferente. Durante el desarrollo de este trabajo se muestra en que consiste cada uno de estos dos metodos heuristicos de optimizacion. Ademas, se describen las caracteristicas principales de otros dos metodos heuristicos conocidos en la literatura (busqueda Tabu y Recorrido Simulado). Estos dos metodos juntos con los algoritmos geneticos y descomposicion de Benders tambien han sido

  16. Bayesian statistics

    OpenAIRE

    Draper, D.

    2001-01-01

    © 2012 Springer Science+Business Media, LLC. All rights reserved. Article Outline: Glossary Definition of the Subject and Introduction The Bayesian Statistical Paradigm Three Examples Comparison with the Frequentist Statistical Paradigm Future Directions Bibliography

  17. A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems

    Directory of Open Access Journals (Sweden)

    Márcio das Chagas Moura

    2008-08-01

    Full Text Available In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.Neste trabalho, é proposto um modelo baseado na integração entre processos semi-Markovianos e redes Bayesianas para avaliação da disponibilidade de sistemas tolerantes à falha. Esta integração resulta em um modelo estocástico híbrido o qual é capaz de representar as características dinâmicas de um sistema assim como tratar as relações de causa e efeito entre fatores externos tais como condições ambientais e operacionais. Além disso, o modelo híbrido permite avaliar a propagação de incerteza sobre a disponibilidade do sistema. É também proposto um procedimento numérico para a solução das equações de probabilidade de estado de processos semi-Markovianos descritos por taxas de transição. Tal procedimento numérico é baseado na aplicação de transformadas de Laplace que são invertidas pelo método de quadratura Gaussiana conhecido como Gauss Legendre. O modelo híbrido e procedimento numérico são ilustrados por meio de um exemplo de aplicação no contexto de

  18. A Study on the Quantitative Assessment Method of Software Requirement Documents Using Software Engineering Measures and Bayesian Belief Networks

    Energy Technology Data Exchange (ETDEWEB)

    Eom, Heung Seop; Kang, Hyun Gook; Park, Ki Hong; Kwon, Kee Choon; Chang, Seung Cheol [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    2005-07-01

    One of the major challenges in using the digital systems in a NPP is the reliability estimation of safety critical software embedded in the digital safety systems. Precise quantitative assessment of the reliability of safety critical software is nearly impossible, since many of the aspects to be considered are of qualitative nature and not directly measurable, but they have to be estimated for a practical use. Therefore an expert's judgment plays an important role in estimating the reliability of the software embedded in safety-critical systems in practice, because they can deal with all the diverse evidence relevant to the reliability and can perform an inference based on the evidence. But, in general, the experts' way of combining the diverse evidence and performing an inference is usually informal and qualitative, which is hard to discuss and will eventually lead to a debate about the conclusion. We have been carrying out research on a quantitative assessment of the reliability of safety critical software using Bayesian Belief Networks (BBN). BBN has been proven to be a useful modeling formalism because a user can represent a complex set of events and relationships in a fashion that can easily be interpreted by others. In the previous works we have assessed a software requirement specification of a reactor protection system by using our BBN-based assessment model. The BBN model mainly employed an expert's subjective probabilities as inputs. In the process of assessing the software requirement documents we found out that the BBN model was excessively dependent on experts' subjective judgments in a large part. Therefore, to overcome the weakness of our methodology we employed conventional software engineering measures into the BBN model as shown in this paper. The quantitative relationship between the conventional software measures and the reliability of software were not identified well in the past. Then recently there appeared a few

  19. Topics in Bayesian statistics and maximum entropy

    International Nuclear Information System (INIS)

    Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)

  20. Bayesian Control for Concentrating Mixed Nuclear Waste

    OpenAIRE

    Welch, Robert L.; Smith, Clayton

    2013-01-01

    A control algorithm for batch processing of mixed waste is proposed based on conditional Gaussian Bayesian networks. The network is compiled during batch staging for real-time response to sensor input.