WorldWideScience

Sample records for bayesian network graphical

  1. 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...... 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...

  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. Adaptive Dynamic Bayesian Networks

    Energy Technology Data Exchange (ETDEWEB)

    Ng, B M

    2007-10-26

    A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.

  4. 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++.

  5. 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++

  6. 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

  7. Quantum Inference on Bayesian Networks

    Science.gov (United States)

    Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

    2014-03-01

    Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

  8. Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

    OpenAIRE

    Mansinghka, Vikash K.; Kulkarni, Tejas D.; Perov, Yura N.; Tenenbaum, Joshua B.

    2013-01-01

    The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics program...

  9. Extracting protein regulatory networks with graphical models.

    Science.gov (United States)

    Grzegorczyk, Marco

    2007-09-01

    During the last decade the development of high-throughput biotechnologies has resulted in the production of exponentially expanding quantities of biological data, such as genomic and proteomic expression data. One fundamental problem in systems biology is to learn the architecture of biochemical pathways and regulatory networks in an inferential way from such postgenomic data. Along with the increasing amount of available data, a lot of novel statistical methods have been developed and proposed in the literature. This article gives a non-mathematical overview of three widely used reverse engineering methods, namely relevance networks, graphical Gaussian models, and Bayesian networks, whereby the focus is on their relative merits and shortcomings. In addition the reverse engineering results of these graphical methods on cytometric protein data from the RAF-signalling network are cross-compared via AUROC scatter plots. PMID:17893851

  10. Benchmarking dynamic Bayesian network structure learning algorithms

    OpenAIRE

    Trabelsi, Ghada; Leray, Philippe; Ben Ayed, Mounir; Alimi, Adel

    2012-01-01

    Dynamic Bayesian Networks (DBNs) are probabilistic graphical models dedicated to modeling multivariate time series. Two-time slice BNs (2-TBNs) are the most current type of these models. Static BN structure learning is a well-studied domain. Many approaches have been proposed and the quality of these algorithms has been studied over a range of di erent standard networks and methods of evaluation. To the best of our knowledge, all studies about DBN structure learning use their own benchmarks a...

  11. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

     Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, 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...

  12. Extended Bayesian Information Criteria for Gaussian Graphical Models

    CERN Document Server

    Foygel, Rina

    2010-01-01

    Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications. For the problem of recovering the graphical structure, information criteria provide useful optimization objectives for algorithms searching through sets of graphs or for selection of tuning parameters of other methods such as the graphical lasso, which is a likelihood penalization technique. In this paper we establish the consistency of an extended Bayesian information criterion for Gaussian graphical models in a scenario where both the number of variables p and the sample size n grow. Compared to earlier work on the regression case, our treatment allows for growth in the number of non-zero parameters in the true model, which is necessary in order to cover connected graphs. We demonstrate the performance of this criterion on simulated data when used in conjunction with the graphical lasso, and verify that the criterion indeed performs better than either cross-validation or the ordi...

  13. 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...

  14. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

    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...

  15. Using imsets for learning Bayesian networks

    Czech Academy of Sciences Publication Activity Database

    Vomlel, Jiří; Studený, Milan

    Praha : UTIA AV ČR, 2007 - (Kroupa, T.; Vejnarová, J.), s. 178-189 [Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./. Liblice (CZ), 15.09.2007-18.09.2007] R&D Projects: GA MŠk(CZ) 1M0572 Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian networks * artificial intelligence * probabilistic graphical models * machine learning Subject RIV: BB - Applied Statistics, Operational Research

  16. 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

  17. 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...

  18. 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...

  19. Modelling of JET diagnostics using Bayesian Graphical Models

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, J. [IPP Greifswald, Greifswald (Germany); Ford, O. [Imperial College, London (United Kingdom); McDonald, D.; Hole, M.; Nessi, G. von; Meakins, A.; Brix, M.; Thomsen, H.; Werner, A.; Sirinelli, A.

    2011-07-01

    The mapping between physics parameters (such as densities, currents, flows, temperatures etc) defining the plasma 'state' under a given model and the raw observations of each plasma diagnostic will 1) depend on the particular physics model used, 2) is inherently probabilistic, from uncertainties on both observations and instrumental aspects of the mapping, such as calibrations, instrument functions etc. A flexible and principled way of modelling such interconnected probabilistic systems is through so called Bayesian graphical models. Being an amalgam between graph theory and probability theory, Bayesian graphical models can simulate the complex interconnections between physics models and diagnostic observations from multiple heterogeneous diagnostic systems, making it relatively easy to optimally combine the observations from multiple diagnostics for joint inference on parameters of the underlying physics model, which in itself can be represented as part of the graph. At JET about 10 diagnostic systems have to date been modelled in this way, and has lead to a number of new results, including: the reconstruction of the flux surface topology and q-profiles without any specific equilibrium assumption, using information from a number of different diagnostic systems; profile inversions taking into account the uncertainties in the flux surface positions and a substantial increase in accuracy of JET electron density and temperature profiles, including improved pedestal resolution, through the joint analysis of three diagnostic systems. It is believed that the Bayesian graph approach could potentially be utilised for very large sets of diagnostics, providing a generic data analysis framework for nuclear fusion experiments, that would be able to optimally utilize the information from multiple diagnostics simultaneously, and where the explicit graph representation of the connections to underlying physics models could be used for sophisticated model testing. This

  20. Inference in hybrid Bayesian networks

    DEFF Research Database (Denmark)

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

    2009-01-01

    and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

  1. 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

  2. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....

  3. Bayesian networks with applications in reliability analysis

    OpenAIRE

    Langseth, Helge

    2002-01-01

    A common goal of the papers in this thesis is to propose, formalize and exemplify the use of Bayesian networks as a modelling tool in reliability analysis. The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and ...

  4. 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...

  5. 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 ...

  6. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

    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 eva...

  7. Graphical Independence Networks with the gRain Package for R

    Directory of Open Access Journals (Sweden)

    Soren Hojsgaard

    2012-01-01

    Full Text Available In this paper we present the R package gRain for propagation in graphical independence networks (for which Bayesian networks is a special instance. The paper includes a description of the theory behind the computations. The main part of the paper is an illustration of how to use the package. The paper also illustrates how to turn a graphical model and data into an independence network

  8. 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 ...

  9. 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.

  10. 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.

  11. Factorial graphical lasso for dynamic networks

    CERN Document Server

    Wit, E C

    2012-01-01

    Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. There are many aspects of dynamical networks that require statistical considerations. In this paper we focus on determining network structure. Estimating dynamic networks is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is bigger than the number of observations. However, a characteristic of many networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and therefore sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. However, the literature has focussed on static networks, which lack specific temporal constraints. We propose a structured Gaussian dynamical graphical model, where structures can consist of specific time dynamics, known presence or abse...

  12. 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

  13. Graphical Model Theory for Wireless Sensor Networks

    International Nuclear Information System (INIS)

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm

  14. Graphical Model Theory for Wireless Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Davis, William B.

    2002-12-08

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.

  15. 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.

  16. Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models

    OpenAIRE

    Gruber, Lutz F.; West, Mike

    2016-01-01

    The recently introduced class of simultaneous graphical dynamic linear models (SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting to higher-dimensional time series. This paper advances the methodology of SGDLMs, developing and embedding a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. The advances include developments in Bayesian computation for scalability, and a case study in exploring the resu...

  17. Bayesian Analysis of Marginal Log-Linear Graphical Models for Three Way Contingency Tables

    OpenAIRE

    Ntzoufras, Ioannis; Tarantola, Claudia

    2008-01-01

    This paper deals with the Bayesian analysis of graphical models of marginal independence for three way contingency tables. We use a marginal log-linear parametrization, under which the model is defined through suitable zero-constraints on the interaction parameters calculated within marginal distributions. We undertake a comprehensive Bayesian analysis of these models, involving suitable choices of prior distributions, estimation, model determination, as well as the allied computational issue...

  18. Bayesian Analysis of Graphical Models of Marginal Independence for Three Way Contingency Tables

    OpenAIRE

    Tarantola, Claudia; Ntzoufras, Ioannis

    2012-01-01

    This paper deals with the Bayesian analysis of graphical models of marginal independence for three way contingency tables. Each marginal independence model corresponds to a particular factorization of the cell probabilities and a conjugate analysis based on Dirichlet prior can be performed. We illustrate a comprehensive Bayesian analysis of such models, involving suitable choices of prior parameters, estimation, model determination, as well as the allied computational issues. The posterior di...

  19. Bayesian Network Models for Adaptive Testing

    Czech Academy of Sciences Publication Activity Database

    Plajner, Martin; Vomlel, Jiří

    Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf

  20. 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.

  1. 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...

  2. 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

  3. Optimizing the Amount of Models Taken into Consideration During Model Selection in Bayesian Networks

    OpenAIRE

    Castelo, J.R.; Siebes, Arno

    1999-01-01

    Graphical model selection from data embodies several difficulties. Among them, it is specially challenging the size of the sample space of models on which one should carry out model selection, even considering only a modest amount of variables. This becomes more severe when one works on those graphical models where some variables may be responses to other. This is the case of Bayesian Networks that are modeled by acyclic digraphs. In this paper we try to reduce the amount of models taken into...

  4. 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...

  5. 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

  6. From least squares to multilevel modeling: A graphical introduction to Bayesian inference

    Science.gov (United States)

    Loredo, Thomas J.

    2016-01-01

    This tutorial presentation will introduce some of the key ideas and techniques involved in applying Bayesian methods to problems in astrostatistics. The focus will be on the big picture: understanding the foundations (interpreting probability, Bayes's theorem, the law of total probability and marginalization), making connections to traditional methods (propagation of errors, least squares, chi-squared, maximum likelihood, Monte Carlo simulation), and highlighting problems where a Bayesian approach can be particularly powerful (Poisson processes, density estimation and curve fitting with measurement error). The "graphical" component of the title reflects an emphasis on pictorial representations of some of the math, but also on the use of graphical models (multilevel or hierarchical models) for analyzing complex data. Code for some examples from the talk will be available to participants, in Python and in the Stan probabilistic programming language.

  7. 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.

  8. 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...

  9. 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.

  10. 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...... mechanism efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....

  11. Centralized Bayesian reliability modelling with sensor networks

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil; Sečkárová, Vladimíra

    2013-01-01

    Roč. 19, č. 5 (2013), s. 471-482. ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant ostatní: GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf

  12. Characteristic imsets for learning Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Hemmecke, R.; Lindner, S.; Studený, Milan

    2012-01-01

    Roč. 53, č. 9 (2012), s. 1336-1349. ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

  13. 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.

  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. Parameterized Complexity Results for Exact Bayesian Network Structure Learning

    OpenAIRE

    Sebastian Ordyniak; Stefan Szeider

    2014-01-01

    Bayesian network structure learning is the notoriously difficult problem of discovering a Bayesian network that optimally represents a given set of training data. In this paper we study the computational worst-case complexity of exact Bayesian network structure learning under graph theoretic restrictions on the (directed) super-structure. The super-structure is an undirected graph that contains as subgraphs the skeletons of solution networks. We introduce the directed super-structure as a nat...

  16. 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.

  17. 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.

  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. Characteristic imset: a simple algebraic representative of a Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Hemmecke, R.; Lindner, S.

    Helsinki : HIIT Publications, 2010 - (Myllymaki, P.; Roos, T.; Jaakkola, T.), s. 257-264 ISBN 978-952-60-3314-3. ISSN 1458-946X. [5th European Workshop on Probabilistic Graphical Models. Helsinki (FI), 13.09.2010-15.09.2010] R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional research plan: CEZ:AV0Z10750506 Keywords : characteristic imset * Bayesian network strucutre * essential graph Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2010/MTR/studeny-characteristic imset a simple algebraic representative of a bayesian network structure.pdf

  20. Integer linear programming approach to learning Bayesian network structure: towards the essential graph

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan

    Granada : DESCAI, University of Granada, 2012, s. 307-314. ISBN 978-84-15536-57-4. [6th European Workshop on Probabilistic Graphical Models (PGM). Granada (ES), 19.09.2012-21.09.2012] R&D Projects: GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * characteristic imset * essential graph Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2012/MTR/studeny-integer linear programming approach to learning Bayesian network structure towards the essential graph.pdf

  1. 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.

  2. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

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

    2009-01-01

    (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.......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...

  3. Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models.

    Science.gov (United States)

    Balfer, Jenny; Bajorath, Jürgen

    2014-09-22

    Supervised machine learning models are widely used in chemoinformatics, especially for the prediction of new active compounds or targets of known actives. Bayesian classification methods are among the most popular machine learning approaches for the prediction of activity from chemical structure. Much work has focused on predicting structure-activity relationships (SARs) on the basis of experimental training data. By contrast, only a few efforts have thus far been made to rationalize the performance of Bayesian or other supervised machine learning models and better understand why they might succeed or fail. In this study, we introduce an intuitive approach for the visualization and graphical interpretation of naïve Bayesian classification models. Parameters derived during supervised learning are visualized and interactively analyzed to gain insights into model performance and identify features that determine predictions. The methodology is introduced in detail and applied to assess Bayesian modeling efforts and predictions on compound data sets of varying structural complexity. Different classification models and features determining their performance are characterized in detail. A prototypic implementation of the approach is provided. PMID:25137527

  4. A Bayesian Networks approach to Operational Risk

    Science.gov (United States)

    Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.

    2010-04-01

    A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.

  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 is...... set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...

  6. Sensor fault diagnosis using Bayesian belief networks

    International Nuclear Information System (INIS)

    This paper describes a method based on Bayesian belief networks (BBNs) sensor fault detection, isolation, classification, and accommodation (SFDIA). For this purpose, a BBN uses three basic types of nodes to represent the information associated with each sensor: (1) sensor-reading nodes that represent the mechanisms by which the information is communicated to the BBN, (2) sensor-status nodes that convey the status of the corresponding sensors at any given time, and (3) process-variable nodes that are a conceptual representation of the actual values of the process variables, which are unknown

  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. Fuzzy Naive Bayesian for constructing regulated network with weights.

    Science.gov (United States)

    Zhou, Xi Y; Tian, Xue W; Lim, Joon S

    2015-01-01

    In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN. PMID:26405944

  10. 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.

  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. Release and Dynamic Management of CAD Network Graphics Library

    Institute of Scientific and Technical Information of China (English)

    XU Mao-feng; ZHANG Yi; LIU Fang; LI Ai-jun

    2003-01-01

    We aimed at the release and dynamic management of CAD network graphics library (NGL). The characteristics of realization on network of CAD graphics are analysed, while the existing problems of the presenting share methods of graphics file are also discussed. Release and dynamic management are accomplished with the B/S combined with C/S as well as the file organization based on attribute information, which have essential practical sense to the establishment of CAD NGL, share and cooperation in tech-design as well as the distance education of engineering graphics.

  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. Survey for Wavelet Bayesian Network Image Denoising

    Directory of Open Access Journals (Sweden)

    Pallavi Sharma,

    2014-04-01

    Full Text Available In now days, wavelet-based image denoising method, which extends a recently emerged ―geometrical‖ Bayesian framework. The new scheme combines three criteria for distinctive theoretically useful coefficients from noise: coefficient magnitudes, their advancement across scales and spatial clustering of bulky coefficients close to image edges. These three criteria are united in a Bayesian construction. The spatial clustering properties are expressed in a earlier model. The statistical properties regarding coefficient magnitudes and their development crossways scales are expressed in a joint conditional model. We address the image denoising difficulty, where zero-mean white and homogeneous Gaussian additive noise is to be uninvolved from a given image. We employ the belief propagation (BP algorithm, which estimates a coefficient based on every one the coefficients of a picture, as the maximum-a-posterior (MAP estimator to derive the denoised wavelet coefficients. We illustrate that if the network is a spanning tree, the customary BP algorithm can achieve MAP estimation resourcefully. Our research consequences show that, in conditions of the peak-signal-to-noise-ratio and perceptual superiority, the planned approach outperforms state-of-the-art algorithms on a number of images, mostly in the textured regions, with a range of amounts of white Gaussian noise.

  15. 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.

  16. RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

    Science.gov (United States)

    Höhna, Sebastian; Landis, Michael J; Heath, Tracy A; Boussau, Bastien; Lartillot, Nicolas; Moore, Brian R; Huelsenbeck, John P; Ronquist, Fredrik

    2016-07-01

    Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]. PMID:27235697

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. A Flexible Software System for Learning Bayesian Networks from data

    OpenAIRE

    Aabakken, Trond

    2007-01-01

    Bayesian networks, also referred to as belief networks, originates from the artificial intelligence field where they were used to reason about uncertain knowledge. They differ from other knowledge representation schemes as they constitute a model of the environment rather than a model of the reasoning process. Among the Bayesian networks' main assets is that they offer a sound methodology for combining (a priori) information a domain expert may have with information available in databases. I...

  3. Introduction to Graphical Modelling

    CERN Document Server

    Scutari, Marco

    2010-01-01

    The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random fields) and Bayesian networks, which comprise most past and current literature on graphical models. In the second part we will review some applications of graphical models in systems biology.

  4. 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.

  5. Graphical reduction of reaction networks by linear elimination of species

    DEFF Research Database (Denmark)

    Sáez, Meritxell; Wiuf, Carsten; Feliu, Elisenda

    2015-01-01

    We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties of the...

  6. 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.

  7. 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...

  8. 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...

  9. 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...

  10. Bayesian Network Structure Learning from Limited Datasets through Graph Evolution

    OpenAIRE

    Tonda, Alberto; Lutton, Evelyne; Reuillon, Romain; Squillero, Giovanni; Wuillemin, Pierre-Henri

    2012-01-01

    Bayesian networks are stochastic models, widely adopted to encode knowledge in several fields. One of the most interesting features of a Bayesian network is the possibility of learning its structure from a set of data, and subsequently use the resulting model to perform new predictions. Structure learning for such models is a NP-hard problem, for which the scientific community developed two main approaches: score-and-search metaheuristics, often evolutionary-based, and dependency-analysis det...

  11. On polyhedral approximations of polytopes for learning Bayesian networks

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.C.

    2013-01-01

    Roč. 4, č. 1 (2013), s. 59-92. ISSN 1309-3452 R&D Projects: GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : Bayesian network structure * integer programming * standard imset * characteristic imset * LP relaxation Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2013/MTR/studeny-on polyhedral approximations of polytopes for learning bayesian networks.pdf

  12. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

    OpenAIRE

    Hernández-Lobato, José Miguel; Adams, Ryan P.

    2015-01-01

    Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian ...

  13. Uncertainty management using bayesian networks in student knowledge diagnosis

    OpenAIRE

    Adina COCU; Diana STEFANESCU

    2005-01-01

    In intelligent tutoring systems, student or user modeling implies dealing with imperfect and uncertain knowledge. One of the artificial intelligence techniques used for uncertainty management is that of Bayesian networks. This paradigm is recommended in the situation when exist dependencies between data and qualitative information about these data. In this work we present a student knowledge diagnosis model based on representation with Bayesian networks. The educational system incorporate a m...

  14. Strategies for Generating Micro Explanations for Bayesian Belief Networks

    OpenAIRE

    Sember, Peter; Zukerman, Ingrid

    2013-01-01

    Bayesian Belief Networks have been largely overlooked by Expert Systems practitioners on the grounds that they do not correspond to the human inference mechanism. In this paper, we introduce an explanation mechanism designed to generate intuitive yet probabilistically sound explanations of inferences drawn by a Bayesian Belief Network. In particular, our mechanism accounts for the results obtained due to changes in the causal and the evidential support of a node.

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

    OpenAIRE

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2010-01-01

    This paper presents how Bayesian networks can be used to make optimal decisions for repairs of offshore wind turbines. The Bayesian network is an efficient tool for updating a deterioration model whenever new information becomes available from inspections/monitoring. The optimal decision is found 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 Ba...

  16. 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…

  17. 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.

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

    OpenAIRE

    Yonghui Cao

    2013-01-01

    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.

  19. 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.

  20. 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.

  1. Teaching Heat Exchanger Network Synthesis Using Interactive Microcomputer Graphics.

    Science.gov (United States)

    Dixon, Anthony G.

    1987-01-01

    Describes the Heat Exchanger Network Synthesis (HENS) program used at Worcester Polytechnic Institute (Massachusetts) as an aid to teaching the energy integration step in process design. Focuses on the benefits of the computer graphics used in the program to increase the speed of generating and changing networks. (TW)

  2. Inference Networks for Sequential Monte Carlo in Graphical Models

    OpenAIRE

    Paige, Brooks; Wood, Frank

    2016-01-01

    We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as proposal distributions in sequential Monte Carlo methods. We describe a procedure for constructing and learning a structured neural network which represents an inverse factorization of the graphical model, resulting in a conditional density estimator that takes as input particular values of the observed random variables...

  3. Grouped graphical Granger modeling for gene expression regulatory networks discovery

    OpenAIRE

    Lozano, Aurélie C.; Abe, Naoki; Yan LIU; Rosset, Saharon

    2009-01-01

    We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of ‘Granger causality’ to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem—the grou...

  4. Control of Complex Systems Using Bayesian Networks and Genetic Algorithm

    CERN Document Server

    Marwala, Tshilidzi

    2007-01-01

    A method based on Bayesian neural networks and genetic algorithm is proposed to control the fermentation process. The relationship between input and output variables is modelled using Bayesian neural network that is trained using hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used to change input variables so that the output variables are as close to the desired target as possible without the loss of confidence level on the prediction that the neural network gives. The proposed procedure is found to reduce the distance between the desired target and measured outputs significantly.

  5. 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...

  6. Flood quantile estimation at ungauged sites by Bayesian networks

    Science.gov (United States)

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

    2012-04-01

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

  7. 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

  8. Graphical user interface for wireless sensor networks simulator

    Science.gov (United States)

    Paczesny, Tomasz; Paczesny, Daniel; Weremczuk, Jerzy

    2008-01-01

    Wireless Sensor Networks (WSN) are currently very popular area of development. It can be suited in many applications form military through environment monitoring, healthcare, home automation and others. Those networks, when working in dynamic, ad-hoc model, need effective protocols which must differ from common computer networks algorithms. Research on those protocols would be difficult without simulation tool, because real applications often use many nodes and tests on such a big networks take much effort and costs. The paper presents Graphical User Interface (GUI) for simulator which is dedicated for WSN studies, especially in routing and data link protocols evaluation.

  9. Sparse time series chain graphical models for reconstructing genetic networks

    NARCIS (Netherlands)

    Abegaz, Fentaw; Wit, Ernst

    2013-01-01

    We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of co

  10. 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.

  11. Logical Graphics Design Technique for Drawing Distribution Networks

    Science.gov (United States)

    Al-A`Ali, Mansoor

    Electricity distribution networks normally consist of tens of primary feeders, thousands of substations and switching stations spread over large geographical areas and thus require a complex system in order to manage them properly from within the distribution control centre. We show techniques for using Delphi Object Oriented components to automatically generate, display and manage graphically and logically the circuits of the network. The graphics components are dynamically interactive and thus the system allows switching operations as well as displays. The object oriented approach was developed to replace an older system, which used Microstation with MDL as the programming language and ORACLE as the DBMS. Before this, the circuits could only be displayed schematically, which has many inherent problems in speed and readability of large displays. Schematic graphics displays were cumbersome when adding or deleting stations; this problem is now resolved using our approach by logically generating the graphics from the database connectivity information. This paper demonstrates the method of designing these Object Oriented components and how they can be used in specially created algorithms to generate the necessary interactive graphics. Four different logical display algorithms were created and in this study we present samples of the four different outputs of these algorithms which prove that distribution engineers can work with logical display of the circuits which are aimed to speed up the switching operations and for better clarity of the display.

  12. 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.

  13. 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.

  14. Dynamic Bayesian Networks for Cue Integration

    OpenAIRE

    Paul Maier; Frederike Petzschner

    2012-01-01

    If we want to understand how humans use contextual cues to solve tasks such as estimating distances from optic flow during path integration, our models need to represent the available information and formally describe how these representations are processed. In particular the temporal dynamics need to be incorporated, since it has been shown that humans exploit short-term experience gained in previous trials (Petzschner und Glasauer, 2011). Existing studies often use a Bayesian approach to mo...

  15. 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.

  16. A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism

    Directory of Open Access Journals (Sweden)

    Carey Vincent J

    2009-05-01

    Full Text Available Abstract Background Graphical models (e.g., Bayesian networks have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs in genes. Results Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1 construction of a graphical Gaussian model (GGM based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2 extraction of a subnetwork of genes directly linked to a target candidate gene of interest; 3 identification of cis-acting regulatory variants for the genes composing the subnetwork; and 4 evaluating the identified cis-acting variants for trans-acting regulatory effects of the target candidate gene. This approach identifies significant gene-gene and gene-SNP associations not solely on the basis of gene co-expression but rather through whole-network modeling. We demonstrate the method by building two complex gene-SNP networks around Interferon Receptor 12B2 (IL12RB2 and Interleukin 1B (IL1B, two biologic candidates in asthma pathogenesis, using 534,290 genotyped variants and gene expression data on 22,177 genes from total RNA derived from peripheral blood CD4+ lymphocytes from 154 asthmatics. Conclusion Our results suggest that graphical models based on integrative genomic data are computationally efficient, work well with small samples, and can describe complex interactions among genes and polymorphisms that could not be identified by pair-wise association testing.

  17. 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…

  18. 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…

  19. 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.

  20. 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

  1. Speeding up the MATLAB complex networks package using graphic processors

    Institute of Scientific and Technical Information of China (English)

    Zhang Bai-Da; Tang Yu-Hua; Wu Jun-Jie; Li Xin

    2011-01-01

    The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously At present,it is believed that statistics is a suitable method to analyse networks with millions,or more,of vertices. The MATLAB language,with its mass of statistical functions,is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units (GPU). This paper presents the strategies and performance of the GPU implementation of a complex networks package,and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations,GPU can achieve a speedup of,on average,11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.

  2. Graphical tools for network meta-analysis in STATA.

    Directory of Open Access Journals (Sweden)

    Anna Chaimani

    Full Text Available Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.

  3. Speeding up the MATLAB complex networks package using graphic processors

    International Nuclear Information System (INIS)

    The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units (GPU). This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3×. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research. (interdisciplinary physics and related areas of science and technology)

  4. 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...

  5. 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...

  6. Dynamic Resource Access Using Graphical Game in Asymmetric Wireless Networks

    Directory of Open Access Journals (Sweden)

    Fangwei Li

    2013-08-01

    Full Text Available In order to improve the resource utilization in asymmetric wireless networks, a novel dynamic resource access algorithm was presented. As the asymmetry of information and the locality of users' actions in distributed wireless networks, the resource access problem was expressed as a simple graphical game model. Let the graphic topology indicate the internal game structure of the realistic environment. Then the Nash equilibrium was got by minimizing the individual regret instead of the system regret. The proposed algorithm realized efficient resource access through exchanging the active information and regret in the competitive community. Theoretical analysis and simulation results show that the algorithm can converge to a suitable pure strategy Nash equilibrium point quickly with less amount of calculation, avoids conflict effectively, and improves the system capacity and power utilization especially in the condition of insufficient resources

  7. Comparison of Lauritzen-Spiegelhalter and successive restrictions algorithms for computing probability distributions in Bayesian networks

    Science.gov (United States)

    Smail, Linda

    2016-06-01

    The basic task of any probabilistic inference system in Bayesian networks is computing the posterior probability distribution for a subset or subsets of random variables, given values or evidence for some other variables from the same Bayesian network. Many methods and algorithms have been developed to exact and approximate inference in Bayesian networks. This work compares two exact inference methods in Bayesian networks-Lauritzen-Spiegelhalter and the successive restrictions algorithm-from the perspective of computational efficiency. The two methods were applied for comparison to a Chest Clinic Bayesian Network. Results indicate that the successive restrictions algorithm shows more computational efficiency than the Lauritzen-Spiegelhalter algorithm.

  8. A Gaussian graphical model approach to climate networks

    Science.gov (United States)

    Zerenner, Tanja; Friederichs, Petra; Lehnertz, Klaus; Hense, Andreas

    2014-06-01

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  9. A Gaussian graphical model approach to climate networks

    International Nuclear Information System (INIS)

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately

  10. A Gaussian graphical model approach to climate networks

    Energy Technology Data Exchange (ETDEWEB)

    Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)

    2014-06-15

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  11. Theory-independent limits on correlations from generalized Bayesian networks

    International Nuclear Information System (INIS)

    Bayesian networks provide a powerful tool for reasoning about probabilistic causation, used in many areas of science. They are, however, intrinsically classical. In particular, Bayesian networks naturally yield the Bell inequalities. Inspired by this connection, we generalize the formalism of classical Bayesian networks in order to investigate non-classical correlations in arbitrary causal structures. Our framework of ‘generalized Bayesian networks’ replaces latent variables with the resources of any generalized probabilistic theory, most importantly quantum theory, but also, for example, Popescu–Rohrlich boxes. We obtain three main sets of results. Firstly, we prove that all of the observable conditional independences required by the classical theory also hold in our generalization; to obtain this, we extend the classical d-separation theorem to our setting. Secondly, we find that the theory-independent constraints on probabilities can go beyond these conditional independences. For example we find that no probabilistic theory predicts perfect correlation between three parties using only bipartite common causes. Finally, we begin a classification of those causal structures, such as the Bell scenario, that may yield a separation between classical, quantum and general-probabilistic correlations. (paper)

  12. 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.

  13. 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

  14. Graphical Features of Functional Genes in Human Protein Interaction Network.

    Science.gov (United States)

    Wang, Pei; Chen, Yao; Lu, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  15. 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.

  16. Graphical Models with R

    DEFF Research Database (Denmark)

    Højsgaard, Søren; Edwards, David; Lauritzen, Steffen

    of these software developments have taken place within the R community, either in the form of new packages or by providing an R ingerface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition......Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many......, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data...

  17. 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...

  18. 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.

  19. 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.

  20. Uncertainty management using bayesian networks in student knowledge diagnosis

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2005-12-01

    Full Text Available In intelligent tutoring systems, student or user modeling implies dealing with imperfect and uncertain knowledge. One of the artificial intelligence techniques used for uncertainty management is that of Bayesian networks. This paradigm is recommended in the situation when exist dependencies between data and qualitative information about these data. In this work we present a student knowledge diagnosis model based on representation with Bayesian networks. The educational system incorporate a multimedia interface for accomplishes the testing tools. The results of testing sessions are represented and interpreted with probability theory in order to ensure an adapted support for the student. The aims of the computer assisted application that contains this diagnose module are to support the student in personalized learning process and errors explanation.

  1. 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.

  2. Which Factors Contributes to Resolving Coreference Chains with Bayesian Networks?

    OpenAIRE

    Weissenbacher, Davy; Sasaki, Yutaka

    2013-01-01

    This paper describes coreference chain resolution with Bayesian Networks. Several factors in the resolution of coreference chains may greatly affect the final performance. If the choice of machine learning algorithm and the features the learner relies on are largely addressed by the community, others factors implicated in the resolution, such as noisy features, anaphoricity resolution or the search windows, have been less studied, and their importance remains unclear. In this article, we desc...

  3. An algeraic approach to structural learning Bayesian networks

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan

    Paris: Editions EDK, 2006 - (Bouchon-Meunier, B.; Yager, R.), s. 2284-2291 ISBN 2-84254-112-X. [IMPU 2006. Paris (FR), 02.07.2006-07.07.2006] R&D Projects: GA ČR GA201/04/0393 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * data vector Subject RIV: BA - General Mathematics

  4. Bayesian Methods for Neural Networks and Related Models

    OpenAIRE

    Titterington, D.M.

    2004-01-01

    Models such as feed-forward neural networks and certain other structures investigated in the computer science literature are not amenable to closed-form Bayesian analysis. The paper reviews the various approaches taken to overcome this difficulty, involving the use of Gaussian approximations, Markov chain Monte Carlo simulation routines and a class of non-Gaussian but “deterministic” approximations called variational approximations.

  5. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

    OpenAIRE

    Doucet, Arnaud; De Freitas, Nando; Murphy, Kevin; Russell, Stuart

    2013-01-01

    Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a techni...

  6. Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules

    OpenAIRE

    Rolly Intan; Oviliani Yenty Yuliana; Dwi Kristanto

    2010-01-01

    Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy label...

  7. 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...

  8. Learning genetic epistasis using Bayesian network scoring criteria

    OpenAIRE

    Barmada M Michael; Neapolitan Richard E; Jiang Xia; Visweswaran Shyam

    2011-01-01

    Abstract Background Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) ep...

  9. A Bayesian Network Framework for Relational Shape Matching

    OpenAIRE

    Rangarajan, Anand; Coughlan, James; Yuille, Alan

    2003-01-01

    A Bayesian network formulation for relational shape matching is presented. The main advantage of the re- lational shape matching approach is the obviation of the non-rigid spatial mappings used by recent non-rigid matching approaches. The basic variables that need to be estimated in the relational shape matching objective function are the global rotation and scale and the lo- cal displacements and correspondences. The new Bethe free energy approach is used to estimate the pairwise co...

  10. Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks

    OpenAIRE

    Mohammad Momani; Subhash Challa; Rami Alhmouz

    2010-01-01

    This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary) and the data trust component (continuous) and proves that either component ...

  11. Fracture prediction of cardiac lead medical devices using Bayesian networks

    International Nuclear Information System (INIS)

    A novel Bayesian network methodology has been developed to enable the prediction of fatigue fracture of cardiac lead medical devices. The methodology integrates in-vivo device loading measurements, patient demographics, patient activity level, in-vitro fatigue strength measurements, and cumulative damage modeling techniques. Many plausible combinations of these variables can be simulated within a Bayesian network framework to generate a family of fatigue fracture survival curves, enabling sensitivity analyses and the construction of confidence bounds on reliability predictions. The method was applied to the prediction of conductor fatigue fracture near the shoulder for two market-released cardiac defibrillation leads which had different product performance histories. The case study used recently published data describing the in-vivo curvature conditions and the in-vitro fatigue strength. The prediction results from the methodology aligned well with the observed qualitative ranking of field performance, as well as the quantitative field survival from fracture. This initial success suggests that study of further extension of this method to other medical device applications is warranted. - Highlights: • A new method to simulate the fatigue experience of an implanted cardiac lead. • Fatigue strength and use conditions are incorporated within a Bayesian network. • Confidence bounds reflect the uncertainty in all input parameters. • A case study is presented using market released cardiac leads

  12. 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.

  13. 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.

  14. Recurrent Bayesian Reasoning in Probabilistic Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Grim, Jiří; Hora, Jan

    Vol. Part I. Berlin: Springer, 2007 - (Marques de Sá, J.; Alexandre, L.; Duch, W.; Mandic, D.), s. 129-138. (Lecture Notes in Computer Scinece. SL 1 - Theoretical Computer Science and General Issues. 4669). ISBN 3-540-74693-5. [International Conference on Artificial Neural Networks /17./. Porto (PT), 09.09.2007-13.09.2007] R&D Projects: GA MŠk 1M0572; GA ČR GA102/07/1594 EU Projects: European Commission(XE) 507752 - MUSCLE Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : neural networks * probabilistic approach * distribution mixtures Subject RIV: BD - Theory of Information

  15. 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

  16. 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)

  17. Filtering in hybrid dynamic Bayesian networks (center)

    DEFF Research Database (Denmark)

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

    framework outperform the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE (root-mean-square error). Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show 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...

  18. 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.

  19. 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 on...

  20. 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...... 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....... 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...

  1. 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 ...

  2. 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.

  3. 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.

  4. 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...

  5. Graphics processing unit-based alignment of protein interaction networks.

    Science.gov (United States)

    Xie, Jiang; Zhou, Zhonghua; Ma, Jin; Xiang, Chaojuan; Nie, Qing; Zhang, Wu

    2015-08-01

    Network alignment is an important bridge to understanding human protein-protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large-scale networks via sequential computing. In this study, the typical Hungarian-Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2-nearest neighbours (HGA-2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA-2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA-2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large-scale networks are considered. By using HGA-2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods. PMID:26243827

  6. 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. PMID:26945109

  7. 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...

  8. 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

  9. 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..

  10. Quantum-Like Bayesian Networks for Modeling Decision Making.

    Science.gov (United States)

    Moreira, Catarina; Wichert, Andreas

    2016-01-01

    In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoner's Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios. PMID:26858669

  11. 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.

  12. 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.

  13. A geometric view on learning Bayesian network structures

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Vomlel, Jiří; Hemmecke, R.

    2010-01-01

    Roč. 51, č. 5 (2010), s. 578-586. ISSN 0888-613X. [PGM 2008] R&D Projects: GA AV ČR(CZ) IAA100750603; GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : learning Bayesian networks * standard imset * inclusion neighborhood * geometric neighborhood * GES algorithm Subject RIV: BA - General Mathematics Impact factor: 1.679, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-0342804.pdf

  14. 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.

  15. Bayesian and neural networks for preliminary ship design

    DEFF Research Database (Denmark)

    Clausen, H. B.; Lützen, Marie; Friis-Hansen, Andreas; Bjørneboe, Nanna Katrine

    2001-01-01

    examples, the three methods are evaluated in terms of accuracy and limitations of use. For different types of ships, the methods provide information on the relations between length, breadth, height, draft, speed, displacement, block coefficient and loading capacity. Thus, useful tools are available to the...... 000 ships is acquired and various methods for derivation of empirical relations are employed. A regression analysis is carried out to fit functions to the data. Further, the data are used to learn Bayesian and neural networks to encode the relations between the characteristics. On the basis of...

  16. Risk-Based Operation and Maintenance Using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2011-01-01

    This paper describes how risk-based decision making can be used for maintenance planning of components exposed to degradation such as fatigue in offshore wind turbines. In fatigue models, large epistemic uncertainties are usually present. These can be reduced if monitoring results are used to...... update the models, and hereby a better basis for decision making is obtained. An application example shows how a Bayesian network model can be used as a tool for updating the model and assist in risk-based decision making....

  17. Learning ground CP-logic theories by means of Bayesian network techniques

    OpenAIRE

    Meert, Wannes; Struyf, Jan; Blockeel, Hendrik

    2007-01-01

    Causal relationships are present in many application domains. CP-logic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CP-theories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CP-logic theories and Bayesian networks, that is, the method applies Bayesian network learning techniques to learn a CP-theory in the form of an equivalent Bayesian net...

  18. Approach to the Correlation Discovery of Chinese Linguistic Parameters Based on Bayesian Method

    Institute of Scientific and Technical Information of China (English)

    WANG Wei(王玮); CAI LianHong(蔡莲红)

    2003-01-01

    Bayesian approach is an important method in statistics. The Bayesian belief network is a powerful knowledge representation and reasoning tool under the conditions of uncertainty.It is a graphics model that encodes probabilistic relationships among variables of interest. In this paper, an approach to Bayesian network construction is given for discovering the Chinese linguistic parameter relationship in the corpus.

  19. 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...

  20. 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.

  1. 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. ...

  2. 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.

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

    DEFF Research Database (Denmark)

    Antal, P.; Fannes, G.; Timmerman, D.; Moreau, Yves; Moor, B.

    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 infor...... performance of a Bayesian network for the classification of ovarian tumors from clinical data....

  4. Mean Field Variational Approximation for Continuous-Time Bayesian Networks

    CERN Document Server

    Cohn, Ido; Friedman, Nir; Kupferman, Raz

    2012-01-01

    Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact representation, inference in such models is intractable even in relatively simple structured networks. Here we introduce a mean field variational approximation in which we use a product of inhomogeneous Markov processes to approximate a distribution over trajectories. This variational approach leads to a globally consistent distribution, which can be efficiently queried. Additionally, it provides a lower bound on the probability of observations, thus making it attractive for learning tasks. We provide the theoretical foundations for the approximation, an efficient implementation that exploits the wide range of highly optimized ordinary differential equations (ODE) solvers, experimentally explore characterizations of processes for which this approximation is suitable, and show applications to a large-scale realworld inference problem.

  5. An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network.

    Science.gov (United States)

    Morota, G; Valente, B D; Rosa, G J M; Weigel, K A; Gianola, D

    2012-12-01

    Linkage disequilibrium (LD) is defined as a non-random association of the distributions of alleles at different loci within a population. This association between loci is valuable in prediction of quantitative traits in animals and plants and in genome-wide association studies. A question that arises is whether standard metrics such as D' and r(2) reflect complex associations in a genetic system properly. It seems reasonable to take the view that loci associate and interact together as a system or network, as opposed to in a simple pairwise manner. We used a Bayesian network (BN) as a representation of choice for an LD network. A BN is a graphical depiction of a probability distribution and can represent sets of conditional independencies. Moreover, it provides a visual display of the joint distribution of the set of random variables in question. The usefulness of BN for linkage disequilibrium was explored and illustrated using genetic marker loci found to have the strongest effects on milk protein in Holstein cattle based on three strategies for ranking marker effect estimates: posterior means, standardized posterior means and additive genetic variance. Two different algorithms, Tabu search (a local score-based algorithm) and incremental association Markov blanket (a constraint-based algorithm), coupled with the chi-square test, were used for learning the structure of the BN and were compared with the reference r(2) metric represented as an LD heat map. The BN captured several genetic markers associated as clusters, implying that markers are inter-related in a complicated manner. Further, the BN detected conditionally dependent markers. The results confirm that LD relationships are of a multivariate nature and that r(2) gives an incomplete description and understanding of LD. Use of an LD Bayesian network enables inferring associations between loci in a systems framework and provides a more accurate picture of LD than that resulting from the use of pairwise

  6. 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

  7. Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

    International Nuclear Information System (INIS)

    This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian networks' inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis

  8. 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.

  9. a Simplified Bayesian Network Model Applied in Crop or Animal Disease Diagnosis

    Science.gov (United States)

    Yu, Helong; Chen, Guifen; Liu, Dayou

    Bayesian network is a powerful tool to represent and deal with uncertain knowledge. There exists much uncertainty in crop or animal disease. The construction of Bayesian network need much data and knowledge. But when data is scarce, some methods should be adopted to construct an effective Bayesian network. This paper introduces a disease diagnosis model based on Bayesian network, which is two-layered and obeys noisy-or assumption. Based on the two-layered structure, the relationship between nodes is obtained by domain knowledge. Based on the noisy-model, the conditional probability table is elicited by three methods, which are parameter learning, domain expert and the existing certainty factor model. In order to implement this model, a Bayesian network tool is developed. Finally, an example about cow disease diagnosis was implemented, which proved that the model discussed in this paper is an effective tool for some simple disease diagnosis in crop or animal field.

  10. Stratified Graphical Models - Context-Specific Independence in Graphical Models

    OpenAIRE

    Nyman, Henrik; Pensar, Johan; Koski, Timo; Corander, Jukka

    2014-01-01

    Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc. Despite of their generic applicability and wide adoptance, the constraints imposed by undirected graphical models and Bayesian networks have also been recognized to be unnecessarily stringent under certain circumstances. This observ...

  11. Development of a Bayesian Belief Network Runway Incursion Model

    Science.gov (United States)

    Green, Lawrence L.

    2014-01-01

    In a previous paper, a statistical analysis of runway incursion (RI) events was conducted to ascertain their relevance to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to perhaps several of the AvSP top ten TC. That data also identified several primary causes and contributing factors for RI events that served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events. The system-level BBN model will allow NASA to generically model the causes of RI events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of RI events in particular, and to improve runway safety in general. The development, structure and assessment of that BBN for RI events by a Subject Matter Expert panel are documented in this paper.

  12. 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.

  13. CEO Emotional Intelligence and Firms’ Financial Policies. Bayesian Network Method

    Directory of Open Access Journals (Sweden)

    Mohamed Ali Azouzi

    2014-03-01

    Full Text Available The aim of this paper is to explore the determinants of firms’ financial policies according to the manager’s psychological characteristics. More specifically, it examines the links between emotional intelligence, decision biases and the effectiveness of firms’ financial policies. The article finds that the main cause of an organization’s problems is the CEO’s emotional intelligence level. We introduce an approach based on Bayesian network techniques with a series of semi-directive interviews. The research paper represents an original approach because it characterizes behavioral corporate policy choices in emerging markets. To the best of our knowledge, this is the first study in the Tunisian context to explore this area of research. Our results show that Tunisian leaders adjust their decisions (on investments and distributions to minimize the risk of loss of compensation or reputation. They opt for decisions that minimize agency costs, transaction costs, and cognitive costs.

  14. 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

  15. 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.

  16. 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.

  17. Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules

    Directory of Open Access Journals (Sweden)

    Rolly Intan

    2010-01-01

    Full Text Available Bayesian Belief Network (BBN, one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. Development of a cyber security risk model using Bayesian networks

    International Nuclear Information System (INIS)

    Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I and C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility. - Highlights: • We developed the cyber security risk model can be find the weak point of cyber security integrated two cyber analysis models by using Bayesian Network. • One is the activity-quality model signifies how people and/or organization comply with the cyber security regulatory guide. • Other is the architecture model represents the probability of cyber-attack on RPS architecture. • The cyber security risk model can provide evidence that is able to determine the key element for cyber security for RPS of a research reactor

  4. Forecasting multivariate road traffic flows using Bayesian dynamic graphical models, splines and other traffic variables

    OpenAIRE

    Anacleto Junior, Osvaldo; Queen, Catriona; Albers, Casper

    2013-01-01

    Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simple model to use. While statistical flow forecasting models usually base their forecasts on flow da...

  5. 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.

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

    OpenAIRE

    Bin Suo; Ying Yan

    2013-01-01

    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 un...

  7. 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.

  8. Bayesian Fusion Algorithm for Inferring Trust in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Momani

    2010-07-01

    Full Text Available This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary and the data trust component (continuous and proves that either component by itself, can mislead the network and eventually cause a total breakdown of the network. As a result of this, new algorithms are needed to combine more than one trust component to infer the overall trust. The proposed algorithm is simple and generic as it allows trust components to be added and deleted easily. Simulation results demonstrate that a node is highly trustworthy provided that both trust components simultaneously confirm its trustworthiness and conversely, a node is highly untrustworthy if its untrustworthiness is asserted by both components.

  9. 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...

  10. 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…

  11. 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.

  12. Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

    Directory of Open Access Journals (Sweden)

    Huang Yufei

    2007-01-01

    Full Text Available We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.

  13. Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

    Directory of Open Access Journals (Sweden)

    Isabel Tienda Luna

    2007-06-01

    Full Text Available We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.

  14. 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

  15. 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.

  16. 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.

  17. 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...

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

    DEFF Research Database (Denmark)

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

    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, and that......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...

  19. 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...

  20. RADYBAN: A tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks

    International Nuclear Information System (INIS)

    In this paper, we present RADYBAN (Reliability Analysis with DYnamic BAyesian Networks), a software tool which allows to analyze a dynamic fault tree relying on its conversion into a dynamic Bayesian network. The tool implements a modular algorithm for automatically translating a dynamic fault tree into the corresponding dynamic Bayesian network and exploits classical algorithms for the inference on dynamic Bayesian networks, in order to compute reliability measures. After having described the basic features of the tool, we show how it operates on a real world example and we compare the unreliability results it generates with those returned by other methodologies, in order to verify the correctness and the consistency of the results obtained

  1. 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

  2. Mining functional modules in genetic networks with decomposable graphical models.

    Science.gov (United States)

    Dejori, Mathäus; Schwaighofer, Anton; Tresp, Volker; Stetter, Martin

    2004-01-01

    In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count. PMID:15268775

  3. 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...

  4. 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...

  5. 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...

  6. 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.

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

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming; Fan, Zhun; Li, Youfeng; Feng, Shan

    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 ...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  8. CEO emotional bias and dividend policy: Bayesian network method

    Directory of Open Access Journals (Sweden)

    Azouzi Mohamed Ali

    2012-10-01

    Full Text Available This paper assumes that managers, investors, or both behave irrationally. In addition, even though scholars have investigated behavioral irrationality from three angles, investor sentiment, investor biases and managerial biases, we focus on the relationship between one of the managerial biases, overconfidence and dividend policy. Previous research investigating the relationship between overconfidence and financial decisions has studied investment, financing decisions and firm values. However, there are only a few exceptions to examine how a managerial emotional bias (optimism, loss aversion and overconfidence affects dividend policies. This stream of research contends whether to distribute dividends or not depends on how managers perceive of the company’s future. I will use Bayesian network method to examine this relation. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some100 Tunisian executives. Our results have revealed that leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its dividend policy 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Kau, Daekwang; /Florida State U.

    2007-08-01

    We present results of a search for single top quark production in p{bar p} collisions using a dataset of approximately 1 fb{sup -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 {sigma}(p{bar p} {yields} tb + X, tqb + X) = 4.4 {+-} 1.5 pb.

  14. Modeling Land-Use Decision Behavior with Bayesian Belief Networks

    Directory of Open Access Journals (Sweden)

    Inge Aalders

    2008-06-01

    Full Text Available The ability to incorporate and manage the different drivers of land-use change in a modeling process is one of the key challenges because they are complex and are both quantitative and qualitative in nature. This paper uses Bayesian belief networks (BBN to incorporate characteristics of land managers in the modeling process and to enhance our understanding of land-use change based on the limited and disparate sources of information. One of the two models based on spatial data represented land managers in the form of a quantitative variable, the area of individual holdings, whereas the other model included qualitative data from a survey of land managers. Random samples from the spatial data provided evidence of the relationship between the different variables, which I used to develop the BBN structure. The model was tested for four different posterior probability distributions, and results showed that the trained and learned models are better at predicting land use than the uniform and random models. The inference from the model demonstrated the constraints that biophysical characteristics impose on land managers; for older land managers without heirs, there is a higher probability of the land use being arable agriculture. The results show the benefits of incorporating a more complex notion of land managers in land-use models, and of using different empirical data sources in the modeling process. Future research should focus on incorporating more complex social processes into the modeling structure, as well as incorporating spatio-temporal dynamics in a BBN.

  15. Computing Posterior Probabilities of Structural Features in Bayesian Networks

    CERN Document Server

    Tian, Jin

    2012-01-01

    We study the problem of learning Bayesian network structures from data. Koivisto and Sood (2004) and Koivisto (2006) presented algorithms that can compute the exact marginal posterior probability of a subnetwork, e.g., a single edge, in O(n2n) time and the posterior probabilities for all n(n-1) potential edges in O(n2n) total time, assuming that the number of parents per node or the indegree is bounded by a constant. One main drawback of their algorithms is the requirement of a special structure prior that is non uniform and does not respect Markov equivalence. In this paper, we develop an algorithm that can compute the exact posterior probability of a subnetwork in O(3n) time and the posterior probabilities for all n(n-1) potential edges in O(n3n) total time. Our algorithm also assumes a bounded indegree but allows general structure priors. We demonstrate the applicability of the algorithm on several data sets with up to 20 variables.

  16. 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.

  17. 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

  18. 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.

  19. 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. PMID:25622335

  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. Use of a Quantum Computer to do Importance and Metropolis-Hastings Sampling of a Classical Bayesian Network

    OpenAIRE

    Tucci, Robert R.

    2008-01-01

    Importance sampling and Metropolis-Hastings sampling (of which Gibbs sampling is a special case) are two methods commonly used to sample multi-variate probability distributions (that is, Bayesian networks). Heretofore, the sampling of Bayesian networks has been done on a conventional "classical computer". In this paper, we propose methods for doing importance sampling and Metropolis-Hastings sampling of a classical Bayesian network on a quantum computer.

  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. A generic method for estimating system reliability using Bayesian networks

    International Nuclear Information System (INIS)

    This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples

  6. 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.

  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. 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

  9. 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.

  10. 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.

  11. 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.

  12. Risk-based design of process systems using discrete-time Bayesian networks

    International Nuclear Information System (INIS)

    Temporal Bayesian networks have gained popularity as a robust technique to model dynamic systems in which the components' sequential dependency, as well as their functional dependency, cannot be ignored. In this regard, discrete-time Bayesian networks have been proposed as a viable alternative to solve dynamic fault trees without resort to Markov chains. This approach overcomes the drawbacks of Markov chains such as the state-space explosion and the error-prone conversion procedure from dynamic fault tree. It also benefits from the inherent advantages of Bayesian networks such as probability updating. However, effective mapping of the dynamic gates of dynamic fault trees into Bayesian networks while avoiding the consequent huge multi-dimensional probability tables has always been a matter of concern. In this paper, a new general formalism has been developed to model two important elements of dynamic fault tree, i.e., cold spare gate and sequential enforcing gate, with any arbitrary probability distribution functions. Also, an innovative Neutral Dependency algorithm has been introduced to model dynamic gates such as priority-AND gate, thus reducing the dimension of conditional probability tables by an order of magnitude. The second part of the paper is devoted to the application of discrete-time Bayesian networks in the risk assessment and safety analysis of complex process systems. It has been shown how dynamic techniques can effectively be applied for optimal allocation of safety systems to obtain maximum risk reduction.

  13. Graphical model construction based on evolutionary algorithms

    Institute of Scientific and Technical Information of China (English)

    Youlong YANG; Yan WU; Sanyang LIU

    2006-01-01

    Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining.The experimental results show that the exact theoretical results and the approximations match very well.

  14. 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.

  15. Tutorial on Exact Belief Propagation in Bayesian Networks: from Messages to Algorithms

    CERN Document Server

    Nuel, G

    2012-01-01

    In Bayesian networks, exact belief propagation is achieved through message passing algorithms. These algorithms (ex: inward and outward) provide only a recursive definition of the corresponding messages. In contrast, when working on hidden Markov models and variants, one classically first defines explicitly these messages (forward and backward quantities), and then derive all results and algorithms. In this paper, we generalize the hidden Markov model approach by introducing an explicit definition of the messages in Bayesian networks, from which we derive all the relevant properties and results including the recursive algorithms that allow to compute these messages. Two didactic examples (the precipitation hidden Markov model and the pedigree Bayesian network) are considered along the paper to illustrate the new formalism and standalone R source code is provided in the appendix.

  16. 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)

  17. 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.

  18. 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.

  19. Graphical reduction of reaction networks by linear elimination of species

    OpenAIRE

    Sáez, Meritxell; Wiuf, Carsten; Feliu, Elisenda

    2015-01-01

    The quasi-steady state approximation and time-scale separation are commonly applied methods to simplify models of biochemical reaction networks based on ordinary differential equations (ODEs). The concentrations of the "fast" species are assumed effectively to be at steady state with respect to the "slow" species. Under this assumption the steady state equations can be used to eliminate the "fast" variables and a new ODE system with only the slow species can be obtained. We interpret a reduce...

  20. 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)

  1. 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.

  2. 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.

  3. New approach using Bayesian Network to improve content based image classification systems

    CERN Document Server

    jayech, Khlifia

    2012-01-01

    This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors. Then, we propose to carry out a classification of the vectors of descriptors to build a vector of labels for each image. Finally, we propose three variants of Bayesian Networks such as Naive Bayesian Network (NB), Tree Augmented Naive Bayes (TAN) and Forest Augmented Naive Bayes (FAN) to classify the image using the vector of labels. The results showed a marked improvement over the FAN, NB and TAN.

  4. Application of Bayesian networks for risk analysis of MV air insulated switch operation

    International Nuclear Information System (INIS)

    Electricity distribution companies regard risk-based approaches as a good philosophy to address their asset management challenges, and there is an increasing trend on developing methods to support decisions where different aspects of risks are taken into consideration. This paper describes a methodology for application of Bayesian networks for risk analysis in electricity distribution system maintenance management. The methodology is used on a case analysing safety risk related to operation of MV air insulated switches. The paper summarises some challenges and benefits of using Bayesian networks as a part of distribution system maintenance management.

  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 network modeling: A case study of an epidemiologic system analysis of cardiovascular risk.

    Science.gov (United States)

    Fuster-Parra, P; Tauler, P; Bennasar-Veny, M; Ligęza, A; López-González, A A; Aguiló, A

    2016-04-01

    An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. PMID:26777431

  7. 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

  8. 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

  9. Using Bayesian Belief Networks To Assess Volcano State from Multiple Monitoring Timeseries And Other Evidence

    Science.gov (United States)

    Odbert, Henry; Aspinall, Willy

    2013-04-01

    When volcanoes exhibit unrest or become eruptively active, science-based decision support invariably is sought by civil authorities. Evidence available to scientists about a volcano's internal state is usually indirect, secondary or very nebulous.Advancement of volcano monitoring technology in recent decades has increased the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Monitoring timeseries may be interpreted in real time by observatory staff and are often later subjected to further analytic scrutiny by the research community at large. With increasing variety and resolution of data, interpreting these multiple strands of parallel, partial evidence has become increasingly complex. In practice, interpretation of many timeseries involves familiarity with the idiosyncracies of the volcano, the monitoring techniques, the configuration of the recording instrumentation, observations from other datasets, and so on. Assimilation of this knowledge is necessary in order to select and apply the appropriate statistical techniques required to extract the required information. 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 observations, model results and interpretations - and associated uncertainties - in a methodical manner. The formulation is 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 timeseries, the certainty with which inferences may be drawn, and how they can be updated dynamically. Such approaches provide a route to developing analytical interface(s) between volcano monitoring analyses and probabilistic hazard analysis. We discuss the use of BBNs in hazard

  10. A new graphical method for Pinch Analysis applications: Heat exchanger network retrofit and energy integration

    International Nuclear Information System (INIS)

    Energy integration is a key solution in chemical process and crude refining industries to minimise external fuel consumption and to face the impact of growing energy crises. Typical energy integration projects can reach a reduction of heating fuels and cold utilities by up to 40% compared with original designs or existing installations. Pinch Analysis is a leading tool and regarded as an efficient method to increase energy efficiency and minimise fuel flow consumptions. It is valid for both natures of design, grassroots and retrofit situations. It can practically be applied to synthesise a HEN (heat exchanger network) or modify an existing preheat train for minimum energy consumption. Heat recovery systems or HENs are networks for exchanging heat between hot and cold process sources. All heat transferred from hot process sources into cold process sinks represent the scope for energy integration. On the other hand, energies required beyond this integrated amount are to be satisfied by external utilities. Graphical representations of Pinch Analysis, such as Composite and Grand Composite Curves are very useful for grassroots designs. Nevertheless, in retrofit situation the analysis is not adequate and besides it is graphically tedious to represent existing exchangers on such graphs. This research proposes a new graphical method for the analysis of heat recovery systems, applicable to HEN retrofit. The new graphical method is based on plotting temperatures of process hot streams versus temperatures of process cold streams. A new graph is constructed for representing existing HENs. For a given network, each existing exchanger is represented by a straight line, whose slope is proportional to the ratio of heat capacities and flows. Further, the length of each exchanger line is related to the heat flow transferred across this exchanger. This new graphical representation can easily identify exchangers across the pinch, Network Pinch, pinching matches and improper placement

  11. 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.

  12. Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

    Directory of Open Access Journals (Sweden)

    Care Matthew A

    2006-09-01

    Full Text Available Abstract Background A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction. Results Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables. Conclusion The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.

  13. 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...

  14. 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

  15. 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

  16. A simulated annealing-based method for learning Bayesian networks from statistical data

    Czech Academy of Sciences Publication Activity Database

    Janžura, Martin; Nielsen, Jan

    2006-01-01

    Roč. 21, č. 3 (2006), s. 335-348. ISSN 0884-8173 R&D Projects: GA ČR GA201/03/0478 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian network * simulated annealing * Markov Chain Monte Carlo Subject RIV: BA - General Mathematics Impact factor: 0.429, year: 2006

  17. 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

  18. Risk-Based Operation and Maintenance of Offshore Wind Turbines using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2011-01-01

    For offshore wind farms, the costs due to operation and maintenance are large, and more optimal planning has the potential of reducing these costs. This paper presents how Bayesian networks can be used for risk-based inspection planning, where the inspection plans are updated each year through the...

  19. 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.

  20. Preliminary investigation to use Bayesian networks in predicting NOx, CO, CO2 and HC emissions

    International Nuclear Information System (INIS)

    A Bayesian network was used to characterize Lister-Petter diesel combustion engine emissions. Three sets of tests were conducted: (1) full open throttle; (2) 68 per cent closed throttle; and (3) 58 per cent closed throttle. The first test simulated normal lean burning conditions, while the last 2 tests simulated a clogged air filter. Experiments were conducted in an engine generator assembly with a fixed speed governor of 1500 rpm. Electrochemical sensors were used to detect nitrogen oxide (NOx); carbon dioxide (CO2); carbon monoxide (CO); hydrocarbons; and particulate matter. Engine oil, engine outlet, and engine inlet and exhaust temperatures were digitally measured. Data from 20 experimental sets of tests were used to train, test and project accurate emission levels. The Bayesian network model was built using input variables and measured output parameters related to the exhaust components. Human knowledge was used to build relationships between defined nodes and a path condition algorithm. An estimation-maximization algorithm was used. Results of the validation study showed that the Bayesian network accurately predicted emissions levels. It was concluded that it is possible to predict engine emission outputs with probable acceptable levels using Bayesian network modelling techniques and limited experimental data. 33 refs., 3 tabs., 8 figs

  1. 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.

  2. 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

  3. Operations for Learning with Graphical Models

    Science.gov (United States)

    Buntine, Wray L.

    1994-01-01

    This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

  4. 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...

  5. 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...

  6. 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...

  7. Cognitive Maps and Bayesian Networks for Knowledge Representation and Reasoning

    OpenAIRE

    Sedki, Karima; Bonneau De Beaufort, Louis

    2012-01-01

    Cognitive maps are powerful graphical models for knowledge representation. They offer an easy means to express individual's judgments, thinking or beliefs about a given problem. However, drawing inferences in cognitive maps, especially when the problem is complex, may not be an easy task. The main reason of this limitation in cognitive maps is that they do not model uncertainty with the variables. Our contribution in this paper is twofold : we firstly enrich the cognitive map formalism regard...

  8. Quantum Graphical Models and Belief Propagation

    OpenAIRE

    Leifer, Matthew; Poulin, David

    2007-01-01

    Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical pro...

  9. 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.

  10. How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing

    OpenAIRE

    Bachrach, Yoram; Graepel, Thore; Minka, Tom; Guiver, John

    2012-01-01

    We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude testing and crowdsourcing settings. We devise an active learning/adaptive testing scheme based on a greedy minimization of expected model entropy, which allows a more efficient resource allocation by dynamically choosing the next question to be asked based on the previous responses. We present experimental results that...

  11. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.

    Science.gov (United States)

    Wang, Ting; Ren, Zhao; Ding, Ying; Fang, Zhou; Sun, Zhe; MacDonald, Matthew L; Sweet, Robert A; Wang, Jieru; Chen, Wei

    2016-02-01

    Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM". PMID:26872036

  12. OpenSim: A Flexible Distributed Neural Network Simulator with Automatic Interactive Graphics.

    Science.gov (United States)

    Jarosch, Andreas; Leber, Jean Francois

    1997-06-01

    An object-oriented simulator called OpenSim is presented that achieves a high degree of flexibility by relying on a small set of building blocks. The state variables and algorithms put in this framework can easily be accessed through a command shell. This allows one to distribute a large-scale simulation over several workstations and to generate the interactive graphics automatically. OpenSim opens new possibilities for cooperation among Neural Network researchers. Copyright 1997 Elsevier Science Ltd. PMID:12662864

  13. 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

  14. 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

  15. 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.

  16. 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. PMID:27076477

  17. 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

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

    International Nuclear Information System (INIS)

    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

  19. 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.

  20. Flow and pressures networks simulation under VisSim trademark graphic interphase

    International Nuclear Information System (INIS)

    In this work, a graphic interphase to facilitate the interaction between the user and a flow and pressure network (FPN) model is presented. The graphic interphase was designed using the VisSim trademark commercial software (VisSim, 1994) which is a visual programming mathematical tool that allows direct simulation from graphics constructed using block diagrams. VisSim trademark was not used to solve the FPN but an external program (solver) was designed ex professo. The reason for this is that VisSim trademark is not able to work with a model whose structure may vary in size and topology. Thus, VisSim trademark was used as a graphic interphase exclusively. The main problem was to interpret the flow diagram (which shows the topology of the system) to feed the solver. This interpretation was made with a preprocessor which reads the VisSim trademark data file (·VSM file) containing the information of all the VisSim trademark configuration, extracts the information of the topology, and feeds the solver

  1. Reliability assessment of a software-based motor protection relay using Bayesian networks

    International Nuclear Information System (INIS)

    Often to make justified reliability claim of a certain system different kinds of evidence needs to be combined. Some of the evidence supporting the claim may be of qualitative type, whereas some of the evidence may be of quantitative type. Combination of disparate evidence together is not always straightforward and the reasoning behind the conclusions obtained from the combination may be hard to explain. Bayesian networks provide a consistent and transparent method for the combination of the evidence and for the reasoning of one's beliefs on the relation of different pieces of evidence. In the special report we demonstrate the combination of disparate evidence with a case study on the reliability assessment of a software-based motor protection relay, where the combination of the reliability related evidence has been carried out using Bayesian networks. The reliability related evidence in the case study is the expert judgement on the development process and the operational experience estimated for the softwarebased motor protection relay. (orig.)

  2. 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.

  3. The threshold EM algorithm for parameter learning in bayesian network with incomplete data

    CERN Document Server

    Lamine, Fradj Ben; Mahjoub, Mohamed Ali

    2012-01-01

    Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algorithms. In order to limit the search space and escape from local maxima produced by executing EM algorithm, this paper presents a learning parameter algorithm that is a fusion of EM and RBE algorithms. This algorithm incorporates the range of a parameter into the EM algorithm. This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. The threshold EM algorithm is applied in brain tumor diagnosis and show some advantages and disadvantages over the EM algorithm.

  4. Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks.

    Science.gov (United States)

    Tylman, Wojciech; Waszyrowski, Tomasz; Napieralski, Andrzej; Kamiński, Marek; Trafidło, Tamara; Kulesza, Zbigniew; Kotas, Rafał; Marciniak, Paweł; Tomala, Radosław; Wenerski, Maciej

    2016-02-01

    This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification. PMID:26456181

  5. 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.

  6. 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.

  7. 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...

  8. 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

  9. 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

  10. 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...

  11. Learning Bayesian network structure: towards the essential graph by integer linear programming tools

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.

    2014-01-01

    Roč. 55, č. 4 (2014), s. 1043-1071. ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * integer linear programming * characteristic imset * essential graph Subject RIV: BA - General Mathematics Impact factor: 2.451, year: 2014 http://library.utia.cas.cz/separaty/2014/MTR/studeny-0427002.pdf

  12. 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...

  13. 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 ...

  14. Hierarchical and Bayesian Scattered Data Taxonomy in Mobile Ad-hoc Network

    OpenAIRE

    Sunar Arif Hussain; K.E. SREENIVASA MURTHY

    2011-01-01

    MANETS promise an unprecedented opportunity to monitor physical environments via inexpensive wireless embedded devices. Given the sheer amount of sensed data, efficient taxonomy of them becomes a critical task in many sensor network applications. The Bayesian classifier is a fundamental taxonomy technique. We introduce two classifiers: Naive Bayes and a classifier based on class decomposition using K-means clustering. We consider two complementary tasks: model computation and scoring a data s...

  15. A BAYESIAN NETWORKS APPROACH TO MODELING FINANCIAL RISKS OF E-LOGISTICS INVESTMENTS

    OpenAIRE

    CHIEN-WEN SHEN

    2009-01-01

    To evaluate whether the investments of e-logistics systems may increase financial risks, models of Bayesian networks are constructed in this study with the mechanism of structural learning and parameter learning. Empirical findings from the transport and logistics sectors suggest that the e-logistics investments generally do not increase the financial risks of companies except the implementation of computer aided picking systems and radio frequency identification. Meanwhile, only the investme...

  16. Intervention and causality: forecasting traffic flows using a dynamic Bayesian network

    OpenAIRE

    Queen, Catriona; Albers, Casper

    2009-01-01

    Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable short-term forecasting models of traffic flows are crucial for the success of any traffic management system. The model proposed in this paper for forecasting traffic flows is a multivariate Bayesian dynamic model called the multiregression dynamic model (MDM). This model is an example of a dynamic ...

  17. WHY ENTREPRENEUR OVERCONFIDENCE AFFECT ITS PROJECT FINANCIAL CAPABILITY: EVIDENCE FROM TUNISIA USING THE BAYESIAN NETWORK METHOD

    OpenAIRE

    Salima TAKTAK; AZOUZI Mohamed Ali; Triki, Mohamed

    2013-01-01

    This article discusses the effect of the entrepreneur’s profile on financing his creative project. It analyzes the impact of overconfidence on improving perceptions financing capacity of the project. To analyze this relationship we used networks as Bayesian data analysis method. Our sample is composed of 200 entrepreneurs. Our results show a high level of entrepreneur’s overconfidence positively affects the evaluation of financing capacity of the project.

  18. 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.

  19. 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...

  20. A Mobile Picture Tagging System Using Tree-Structured Layered Bayesian Networks

    OpenAIRE

    Young-Seol Lee; Sung-Bae Cho

    2013-01-01

    Advances in digital media technology have increased in multimedia content. Tagging is one of the most effective methods to manage a great volume of multimedia content. However, manual tagging has limitations such as human fatigue and subjective and ambiguous keywords. In this paper, we present an automatic tagging method to generate semantic annotation on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two layered Bayesian networks. In contrast ...

  1. 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...

  2. 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...

  3. 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...

  4. 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...

  5. How matroids occur in the context of learning Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan

    Corvallis, Oregon: AUAI Press, 2015, s. 832-841. ISBN 978-0-9966431-0-8. [31st Conference on Uncertainty in Artificial Intelligence. Amsterdam (NL), 12.07.2015-16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * matroid * family-variable polytope Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2015/MTR/studeny-0447685.pdf

  6. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

    OpenAIRE

    Gevaert, Olivier; De Smet, Frank; Timmerman, Dirk; Moreau, Yves; De Moor, Bart

    2006-01-01

    MOTIVATION: Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows t...

  7. 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...

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

    OpenAIRE

    Huawei Wang; Jun Gao

    2013-01-01

    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 ...

  9. A Study of Scaling Issues in Bayesian Belief Networks for Ship Classification

    OpenAIRE

    Musman, Scott A.; Chang, L. W.

    2013-01-01

    The problems associated with scaling involve active and challenging research topics in the area of artificial intelligence. The purpose is to solve real world problems by means of AI technologies, in cases where the complexity of representation of the real world problem is potentially combinatorial. In this paper, we present a novel approach to cope with the scaling issues in Bayesian belief networks for ship classification. The proposed approach divides the conceptual model of a complex ship...

  10. 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.

  11. 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.)

  12. Unavailability analysis of a PWR safety system by a Bayesian network

    International Nuclear Information System (INIS)

    Bayesian networks (BN) are directed acyclic graphs that have dependencies between variables, which are represented by nodes. These dependencies are represented by lines connecting the nodes and can be directed or not. Thus, it is possible to model conditional probabilities and calculate them with the help of Bayes' Theorem. The objective of this paper is to present the modeling of the failure of a safety system of a typical second generation light water reactor plant, the Containment Heat Removal System (CHRS), whose function is to cool the water of containment reservoir being recirculated through the Containment Spray Recirculation System (CSRS). CSRS is automatically initiated after a loss of coolant accident (LOCA) and together with the CHRS cools the reservoir water. The choice of this system was due to the fact that its analysis by a fault tree is available in Appendix II of the Reactor Safety Study Report (WASH-1400), and therefore all the necessary technical information is also available, such as system diagrams, failure data input and the fault tree itself that was developed to study system failure. The reason for the use of a bayesian network in this context was to assess its ability to reproduce the results of fault tree analyses and also verify the feasibility of treating dependent events. Comparing the fault trees and bayesian networks, the results obtained for the system failure were very close. (author)

  13. 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.)

  14. 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.

  15. On Graphical (Decomposable) Models and Belief Networks in Dempster-Shafer Theory of Evidence

    Czech Academy of Sciences Publication Activity Database

    Jiroušek, Radim

    Otaru : University Hall, 2010 - (Itoh, T.; Suzuki, K.), s. 61-66 [Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /13./. Otaru (JP), 03.11.2010-05.11.2010] R&D Projects: GA MŠk 1M0572; GA ČR GA201/09/1891 Grant ostatní: GA ČR(XE) ICC/08/E010 Eurocores LogICCC Institutional research plan: CEZ:AV0Z10750506 Keywords : Discrete belief functions * conditional independence * multidimensional model * Dempster-Shafer theory * graphical model * operator of composition Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2010/MTR/jirousek-on graphical (decomposable) models and belief networks in dempster-shafer theory of evidence.pdf

  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. 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.

  18. Bayesian neural networks for bivariate binary data: an application to prostate cancer study.

    Science.gov (United States)

    Chakraborty, Sounak; Ghosh, Malay; Maiti, Tapabrata; Tewari, Ashutosh

    2005-12-15

    Prostate cancer is one of the most common cancers in American men. The cancer could either be locally confined, or it could spread outside the organ. When locally confined, there are several options for treating and curing this disease. Otherwise, surgery is the only option, and in extreme cases of outside spread, it could very easily recur within a short time even after surgery and subsequent radiation therapy. Hence, it is important to know, based on pre-surgery biopsy results how likely the cancer is organ-confined or not. The paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ confined prostate cancer. In particular, we find such probabilities for margin positivity (MP) and seminal vesicle (SV) positivity jointly. The available training set consists of bivariate binary outcomes indicating the presence or absence of the two. In addition, we have certain covariates such as prostate specific antigen (PSA), gleason score and the indicator for the cancer to be unilateral or bilateral (i.e. spread on one or both sides) in one data set and gene expression microarrays in another data set. We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test and validation set, and compare these with the actual outcomes for the first data set. In case of the microarray data we use leave one out cross-validation to access the accuracy of our method. We also demonstrate the superiority of our method to the other competing methods through a simulation study. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, our Bayesian bivariate neural network procedure is shown to be superior to the classical neural network, Radford Neal's Bayesian neural network as well as bivariate logistic models to predict jointly the MP and SV in a patient in both the

  19. 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 ...

  20. 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.

  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. Development of a Video Network for Efficient Dissemination of the Graphical Images in a Collaborative Environment.

    Directory of Open Access Journals (Sweden)

    Anatoliy Gordonov

    1999-01-01

    Full Text Available Video distribution inside a local area network can impede or even paralyze normal data transmission activities. The problem can be solved, at least for a while, by compression and by increasing bandwidth, but that solution can become excessively costly or otherwise impractical. Moreover, experience indicates that usage quickly expands to test the limits of bandwidth. In this paper we introduce and analyze the architecture of a Hybrid AnalogDigital Video Network (ADViNet which separates video distribution from standard data handling functions. The network preserves the features of a standard digital network and, in addition, provides efficient real-time full-screen video transmission through a separate analog communication medium. A specially developed control and management protocol is discussed. For all practical purposes ADViNet may be used when graphical images have to be distributed among many nodes of a local area network. It relieves the burden of video distribution and allows users to combine efficient video data transmission with normal regular network activities.

  3. 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.

  4. 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.

  5. 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.

  6. Polyhedral approach to statistical learning graphical models

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Hemmecke, R.; Vomlel, Jiří; Lindner, S.

    Osaka : JST CREST, 2010. s. 1-4. [The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Moderm Industrial Socienty". 28.06.2010-02.07.2010, Hotel Hankyu Expo Park, Osaka] Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian network * polyhedral approach * imset Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2010/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf

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

    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 we take 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 specific 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, we believe 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 paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a 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, we could connect the results of probabilistic inference from the Bayesian network with the consequence

  9. 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.

  10. 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.

  11. 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.

  12. Cerebral Glioma Grading Using Bayesian Network with Features Extracted from Multiple Modalities of Magnetic Resonance Imaging

    Science.gov (United States)

    Wang, Huiting; Liu, Renyuan; Zhang, Xin; Li, Ming; Yang, Yongbo; Yan, Jing; Niu, Fengnan; Tian, Chuanshuai; Wang, Kun; Yu, Haiping; Chen, Weibo; Wan, Suiren; Sun, Yu; Zhang, Bing

    2016-01-01

    Many modalities of magnetic resonance imaging (MRI) have been confirmed to be of great diagnostic value in glioma grading. Contrast enhanced T1-weighted imaging allows the recognition of blood-brain barrier breakdown. Perfusion weighted imaging and MR spectroscopic imaging enable the quantitative measurement of perfusion parameters and metabolic alterations respectively. These modalities can potentially improve the grading process in glioma if combined properly. In this study, Bayesian Network, which is a powerful and flexible method for probabilistic analysis under uncertainty, is used to combine features extracted from contrast enhanced T1-weighted imaging, perfusion weighted imaging and MR spectroscopic imaging. The networks were constructed using K2 algorithm along with manual determination and distribution parameters learned using maximum likelihood estimation. The grading performance was evaluated in a leave-one-out analysis, achieving an overall grading accuracy of 92.86% and an area under the curve of 0.9577 in the receiver operating characteristic analysis given all available features observed in the total 56 patients. Results and discussions show that Bayesian Network is promising in combining features from multiple modalities of MRI for improved grading performance. PMID:27077923

  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. 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.

  15. Forecasting Rainfall Time Series with stochastic output approximated by neural networks Bayesian approach

    Directory of Open Access Journals (Sweden)

    Cristian Rodriguez Rivero

    2014-07-01

    Full Text Available The annual estimate of the availability of the amount of water for the agricultural sector has become a lifetime in places where rainfall is scarce, as is the case of northwestern Argentina. This work proposes to model and simulate monthly rainfall time series from one geographical location of Catamarca, Valle El Viejo Portezuelo. In this sense, the time series prediction is mathematical and computational modelling series provided by monthly cumulative rainfall, which has stochastic output approximated by neural networks Bayesian approach. We propose to use an algorithm based on artificial neural networks (ANNs using the Bayesian inference. The result of the prediction consists of 20% of the provided data consisting of 2000 to 2010. A new analysis for modelling, simulation and computational prediction of cumulative rainfall from one geographical location is well presented. They are used as data information, only the historical time series of daily flows measured in mmH2O. Preliminary results of the annual forecast in mmH2O with a prediction horizon of one year and a half are presented, 18 months, respectively. The methodology employs artificial neural network based tools, statistical analysis and computer to complete the missing information and knowledge of the qualitative and quantitative behavior. They also show some preliminary results with different prediction horizons of the proposed filter and its comparison with the performance Gaussian process filter used in the literature.

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

    DEFF Research Database (Denmark)

    Vicard, P.; Dawid, A.P.; Mortera, J.; Lauritzen, Steffen Lilholt

    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...

  17. Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

    Directory of Open Access Journals (Sweden)

    LiMin Wang

    2013-01-01

    Full Text Available The problem of extracting knowledge from a relational database for probabilistic reasoning is still unsolved. On the basis of a three-phase learning framework, we propose the integration of a Bayesian network (BN with the functional dependency (FD discovery technique. Association rule analysis is employed to discover FDs and expert knowledge encoded within a BN; that is, key relationships between attributes are emphasized. Moreover, the BN can be updated by using an expert-driven annotation process wherein redundant nodes and edges are removed. Experimental results show the effectiveness and efficiency of the proposed approach.

  18. 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

    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...... to derive informative pairwise dependency measures, which are derived from the statistical cooccurrence of the names of the variables, from the similarity of the "kernel" descriptions of the variables and from a combined method. We perform wide-scale evaluation of these text-based dependency scores...

  19. 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.

  20. Learning ground CP-logic theories by leveraging Bayesian network learning techniques

    OpenAIRE

    Meert, Wannes; Struyf, Jan; Blockeel, Hendrik

    2008-01-01

    Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its first contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) learning techniques. SEM-CP-logic is based on a transformation between CP-theories and BNs. That is, the ...

  1. 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

    -categories of leg disorders were divided into infectious causes (arthritis caused by infectious pathogens), physical causes (e.g. fracture and claw lesions), and inherited causes (osteochondrosis). Information about the herd (e.g. the herd size, floor type and number of suppliers) and information about...... individual 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...

  2. 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.

  3. 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

  4. Bayesian Test of Significance for Conditional Independence: The Multinomial Model

    Science.gov (United States)

    de Morais Andrade, Pablo; Stern, Julio; de Bragança Pereira, Carlos

    2014-03-01

    Conditional independence tests (CI tests) have received special attention lately in Machine Learning and Computational Intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of Probabilistic Graphical Models (PGM)--which includes Bayesian Networks (BN) models--CI tests are especially important for the task of learning the PGM structure from data. In this paper, we propose the Full Bayesian Significance Test (FBST) for tests of conditional independence for discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as an alternative to frequentist's significance tests (characterized by the calculation of the \\emph{p-value}).

  5. 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

  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. PMID:26433361

  7. 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. PMID:26264661

  8. 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

  9. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  10. Nuclear mass predictions for the crustal composition of neutron stars: A Bayesian neural network approach

    Science.gov (United States)

    Utama, R.; Piekarewicz, J.; Prosper, H. B.

    2016-01-01

    Background: Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r -process nucleosynthesis and neutron-star structure. Purpose: To overcome the intrinsic limitations of existing "state-of-the-art" mass models through a refinement based on a Bayesian neural network (BNN) formalism. Methods: A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. Results: A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a "world average" of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star. Conclusions: The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.

  11. 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.

  12. A stochastic model of human visual attention with a dynamic Bayesian network

    CERN Document Server

    kimura, Akisato; Takeuchi, Tatsuto; Miyazato, Kouji; Yamato, Junji; Kashino, Kunio

    2010-01-01

    Recent studies in the field of human vision science suggest that the human responses to the stimuli on a visual display are non-deterministic. People may attend to different locations on the same visual input at the same time. Based on this knowledge, we propose a new stochastic model of visual attention by introducing a dynamic Bayesian network to predict the likelihood of where humans typically focus on a video scene. The proposed model is composed of a dynamic Bayesian network with 4 layers. Our model provides a framework that simulates and combines the visual saliency response and the cognitive state of a person to estimate the most probable attended regions. Sample-based inference with Markov chain Monte-Carlo based particle filter and stream processing with multi-core processors enable us to estimate human visual attention in near real time. Experimental results have demonstrated that our model performs significantly better in predicting human visual attention compared to the previous deterministic mode...

  13. 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

  14. 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.

  15. 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.

  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. 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.

  18. 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

  19. 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

  20. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

    Directory of Open Access Journals (Sweden)

    Okut Hayrettin

    2011-10-01

    Full Text Available Abstract Background In the study of associations between genomic data and complex phenotypes there may be relationships that are not amenable to parametric statistical modeling. Such associations have been investigated mainly using single-marker and Bayesian linear regression models that differ in their distributions, but that assume additive inheritance while ignoring interactions and non-linearity. When interactions have been included in the model, their effects have entered linearly. There is a growing interest in non-parametric methods for predicting quantitative traits based on reproducing kernel Hilbert spaces regressions on markers and radial basis functions. Artificial neural networks (ANN provide an alternative, because these act as universal approximators of complex functions and can capture non-linear relationships between predictors and responses, with the interplay among variables learned adaptively. ANNs are interesting candidates for analysis of traits affected by cryptic forms of gene action. Results We investigated various Bayesian ANN architectures using for predicting phenotypes in two data sets consisting of milk production in Jersey cows and yield of inbred lines of wheat. For the Jerseys, predictor variables were derived from pedigree and molecular marker (35,798 single nucleotide polymorphisms, SNPS information on 297 individually cows. The wheat data represented 599 lines, each genotyped with 1,279 markers. The ability of predicting fat, milk and protein yield was low when using pedigrees, but it was better when SNPs were employed, irrespective of the ANN trained. Predictive ability was even better in wheat because the trait was a mean, as opposed to an individual phenotype in cows. Non-linear neural networks outperformed a linear model in predictive ability in both data sets, but more clearly in wheat. Conclusion Results suggest that neural networks may be useful for predicting complex traits using high

  1. 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

  2. Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

    Directory of Open Access Journals (Sweden)

    Liu Lin

    2009-12-01

    Full Text Available Abstract Background microRNAs (miRNAs regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT. Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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…

  8. 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.

  9. From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

    Directory of Open Access Journals (Sweden)

    Kok-Chin Khor

    2009-01-01

    Full Text Available Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC, Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms.

  10. Influences of variables on ship collision probability in a Bayesian belief network model

    International Nuclear Information System (INIS)

    The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watch's action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officer's fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

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

    DEFF Research Database (Denmark)

    Åström, Helena Lisa Alexandra

    Flooding is one of the most damaging natural hazards to human societies. Recent decades have shown that flooding constitutes major threats worldwide, and due to anticipated climate change the occurrence of damaging flood events is expected to increase. Urban areas are especially vulnerable to...... flood risk scoping, flood risk assessment (FRA), and adaptation implementation and involves an ongoing process of assessment, reassessment, and response. This thesis mainly focuses on the FRA phase of FRM. FRA includes hazard analysis and impact assessment (combined called a risk analysis), adaptation...... 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. Research on Risk Manage of Power Construction Project Based on Bayesian Network

    Science.gov (United States)

    Jia, Zhengyuan; Fan, Zhou; Li, Yong

    With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.

  18. Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River

    International Nuclear Information System (INIS)

    Formal safety assessment (FSA), as a structured and systematic risk evaluation methodology, has been increasingly and broadly used in the shipping industry around the world. Concerns have been raised as to navigational safety of the Yangtze River, China's largest and the world's busiest inland waterway. Over the last few decades, the throughput of ships in the Yangtze River has increased rapidly due to the national development of the Middle and Western parts of China. Accidents such as collisions, groundings, contacts, oil-spills and fires occur repeatedly, often causing serious consequences. In order to improve the navigational safety in the Yangtze River, this paper estimates the navigational risk of the Yangtze River using the FSA concept and a Bayesian network (BN) technique. The navigational risk model is established by considering both probability and consequences of accidents with respect to a risk matrix method, followed by a scenario analysis to demonstrate the application of the proposed model

  19. 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

  20. Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding.

    Science.gov (United States)

    Pérez-Rodríguez, P; Gianola, D; Weigel, K A; Rosa, G J M; Crossa, J

    2013-08-01

    In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples. PMID:23658327

  1. 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...

  2. 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.

  3. Wind Farm Reliability Modelling Using Bayesian Networks and Semi-Markov Processes

    Directory of Open Access Journals (Sweden)

    Robert Adam Sobolewski

    2015-09-01

    Full Text Available Technical reliability plays an important role among factors affecting the power output of a wind farm. The reliability is determined by an internal collection grid topology and reliability of its electrical components, e.g. generators, transformers, cables, switch breakers, protective relays, and busbars. A wind farm reliability’s quantitative measure can be the probability distribution of combinations of operating and failed states of the farm’s wind turbines. The operating state of a wind turbine is its ability to generate power and to transfer it to an external power grid, which means the availability of the wind turbine and other equipment necessary for the power transfer to the external grid. This measure can be used for quantitative analysis of the impact of various wind farm topologies and the reliability of individual farm components on the farm reliability, and for determining the expected farm output power with consideration of the reliability. This knowledge may be useful in an analysis of power generation reliability in power systems. The paper presents probabilistic models that quantify the wind farm reliability taking into account the above-mentioned technical factors. To formulate the reliability models Bayesian networks and semi-Markov processes were used. Using Bayesian networks the wind farm structural reliability was mapped, as well as quantitative characteristics describing equipment reliability. To determine the characteristics semi-Markov processes were used. The paper presents an example calculation of: (i probability distribution of the combination of both operating and failed states of four wind turbines included in the wind farm, and (ii expected wind farm output power with consideration of its reliability.

  4. 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

  5. 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

  6. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

    International Nuclear Information System (INIS)

    A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant. - Highlights: • A Dynamic Bayesian Network methodology has been developed to model domino effects. • Considering time-dependencies, both spatial and temporal evolutions of domino effects have been modeled. • The concept of most probable sequence of accidents has been proposed instead of the most probable combination of accidents. • Using backward analysis, the most vulnerable units have been identified during a potential domino effect. • The proposed methodology does not need to identify a unique primary unit (accident) for domino effect modeling

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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.

  12. Development of a Bayesian Belief Network Runway Incursion and Excursion Model

    Science.gov (United States)

    Green, Lawrence L.

    2014-01-01

    In a previous work, a statistical analysis of runway incursion (RI) event data was conducted to ascertain the relevance of this data to the top ten Technical Challenges (TC) of the National Aeronautics and Space Administration (NASA) Aviation Safety Program (AvSP). The study revealed connections to several of the AvSP top ten TC and identified numerous primary causes and contributing factors of RI events. The statistical analysis served as the basis for developing a system-level Bayesian Belief Network (BBN) model for RI events, also previously reported. Through literature searches and data analysis, this RI event network has now been extended to also model runway excursion (RE) events. These RI and RE event networks have been further modified and vetted by a Subject Matter Expert (SME) panel. The combined system-level BBN model will allow NASA to generically model the causes of RI and RE events and to assess the effectiveness of technology products being developed under NASA funding. These products are intended to reduce the frequency of runway safety incidents/accidents, and to improve runway safety in general. The development and structure of the BBN for both RI and RE events are documented in this paper.

  13. 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.

  14. 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

  15. 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.

  16. Assessing Vermont's stream health and biological integrity using artificial neural networks and Bayesian methods

    Science.gov (United States)

    Rizzo, D. M.; Fytilis, N.; Stevens, L.

    2012-12-01

    Environmental managers are increasingly required to monitor and forecast long-term effects and vulnerability of biophysical systems to human-generated stresses. Ideally, a study involving both physical and biological assessments conducted concurrently (in space and time) could provide a better understanding of the mechanisms and complex relationships. However, costs and resources associated with monitoring the complex linkages between the physical, geomorphic and habitat conditions and the biological integrity of stream reaches are prohibitive. Researchers have used classification techniques to place individual streams and rivers into a broader spatial context (hydrologic or health condition). Such efforts require environmental managers to gather multiple forms of information - quantitative, qualitative and subjective. We research and develop a novel classification tool that combines self-organizing maps with a Naïve Bayesian classifier to direct resources to stream reaches most in need. The Vermont Agency of Natural Resources has developed and adopted protocols for physical stream geomorphic and habitat assessments throughout the state of Vermont. Separate from these assessments, the Vermont Department of Environmental Conservation monitors the biological communities and the water quality in streams. Our initial hypothesis is that the geomorphic reach assessments and water quality data may be leveraged to reduce error and uncertainty associated with predictions of biological integrity and stream health. We test our hypothesis using over 2500 Vermont stream reaches (~1371 stream miles) assessed by the two agencies. In the development of this work, we combine a Naïve Bayesian classifier with a modified Kohonen Self-Organizing Map (SOM). The SOM is an unsupervised artificial neural network that autonomously analyzes inherent dataset properties using input data only. It is typically used to cluster data into similar categories when a priori classes do not exist. The

  17. 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.

  18. A graphical user interface for a method to infer kinetics and network architecture (MIKANA.

    Directory of Open Access Journals (Sweden)

    Márcio A Mourão

    Full Text Available One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1.

  19. Quantum Graphical Models and Belief Propagation

    International Nuclear Information System (INIS)

    Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markov Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described

  20. 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.

  1. Reasoning with probabilistic and deterministic graphical models exact algorithms

    CERN Document Server

    Dechter, Rina

    2013-01-01

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

  2. An application of GIS and Bayesian network in studying spatial-causal relations between enterprises and environmental factors

    Science.gov (United States)

    Shen, Tiyan; Li, Xi; Li, Maiqing

    2009-10-01

    The paper intends to employ Geographic Information System (GIS) and Bayesian Network to discover the spatial causality between enterprises and environmental factors in Beijing Metropolis. The census data of Beijing was spatialized by means of GIS in the beginning, and then the training data was made using density mapping technique. Base on the training data, the structure of a Bayesian Network was learnt with the help of Maximum Weight Spanning Tree. Eight direct relations were discussed in the end, of which, the most exciting discovery, "Enterprise-Run Society", as the symbol of the former planned economy, was emphasized in the spatial relations between heavy industry and schools. Though the final result is not so creative in economic perspective, it is of significance in technique view due to all discoveries were drawn from data, therefore leading to the realization of the importance of GIS and data mining to economic geography research.

  3. 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.

  4. 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.

  5. 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,...

  6. 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) ...

  7. 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

  8. An automated method for estimating reliability of grid systems using Bayesian networks

    International Nuclear Information System (INIS)

    Grid computing has become relevant due to its applications to large-scale resource sharing, wide-area information transfer, and multi-institutional collaborating. In general, in grid computing a service requests the use of a set of resources, available in a grid, to complete certain tasks. Although analysis tools and techniques for these types of systems have been studied, grid reliability analysis is generally computation-intensive to obtain due to the complexity of the system. Moreover, conventional reliability models have some common assumptions that cannot be applied to the grid systems. Therefore, new analytical methods are needed for effective and accurate assessment of grid reliability. This study presents a new method for estimating grid service reliability, which does not require prior knowledge about the grid system structure unlike the previous studies. Moreover, the proposed method does not rely on any assumptions about the link and node failure rates. This approach is based on a data-mining algorithm, the K2, to discover the grid system structure from raw historical system data, that allows to find minimum resource spanning trees (MRST) within the grid then, uses Bayesian networks (BN) to model the MRST and estimate grid service reliability.

  9. 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)

  10. 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.

  11. 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

  12. Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

    2010-01-01

    For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

  13. 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.

  14. 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. PMID:23499984

  15. 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

  16. A Bayesian network modeling approach to forecasting the 21st century worldwide status of polar bears

    Science.gov (United States)

    Amstrup, Steven C.; Marcot, Bruce G.; Douglas, David C.

    To inform the U.S. Fish and Wildlife Service decision, whether or not to list polar bears as threatened under the Endangered Species Act (ESA), we projected the status of the world's polar bears (Ursus maritimus) for decades centered on future years 2025, 2050, 2075, and 2095. We defined four ecoregions based on current and projected sea ice conditions: seasonal ice, Canadian Archipelago, polar basin divergent, and polar basin convergent ecoregions. We incorporated general circulation model projections of future sea ice into a Bayesian network (BN) model structured around the factors considered in ESA decisions. This first-generation BN model combined empirical data, interpretations of data, and professional judgments of one polar bear expert into a probabilistic framework that identifies causal links between environmental stressors and polar bear responses. We provide guidance regarding steps necessary to refine the model, including adding inputs from other experts. The BN model projected extirpation of polar bears from the seasonal ice and polar basin divergent ecoregions, where ≈2/3 of the world's polar bears currently occur, by mid century. Projections were less dire in other ecoregions. Decline in ice habitat was the overriding factor driving the model outcomes. Although this is a first-generation model, the dependence of polar bears on sea ice is universally accepted, and the observed sea ice decline is faster than models suggest. Therefore, incorporating judgments of multiple experts in a final model is not expected to fundamentally alter the outlook for polar bears described here.

  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. 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.

  19. HEAT STRESS RISK PREDICTION BY USING BAYESIAN NET MODEL WITH SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    Kanchan M. Taiwade

    2014-07-01

    Full Text Available With advancement in use of automation system, it is also desired to be able to know about the susceptible risk in advance for taking the preventive measures either automatically or manually. Disaster management is such an area where operatives wearing the suits and performing the activities are prone to the risk of heat stress which may cause mental impairments along with other serious effects leading to death. Such type of risk occurs in human body by not being able to compensate the heat generated into the surrounding air. The paper presents the concept of mechanism which can be used to prevent such situation by activating an alert to the operative or invoke cooling mechanism automatically before onset of the risk. The Bayesian Network Model is used to predict the onset of the risk. The model is based on the probabilities gives flexibility and simplicity in modeling the system. The system was trained with appropriate data and then compared with the real time parameters to check whether possibility of risk or not. Only those body parameters are considered which directly or indirectly participate in indicating heat stress or its onset.

  20. 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.

  1. 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.

  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. 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

  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. 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

  6. 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...

  7. 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.

  8. Polyhedral approach to statistical learning graphical models

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.; Hemmecke, R.; Lindner, S.

    Singapore : World Scientific Press, 2012, s. 346-372. ISBN 978-981-4383-45-5. [The 2nd CREST-SBM International Conference "Harmony of Groebner Bases and the Modern Industrial Society". Osaka (JP), 28.06.2012-2.07.2012] R&D Projects: GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : Bayesian network stucture * standard imset * characteristic imset * polyhedral geometry Subject RIV: BA - General Mathematics http://library.utia.cas.cz/separaty/2012/MTR/studeny-polyhedral approach to statistical learning graphical models.pdf

  9. 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.

  10. Graphic/network display system for ROSA-V large scale test facility

    International Nuclear Information System (INIS)

    Integral experiments on thermal-hydraulic response of a pressurized water reactor (PWR) during small break loss-of-coolant accidents (LOCAs) and abnormal transients are being conducted using the ROSA-V Large Scale Test Facility (LSTF). The LSTF is instrumented with more than 2400 transducers to measure the transient system responses which involve complicated interactions among the simulated reactor components. A graphic real-time display system was built to support control room decisions during experiments by providing graphic images of water inventory distribution, temperature distribution, flow etc, based on a number of measurements. It can also produce graphic images from the recorded data. This system has greatly helped engineers make timely decisions during experiments, interpret complicated transient phenomena, and communicate with foreign partners. (author)

  11. Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package

    OpenAIRE

    Scutari, Marco

    2014-01-01

    It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world scenarios. Efficient implementations of score-based structure learning benefit from past and current research in optimisation theory, which can be adapted to the task by using the network score as the objective function to maximise. This is not true...

  12. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

    International Nuclear Information System (INIS)

    In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies

  13. Inference of nonlinear gene regulatory networks through optimized ensemble of support vector regression and dynamic Bayesian networks.

    Science.gov (United States)

    Akutekwe, Arinze; Seker, Huseyin

    2015-08-01

    Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in systems biology. Most methods for modeling and inferring the dynamics of GRNs, such as those based on state space models, vector autoregressive models and G1DBN algorithm, assume linear dependencies among genes. However, this strong assumption does not make for true representation of time-course relationships across the genes, which are inherently nonlinear. Nonlinear modeling methods such as the S-systems and causal structure identification (CSI) have been proposed, but are known to be statistically inefficient and analytically intractable in high dimensions. To overcome these limitations, we propose an optimized ensemble approach based on support vector regression (SVR) and dynamic Bayesian networks (DBNs). The method called SVR-DBN, uses nonlinear kernels of the SVR to infer the temporal relationships among genes within the DBN framework. The two-stage ensemble is further improved by SVR parameter optimization using Particle Swarm Optimization. Results on eight insilico-generated datasets, and two real world datasets of Drosophila Melanogaster and Escherichia Coli, show that our method outperformed the G1DBN algorithm by a total average accuracy of 12%. We further applied our method to model the time-course relationships of ovarian carcinoma. From our results, four hub genes were discovered. Stratified analysis further showed that the expression levels Prostrate differentiation factor and BTG family member 2 genes, were significantly increased by the cisplatin and oxaliplatin platinum drugs; while expression levels of Polo-like kinase and Cyclin B1 genes, were both decreased by the platinum drugs. These hub genes might be potential biomarkers for ovarian carcinoma. PMID:26738192

  14. 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...

  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. 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 (pvalue (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

  17. 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.

  18. Using Bayesian Belief Network (BBN) modelling for rapid source term prediction. Final report

    International Nuclear Information System (INIS)

    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)

  19. 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.

  20. 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)

  1. 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.

  2. Co-occurrence rate networks: towards separate training for undirected graphical models

    OpenAIRE

    Zhu, Zhemin

    2015-01-01

    Dependence is a universal phenomenon which can be observed everywhere. In machine learning, probabilistic graphical models (PGMs) represent dependence relations with graphs. PGMs find wide applications in natural language processing (NLP), speech processing, computer vision, biomedicine, information retrieval, etc. Many traditional models, such as hidden Markov models (HMMs), Kalman filters, can be put under the umbrella of PGMs. The central idea of PGMs is to decompose (factorize) a joint pr...

  3. 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.

  4. Bayesian Nonparametric Graph Clustering

    OpenAIRE

    Banerjee, Sayantan; Akbani, Rehan; Baladandayuthapani, Veerabhadran

    2015-01-01

    We present clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables. As opposed to standard approaches that assume known graph structures, we first estimate the edge structure of the unknown graph using Bayesian neighborhood selection approaches, wherein we account for the uncertainty of graphical structure learning through model-averaged estimates of the suitable parameters. Subsequently, we develop a nonparametric graph cluster...

  5. Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Yunfei; Tong, Jiejuan; Zhang, Liguo, E-mail: lgzhang@tsinghua.edu.cn; Zhang, Qin

    2015-09-15

    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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. 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...

  13. Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study

    Directory of Open Access Journals (Sweden)

    Kibret T

    2014-12-01

    Full Text Available Taddele Kibret,1 Danielle Richer,2 Joseph Beyene1,2 1Department of Mathematics and Statistics, 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada Abstract: Network meta-analysis (NMA has emerged as a useful analytical tool allowing comparison of multiple treatments based on direct and indirect evidence. Commonly, a hierarchical Bayesian NMA model is used, which allows rank probabilities (the probability that each treatment is best, second best, and so on to be calculated for decision making. However, the statistical properties of rank probabilities are not well understood. This study investigates how rank probabilities are affected by various factors such as unequal number of studies per comparison in the network, the sample size of individual studies, the network configuration, and effect sizes between treatments. In order to explore these factors, a simulation study of four treatments (three equally effective treatments and one less effective reference was conducted. The simulation illustrated that estimates of rank probabilities are highly sensitive to both the number of studies per comparison and the overall network configuration. An unequal number of studies per comparison resulted in biased estimates of treatment rank probabilities for every network considered. The rank probability for the treatment that was included in the fewest number of studies was biased upward. Conversely, the rank of the treatment included in the most number of studies was consistently underestimated. When the simulation was altered to include three equally effective treatments and one superior treatment, the hierarchical Bayesian NMA model correctly identified the most effective treatment, regardless of all factors varied. The results of this study offer important insight into the ability of NMA models to rank treatments accurately under several scenarios. The authors recommend that health researchers use rank

  14. Development and Execution of the RUNSAFE Runway Safety Bayesian Belief Network Model

    Science.gov (United States)

    Green, Lawrence L.

    2015-01-01

    One focus area of the National Aeronautics and Space Administration (NASA) is to improve aviation safety. Runway safety is one such thrust of investigation and research. The two primary components of this runway safety research are in runway incursion (RI) and runway excursion (RE) events. These are adverse ground-based aviation incidents that endanger crew, passengers, aircraft and perhaps other nearby people or property. A runway incursion is the incorrect presence of an aircraft, vehicle or person on the protected area of a surface designated for the landing and take-off of aircraft; one class of RI events simultaneously involves two aircraft, such as one aircraft incorrectly landing on a runway while another aircraft is taking off from the same runway. A runway excursion is an incident involving only a single aircraft defined as a veer-off or overrun off the runway surface. Within the scope of this effort at NASA Langley Research Center (LaRC), generic RI, RE and combined (RI plus RE, or RUNSAFE) event models have each been developed and implemented as a Bayesian Belief Network (BBN). Descriptions of runway safety issues from the literature searches have been used to develop the BBN models. Numerous considerations surrounding the process of developing the event models have been documented in this report. The event models were then thoroughly reviewed by a Subject Matter Expert (SME) panel through multiple knowledge elicitation sessions. Numerous improvements to the model structure (definitions, node names, node states and the connecting link topology) were made by the SME panel. Sample executions of the final RUNSAFE model have been presented herein for baseline and worst-case scenarios. Finally, a parameter sensitivity analysis for a given scenario was performed to show the risk drivers. The NASA and LaRC research in runway safety event modeling through the use of BBN technology is important for several reasons. These include: 1) providing a means to clearly

  15. Assessing uncertainty in climate change impacts on water resources: Bayesian neural network approach

    International Nuclear Information System (INIS)

    Climate change impact studies on water resources have so far provided results difficult to use for policy decision and planning of adaptation measures because of the lack of robust uncertainty estimates. There are various sources of uncertainty due to the global circulation models (GCMs) or the regional climate models (RCMs), the emission scenarios, the downscaling techniques, and the hydrological models. The estimation of the overall impact of those uncertainties on the future streamflow or reservoir inflow simulations at the watershed scale remains a difficult and challenging task. The use of multi-model super-ensembles in order to capture the wide range of uncertainties is cumbersome and requires large computational and human resources. As an alternative, a Bayesian Neural Network (BNN) approach is proposed as an effective hydrologic modeling tool for simulating future flows with uncertainty estimates. The BNN model is used with two versions of Canadian GCMs (CGCM1 and CGCM2) with two emission scenarios (SRES B2 and IPCC IS92a), and with one well established statistical downscaling model (SDSM) to simulate daily river flow and reservoir inflow in the Serpent River and the Chute-du-Diable watersheds in northern Quebec. It is found that the 95% uncertainty bands of the BNN mean ensemble flow (i.e. flow simulated using the mean ensemble of downscaled meteorological variables) is capable of encompassing all other possible flows corresponding to various individual downscaled meteorological ensembles whatever the CGCM and the emission scenario used. Specifically, this indicates that the BNN model confidence intervals are capable of including all possible flow variations due to various ensembles of downscaled meteorological variables from two different CGCMs and emission scenarios. Furthermore, the confidence limits of the BNN model also encompasses the flows simulated using another conceptual hydrologic model (namely HBV) whatever the GCM and the emission scenario

  16. 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

  17. 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

  18. 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

  19. 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

  20. Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

    Science.gov (United States)

    Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in

  1. Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study.

    Science.gov (United States)

    Kibret, Taddele; Richer, Danielle; Beyene, Joseph

    2014-01-01

    Network meta-analysis (NMA) has emerged as a useful analytical tool allowing comparison of multiple treatments based on direct and indirect evidence. Commonly, a hierarchical Bayesian NMA model is used, which allows rank probabilities (the probability that each treatment is best, second best, and so on) to be calculated for decision making. However, the statistical properties of rank probabilities are not well understood. This study investigates how rank probabilities are affected by various factors such as unequal number of studies per comparison in the network, the sample size of individual studies, the network configuration, and effect sizes between treatments. In order to explore these factors, a simulation study of four treatments (three equally effective treatments and one less effective reference) was conducted. The simulation illustrated that estimates of rank probabilities are highly sensitive to both the number of studies per comparison and the overall network configuration. An unequal number of studies per comparison resulted in biased estimates of treatment rank probabilities for every network considered. The rank probability for the treatment that was included in the fewest number of studies was biased upward. Conversely, the rank of the treatment included in the most number of studies was consistently underestimated. When the simulation was altered to include three equally effective treatments and one superior treatment, the hierarchical Bayesian NMA model correctly identified the most effective treatment, regardless of all factors varied. The results of this study offer important insight into the ability of NMA models to rank treatments accurately under several scenarios. The authors recommend that health researchers use rank probabilities cautiously in making important decisions. PMID:25506247

  2. Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

    OpenAIRE

    Bošnački Dragan; Odenbrett Maximilian R; Wijs Anton; Ligtenberg Willem; Hilbers Peter

    2012-01-01

    Abstract Background Techniques for reconstruction of biological networks which are based on perturbation experiments often predict direct interactions between nodes that do not exist. Transitive reduction removes such relations if they can be explained by an indirect path of influences. The existing algorithms for transitive reduction are sequential and might suffer from too long run times for large networks. They also exhibit the anomaly that some existing direct interactions are also remove...

  3. Graphical User Interface of an Interactive System for Wireless Sensor Networks: Design and Development

    OpenAIRE

    Hwakyung, Kim

    2008-01-01

    Wireless sensor networks (WSNs) are utilized for ubiquitous and pervasive computing to detect contextual information as well as represent a new monitoring and control ability for applications in industries, transportation and health care. A user is able to gain the effective use of captured context by the sensor network. The WSN software application is an important tool to visualize the information for the different applications. This research provides how to design WSN software application f...

  4. 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 f...

  5. 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 f...

  6. 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

  7. 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. PMID:24155876

  8. 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.

  9. 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

  10. 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

  11. FamSeq: a variant calling program for family-based sequencing data using graphics processing units.

    Directory of Open Access Journals (Sweden)

    Gang Peng

    2014-10-01

    Full Text Available Various algorithms have been developed for variant calling using next-generation sequencing data, and various methods have been applied to reduce the associated false positive and false negative rates. Few variant calling programs, however, utilize the pedigree information when the family-based sequencing data are available. Here, we present a program, FamSeq, which reduces both false positive and false negative rates by incorporating the pedigree information from the Mendelian genetic model into variant calling. To accommodate variations in data complexity, FamSeq consists of four distinct implementations of the Mendelian genetic model: the Bayesian network algorithm, a graphics processing unit version of the Bayesian network algorithm, the Elston-Stewart algorithm and the Markov chain Monte Carlo algorithm. To make the software efficient and applicable to large families, we parallelized the Bayesian network algorithm that copes with pedigrees with inbreeding loops without losing calculation precision on an NVIDIA graphics processing unit. In order to compare the difference in the four methods, we applied FamSeq to pedigree sequencing data with family sizes that varied from 7 to 12. When there is no inbreeding loop in the pedigree, the Elston-Stewart algorithm gives analytical results in a short time. If there are inbreeding loops in the pedigree, we recommend the Bayesian network method, which provides exact answers. To improve the computing speed of the Bayesian network method, we parallelized the computation on a graphics processing unit. This allowed the Bayesian network method to process the whole genome sequencing data of a family of 12 individuals within two days, which was a 10-fold time reduction compared to the time required for this computation on a central processing unit.

  12. Assessment of type 1 diabetes risk conferred by HLA-DRB1, INS-VNTR and PTPN22 genes using the Bayesian network approach.

    Directory of Open Access Journals (Sweden)

    Rosalba Portuesi

    Full Text Available BACKGROUND: Determining genetic risk is a fundamental prerequisite for the implementation of primary prevention trials for type 1 diabetes (T1D. The aim of this study was to assess the risk conferred by HLA-DRB1, INS-VNTR and PTPN22 single genes on the onset of T1D and the joint risk conferred by all these three susceptibility loci using the Bayesian Network (BN approach in both population-based case-control and family clustering data sets. METHODOLOGY/PRINCIPAL FINDINGS: A case-control French cohort, consisting of 868 T1D patients and 73 French control subjects, a French family data set consisting of 1694 T1D patients and 2340 controls were analysed. We studied both samples separately applying the BN probabilistic approach, that is a graphical model that encodes probabilistic relationships among variables of interest. As expected HLA-DRB1 is the most relevant susceptibility gene. We proved that INS and PTPN22 genes marginally influence T1D risk in all risk HLA-DRB1 genotype categories. The absolute risk conferred by carrying simultaneously high, moderate or low risk HLA-DRB1 genotypes together with at risk INS and PTPN22 genotypes, was 11.5%, 1.7% and 0.1% in the case-control sample and 19.8%, 6.6% and 2.2% in the family cohort, respectively. CONCLUSIONS/SIGNIFICANCE: This work represents, to the best of our knowledge, the first study based on both case-control and family data sets, showing the joint effect of HLA, INS and PTPN22 in a T1D Caucasian population with a wide range of age at T1D onset, adding new insights to previous findings regarding data sets consisting of patients and controls <15 years at onset.

  13. 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...

  14. Comparing energy sources for surgical ablation of atrial fibrillation: a Bayesian network meta-analysis of randomized, controlled trials.

    Science.gov (United States)

    Phan, Kevin; Xie, Ashleigh; Kumar, Narendra; Wong, Sophia; Medi, Caroline; La Meir, Mark; Yan, Tristan D

    2015-08-01

    Simplified maze procedures involving radiofrequency, cryoenergy and microwave energy sources have been increasingly utilized for surgical treatment of atrial fibrillation as an alternative to the traditional cut-and-sew approach. In the absence of direct comparisons, a Bayesian network meta-analysis is another alternative to assess the relative effect of different treatments, using indirect evidence. A Bayesian meta-analysis of indirect evidence was performed using 16 published randomized trials identified from 6 databases. Rank probability analysis was used to rank each intervention in terms of their probability of having the best outcome. Sinus rhythm prevalence beyond the 12-month follow-up was similar between the cut-and-sew, microwave and radiofrequency approaches, which were all ranked better than cryoablation (respectively, 39, 36, and 25 vs 1%). The cut-and-sew maze was ranked worst in terms of mortality outcomes compared with microwave, radiofrequency and cryoenergy (2 vs 19, 34, and 24%, respectively). The cut-and-sew maze procedure was associated with significantly lower stroke rates compared with microwave ablation [odds ratio <0.01; 95% confidence interval 0.00, 0.82], and ranked the best in terms of pacemaker requirements compared with microwave, radiofrequency and cryoenergy (81 vs 14, and 1, <0.01% respectively). Bayesian rank probability analysis shows that the cut-and-sew approach is associated with the best outcomes in terms of sinus rhythm prevalence and stroke outcomes, and remains the gold standard approach for AF treatment. Given the limitations of indirect comparison analysis, these results should be viewed with caution and not over-interpreted. PMID:25391388

  15. Development of 3-D fracture network visualization software based on graphical user interface

    Science.gov (United States)

    Young-Hwan, Noh; Jeong-Gi, Um; Yosoon, Choi; Myong-Ho, Park; Jaeyoung, Choi

    2013-04-01

    A sound understanding of the structural characteristics of fractured rock masses is important in designing and maintaining earth structures because their strength, deformability, and hydraulic behavior depend mainly on the characteristics of discontinuity network structures. Despite considerable progress in understanding the structural characteristics of rock masses, the complexity of discontinuity patterns has prevented satisfactory analysis based on a 3-D rock mass visualization model. This research presents the results of studies performed to develop rock mass visualization in 3-D to analysis the mechanical and hydraulic behavior of fractured rock masses. General and particular solutions of non-linear equations of disk-shaped fractures have been derived to calculated lines of intersection and equivalent pipes. Also, program modules of DISK3D, FNTWK3D, BOUNDARY and BDM(borehole data management) have been developed to perform the visualization of fracture network and corresponding equivalent pipes for DFN based fluid flow model. The developed software for the 3-D fractured rock mass visualization model based on MS visual studio can be used to characterize rock mass geometry and network systems effectively. The results obtained in this study will be refined and then combined for use as a tool for assessing geomechanical problems related to strength, deformability and hydraulic behaviors of the fractured rock masses. Acknowledgements. This work was supported by the 2011 Energy Efficiency and Resources Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant.

  16. 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.

  17. A Bayesian Belief Network Approach to Explore Alternative Decisions for Sediment Control and water Storage Capacity at Lago Lucchetti, Puerto Rico

    Science.gov (United States)

    A Bayesian belief network (BBN) was developed to characterize the effects of sediment accumulation on the water storage capacity of Lago Lucchetti (located in southwest Puerto Rico) and to forecast the life expectancy (usefulness) of the reservoir under different management scena...

  18. 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…

  19. 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...

  20. Immigrant maternal depression and social networks. A multilevel Bayesian spatial logistic regression in South Western Sydney, Australia.

    Science.gov (United States)

    Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A; Phung, Hai N; Barnett, Bryanne E W

    2013-09-01

    The purpose is to explore the multilevel spatial distribution of depressive symptoms among migrant mothers in South Western Sydney and to identify any group level associations that could inform subsequent theory building and local public health interventions. Migrant mothers (n=7256) delivering in 2002 and 2003 were assessed at 2-3 weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale scores (EPDS) of >9 and >12. Individual level variables included were: financial income, self-reported maternal health, social support network, emotional support, practical support, baby trouble sleeping, baby demanding and baby not content. The group level variable reported here is aggregated social support networks. We used Bayesian hierarchical multilevel spatial modelling with conditional autoregression. Migrant mothers were at higher risk of having depressive symptoms if they lived in a community with predominantly Australian-born mothers and strong social capital as measured by aggregated social networks. These findings suggest that migrant mothers are socially isolated and current home visiting services should be strengthened for migrant mothers living in communities where they may have poor social networks. PMID:23973180