Bayesian Artificial Intelligence  book cover
2nd Edition

Bayesian Artificial Intelligence

ISBN 9781439815915
Published December 16, 2010 by CRC Press
491 Pages 159 B/W Illustrations

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Book Description

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 Edition

    • New chapter on Bayesian network classifiers
    • New section on object-oriented Bayesian networks
    • New section that addresses foundational problems with causal discovery and Markov blanket discovery
    • New section that covers methods of evaluating causal discovery programs
    • Discussions of many common modeling errors
    • New applications and case studies
    • More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks

    Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems.

    Web Resource
    The book’s website at offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

    Table of Contents

    Bayesian Reasoning

    Reasoning under uncertainty
    Uncertainty in AI
    Probability calculus
    Interpretations of probability
    Bayesian philosophy
    The goal of Bayesian AI
    Achieving Bayesian AI
    Are Bayesian networks Bayesian?

    Introducing Bayesian Networks
    Bayesian network basics
    Reasoning with Bayesian networks
    Understanding Bayesian networks
    More examples

    Inference in Bayesian Networks
    Exact inference in chains
    Exact inference in polytrees
    Inference with uncertain evidence
    Exact inference in multiply-connected networks
    Approximate inference with stochastic simulation
    Other computations
    Causal inference

    Decision Networks
    Decision network basics
    Sequential decision making
    Dynamic Bayesian networks
    Dynamic decision networks
    Object-oriented Bayesian networks

    Applications of Bayesian Networks
    A brief survey of BN applications
    Cardiovascular risk assessment
    Goulburn Catchment Ecological Risk Assessment
    Bayesian poker
    Ambulation monitoring and fall detection
    A Nice Argument Generator (NAG)

    Learning Probabilities
    Parameterizing discrete models
    Incomplete data
    Learning local structure

    Bayesian Network Classifiers
    Naive Bayes models
    Semi-naive Bayes models
    Ensemble Bayes prediction
    The evaluation of classifiers

    Learning Linear Causal Models
    Path models
    Constraint-based learners

    Learning Discrete Causal Structure
    Cooper and Herskovits’ K2
    MDL causal discovery
    Metric pattern discovery
    CaMML: Causal discovery via MML
    CaMML stochastic search
    Problems with causal discovery
    Evaluating causal discovery

    Knowledge Engineering with Bayesian Networks

    The KEBN process
    Stage 1: BN structure
    Stage 2: probability parameters
    Stage 3: decision structure
    Stage 4: utilities (preferences)
    Modeling example: missing car
    Incremental modeling

    KEBN Case Studies
    Bayesian poker revisited
    An intelligent tutoring system for decimal understanding
    Goulburn Catchment Ecological Risk Assessment
    Cardiovascular risk assessment

    Appendix A: Notation
    Appendix B: Software Packages



    A Summary, Notes, and Problems appear at the end of each chapter.

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    Kevin B. Korb is a Reader in the Clayton School of Information Technology at Monash University in Australia. He earned his Ph.D. from Indiana University. His research encompasses causal discovery, probabilistic causality, evaluation theory, informal logic and argumentation, artificial evolution, and philosophy of artificial intelligence.

    Ann E. Nicholson an Associate Professor in the Clayton School of Information Technology at Monash University in Australia. She earned her Ph.D. from the University of Oxford. Her research interests include artificial intelligence, probabilistic reasoning, Bayesian networks, knowledge engineering, plan recognition, user modeling, evolutionary ethics, and data mining


    … useful insights on Bayesian reasoning. … There are extensive examples of applications and case studies. … The exposition is clear, with many comments that help set the context for the material that is covered. The reader gets a strong sense that Bayesian networks are a work in progress.
    —John H. Maindonald, International Statistical Review (2011), 79

    Praise for the First Edition:
    … this excellent book would also serve well for final year undergraduate courses in mathematics or statistics and is a solid first reference text for researchers wanting to implement Bayesian belief network (BBN) solutions for practical problems. … beautifully presented, nicely written, and made accessible. Mathematical ideas, some quite deep, are presented within the flow but do not get in the way. This has the advantage that students can see and interpret the mathematics in the practical context, whereas practitioners can acquire, to personal taste, the mathematical seasoning. If you are interested in applying BBN methods to real-life problems, this book is a good place to start…
    Journal of the Royal Statistical Society, Series A, Vol. 157(3)