Bayesian Artificial Intelligence, Second Edition

Kevin B. Korb, Ann E. Nicholson

December 16, 2010 by CRC Press
Reference - 491 Pages - 159 B/W Illustrations
ISBN 9781439815915 - CAT# K10816
Series: Chapman & Hall/CRC Computer Science & Data Analysis


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  • Covers several applications of Bayesian networks, including cardiovascular and ecological risk assessments
  • Incorporates recent work on interventions and experimental data
  • Describes naive Bayes models and other classifiers
  • Requires only a basic understanding of algebra and calculus
  • Includes a guide to software tools in an appendix
  • Presents exercises at the end of each chapter
  • Provides software, examples, and exercises on the book’s website


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.


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