Bayesian Artificial Intelligence
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 www.csse.monash.edu.au/bai/book/book.html 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.

    1101706028
    Bayesian Artificial Intelligence
    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 www.csse.monash.edu.au/bai/book/book.html 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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      Bayesian Artificial Intelligence

      Bayesian Artificial Intelligence

      Bayesian Artificial Intelligence
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      Bayesian Artificial Intelligence

      Bayesian Artificial Intelligence

      Overview

      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 www.csse.monash.edu.au/bai/book/book.html 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.

        Product Details

        ISBN-13: 9781032477657
        Publisher: CRC Press
        Publication date: 01/21/2023
        Series: Chapman & Hall/CRC Computer Science & Data Analysis
        Edition description: 2nd ed.
        Pages: 492
        Product dimensions: 6.12(w) x 9.19(h) x (d)

        About the Author

        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

        Table of Contents

        Probabilistic Reasoning. Learning Causal Models. Knowledge Engineering. Appendices. References. Index.

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