Bayesian Artificial Intelligence, 2nd Edition

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.

Table of Contents

Part I. Probabilistic Reasoning
Chapter 1. Bayesian Reasoning
Chapter 2. Introducing Bayesian Networks
Chapter 3. Inference in Bayesian Networks
Chapter 4. Decision Networks
Chapter 5. Applications of Bayesian Networks

Part II. Learning Causal Models
Chapter 6. Learning Probabilities
Chapter 7. Bayesian Network Classifiers
Chapter 8. Learning Linear Causal Models
Chapter 9. Learning Discrete Causal Structure

Part III. Knowledge Engineering
Chapter 10. Knowledge Engineering with Bayesian Networks
Chapter 11. KEBN Case Studies

A. Notation
B. Software Packages