Learning Bayesian Networks

Learning Bayesian Networks

Paperback Artificial Intelligence

By (author) Richard E. Neapolitan

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  • Publisher: Prentice Hall
  • Format: Paperback | 674 pages
  • Dimensions: 185mm x 236mm x 30mm | 1,134g
  • Publication date: 6 April 2003
  • Publication City/Country: Upper Saddle River
  • ISBN 10: 0130125342
  • ISBN 13: 9780130125347
  • Illustrations note: bibliography, index
  • Sales rank: 824,350

Product description

For courses in Bayesian Networks or Advanced Networking focusing on Bayesian networks found in departments of Computer Science, Computer Engineering and Electrical Engineering. Also appropriate as a supplementary text in courses on Expert Systems, Machine Learning, and Artificial Intelligence where the topic of Bayesian Networks is covered. This book provides an accessible and unified discussion of Bayesian networks. It includes discussions of topics related to the areas of artificial intelligence, expert systems and decision analysis, the fields in which Bayesian networks are frequently applied. The author discusses both methods for doing inference in Bayesian networks and influence diagrams. The book also covers the Bayesian method for learning the values of discrete and continuous parameters. Both the Bayesian and constraint-based methods for learning structure are discussed in detail.

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Author information

Richard E. Neapolitan has been a researcher in Bayesian networks and the area of uncertainty in artificial intelligence since the mid-1980s. In 1990, he wrote the seminal text, Probabilistic Reasoning in Expert Systems, which helped to unify the field of Bayesian networks. Dr. Neapolitan has published numerous articles spanning the fields of computer science, mathematics, philosophy of science, and psychology. Dr. Neapolitan is currently professor and chair of Computer Science at Northeastern Illinois University.

Back cover copy

"Learning Bayesian Networks" offers the first accessible and unified text on the study and application of Bayesian networks. This book serves as a key textbook or reference for anyone with an interest in probabilistic modeling in the fields of computer science, computer engineering, and electrical engineering. This text is also a valuable supplemental resource for courses on expert systems, machine learning, and artificial intelligence. Appropriate for classroom teaching or self-instruction, the text is organized to provide fundamental concepts in an accessible, practical format. Beginning with a basic theoretical introduction, the author then provides a comprehensive discussion of inference, methods of learning, and applications based on Bayesian networks and beyond. "Learning Bayesian Networks: "Includes hundreds of examples and problemsMakes learning easy by introducing complex concepts through simple examplesClarifies with separate discussions on statistical development of Bayesian networks and application to causality

Table of contents

Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference: Discrete Variables. 4. More Inference Algorithms. 5. Influence Diagrams. III. LEARNING. 6. Parameter Learning: Binary Variables. 7. More Parameter Learning. 8. Bayesian Structure Learning. 9. Approximate Bayesian Structure Learning. 10. Constraint-Based Learning. 11. More Structure Learning. IV. APPICATIONS. 12. Applications. Bibliography. Index.