Learning Bayesian Networks
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.
- Paperback | 674 pages
- 185.42 x 236.22 x 30.48mm | 1,133.98g
- 06 Apr 2003
- Pearson Education (US)
- Upper Saddle River, NJ, United States
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Back cover copy
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 problems Makes learning easy by introducing complex concepts through simple examples Clarifies with separate discussions on statistical development of Bayesian networks and application to causality
Table of contents
1. Introduction to Bayesian Networks.
2. More DAG/Probability Relationships.
3. Inference: Discrete Variables.
4. More Inference Algorithms.
5. Influence Diagrams.
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.
About Richard E. Neapolitan