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Other titles in the Artificial Intelligence series:
Learning Bayesian Networks (Artificial Intelligence)by Richard E. Neapolitan
Synopses & Reviews
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:
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
About the Author
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.
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.
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