Learning Bayesian Networks
Published on 14. April 2003
Book
Paperback/Softback
696 pages
978-0-13-012534-7 (ISBN)
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.
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.
More details
Person
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.
Content
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.
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.