Learning Bayesian networks
著者
書誌事項
Learning Bayesian networks
（Prentice Hall series in artificial intelligence）
Pearson Prentice Hall, c2004
大学図書館所蔵 全39件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
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