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Learning Bayesian Networks by Richard E. Neapolitan (2019, Trade Paperback)

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Product Identifiers

PublisherPrentice Hall PTR

ISBN-100130125342

ISBN-139780130125347

eBay Product ID (ePID)2318403

Product Key Features

Number of Pages696 Pages

Publication NameLearning Bayesian Networks

LanguageEnglish

Publication Year2019

SubjectNeural Networks, Probability & Statistics / Bayesian Analysis

TypeTextbook

Subject AreaMathematics, Computers

AuthorRichard E. Neapolitan

FormatTrade Paperback

Dimensions

Item Height1.4 in

Item Weight38.3 Oz

Item Length9.1 in

Item Width7.1 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2004-296044

Dewey Edition22

IllustratedYes

Dewey Decimal519.5/42

Table Of ContentPreface. 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.

SynopsisIn 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., 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.

LC Classification NumberQA279.5