Learning Bayesian networks. Richard E. Neapolitan

Learning Bayesian networks



Download Learning Bayesian networks



Learning Bayesian networks Richard E. Neapolitan ebook pdf
Publisher: Prentice Hall
Language: English
Page: 703
ISBN: 0130125342, 9780130125347

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.

From the Back Cover

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: 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



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