Learning Bayesian Networks - Rilegato

Neapolitan, Richard E.

Rilegato

ISBN 10: 0130125342 ISBN 13: 9780130125347

Casa editrice: Pearson College Div, 2003

Riassunto

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.

Su questo libro

L'autore

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.

Dalla quarta di copertina

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

Dettagli dell?edizione

• Editore: Pearson College Div
• Data di pubblicazione: 2003
• ISBN 10: 0130125342
• ISBN 13: 9780130125347
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 1
• Numero di pagine: 696