Learning Bayesian Networks

Pearson Prentice Hall, 2004 - 674 pages

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.

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Table des matières

Introduction to Bayesian Networks
More DAGProbability Relationships
Droits d'auteur

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Expressions et termes fréquents

À propos de l'auteur (2004)

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.

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