ABSTRACT

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors dis

part |2 pages

I PROBABILISTIC REASONING

chapter 1|26 pages

Bayesian Reasoning

chapter 2|26 pages

Introducing Bayesian Networks

chapter 3|42 pages

Inference in Bayesian Networks

chapter 4|36 pages

Decision Networks

chapter 5|48 pages

Applications of Bayesian Networks

part |4 pages

II LEARNING CAUSAL MODELS

chapter 6|20 pages

Learning Probabilities

chapter 7|26 pages

Bayesian Network Classifiers

chapter 8|24 pages

Learning Linear Causal Models

chapter 9|38 pages

Learning Discrete Causal Structure

part |4 pages

III KNOWLEDGE ENGINEERING

chapter 10|64 pages

Knowledge Engineering with Bayesian Networks

chapter 11|44 pages

KEBN Case Studies