Bayesian Artificial Intelligence

by Kevin B. Korb

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 discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.

New to the Second Edition

New chapter on Bayesian network classifiers
New section on object-oriented Bayesian networks
New section that addresses foundational problems with causal discovery and Markov blanket discovery
New section that covers methods of evaluating causal discovery programs
Discussions of many common modeling errors
New applications and case studies
More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks

Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems.

Web Resource
The book’s website at www.csse.monash.edu.au/bai/book/book.... offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Genres: Textbooks, Artificial Intelligence

491 pages, Kindle Edition

First published September 25, 2003

Reviews

[Darya Biparva · Rating 4 out of 5]

I think it's a good start for people interested in graphical models and bayesian networks. It's certainly an easier read than Pearl. It gives some good intuitions rather than very strict theoretical background. I'd recommend it to people who want to start doing research on graphical models before reading Pearl and Koller.

[Jovany Agathe · Rating 2 out of 5]

pas mal

[Bria · Rating 3 out of 5]

The first four or so chapters are good, and I'm sure the rest would be fine if I were currently programming any sort of Bayesian network, but since I'm not, they were a little tedious.