Bayesian Artificial Intelligence

by Kevin B. Korb, Ann E. Nicholson

As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors’ website.

Genres: Textbooks, Artificial Intelligence

392 pages, Hardcover

First published September 25, 2003

Reviews

[Darya Biparva]

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]

pas mal

[Bria]

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.