Modeling and Reasoning with Bayesian Networks

by Adnan Darwiche

This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Genres: Computer Science, Artificial Intelligence, Mathematics, Reference

562 pages, Hardcover

First published January 1, 2009

Reviews

[Daniel Korzekwa · Rating 4 out of 5]

It's an excellent book on Bayesian Networks. After reading it I gained a solid understanding on how Bayesian Networks work as well as how to design and use them to solve real probabilistic problems. This book is accompanied by a tool for modelling and reasoning with Bayesian Network, which was created by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. I'm planning to adopt Bayesian Networks in analyzing betting exchange markets and reading such a great book gave me all I needed to apply Bayesian Networks in my research.