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Modeling and Reasoning with Bayesian Networks

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

treatment of exact algorithms covers the main inference paradigms based on elimination and

conditioning and includes advanced methods for compiling Bayesian networks, time-space

tradeoffs, and exploiting local structure of massively connected networks. The treatment of

approximate algorithms covers the main inference paradigms based on sampling and opti-

mization and includes inﬂuential algorithms such as importance sampling, MCMC, and belief

propagation.

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 algo-

rithmic cookbook for the system developer.

Adnan Darwiche is a Professor and Chairman of the Computer Science Department at UCLA.

He is also the Editor-in-Chief for the Journal of Artiﬁcial Intelligence Research (JAIR) and a

AAAI Fellow.

Modeling and Reasoning with

Bayesian Networks

Adnan Darwiche

University of California, Los Angeles

Modeling and Reasoning With Bayesian Net - Darwiche_ Adnan.pdf