Modeling and Reasoning with Bayesian Networks (Hardcover)

~ (Author) "Automated reasoning has been receiving much interest from a number of fields, including philosophy, cognitive science, and computer science..." (more)
Key Phrases: query mode, jointree algorithm, jointree property, Consider Figure, Monte Carlo, Consider Exercise (more...)

Editorial Reviews

Review

"Bayesian networks are as important to AI and machine learning as Boolean circuits are to computer science. Adnan Darwiche is a leading expert in this area and this book provides a superb introduction to both theory and practice, with much useful material not found elsewhere."
Stuart Russell, University of California, Berkeley

"Bayesian networks have revolutionized AI. This book gives a clear and insightful overview of what we have learnt in 25 years of research, by one of the leading researchers. It is both accessible and deep, making it essential reading for both beginning students and advanced researchers."
David Poole, Professor of Computer Science University of British Columbia

"Bayesian Networks are models for representing and using probabilistic knowledge, introduced in the field of Artificial Intelligence by Judea Pearl back in the 1980's. Since then many inference methods, learning algorithms, and applications of Bayesian Networks have been developed, tested, and deployed, making Bayesian Networks into a solid and established framework for reasoning with uncertain information. Adnan Darwiche, a leading researcher in the field, has produced a book that provides a clear, coherent, and advanced introduction to Bayesian Networks that will appeal to students, practitioners, and scientists alike. A wonderful exposition that starts with propositional logic and probability calculus, and ends with state-of-the-art inference methods and learning algorithms. In my view, the best book on Bayesian Networks since Pearl's seminal book."
Hector Geffner, ICREA and Universitat Pompeu Fabra

Product Description

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.

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Product Details

• Hardcover: 560 pages
• Publisher: Cambridge University Press; 1 edition (April 6, 2009)
• Language: English
• ISBN-10: 0521884381
• ISBN-13: 978-0521884389
• Product Dimensions: 10 x 7.3 x 1.2 inches
• Shipping Weight: 2.5 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #241,353 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

9 of 9 people found the following review helpful:

5.0 out of 5 stars Bayesian networks explained simply and clearly., April 7, 2009

By	Ethan L. Schreiber "Ethan Schreiber" (New York, NY) - See all my reviews (REAL NAME)

While pursuing my PhD at UCLA, I took Professor Darwiche's classes and had the privilege of using the pre-release version of this book. Before taking professor Darwiche's class, I had spent a good deal of time while working on my masters degree working on Bayesian networks. I found that much of the literature on Bayesian networks was inaccessible to someone new to the field. There simply was not a comprehensive resource that would explain Bayesian networks from the beginning in a through and clear manner. I say with confidence that this has now changed.

The book begins with the fundamentals of logic. It continues on to describe the properties of the Bayesian network graph such as independence relationships and d-separation as well as how the parameters of a Bayesian network work.

There are then in depth discussions of the various queries we are able to perform on Bayesian networks and the algorithms for accomplishing them. These include queries such as probability of evidence, most probable explanation and probabilistic inference. Techniques such as summing out, pearl's polytree algorithm and belief propagation are described elogently and clearly.

The book also contains information on the current state of the art research going on in the field. This book is a valuable resource for anyone new to or ingrained in the use of Bayesian Networks. A book of this scope and target was sorely needed and I for one am glad it has arrived. I would and have recommended this to any of my peers in the field.

4 of 4 people found the following review helpful:

5.0 out of 5 stars Excellent text, May 30, 2009

By	J. Bergman - See all my reviews (REAL NAME)

Having taken Professor Darwiche's course on Bayesian Networks, I was excited to get my hands on this book, which is a culmination of the notes from that class and his research on the subject.

This is an excellent text, with very clear explanations and step by step descriptions in pseudo code of the important algorithms in the text.

The first few chapters lay the probabilistic foundations needed for understanding Bayesian Networks and the conditional independences such networks encode.

Chapter 5 gives examples in several different domains of using Bayesian Networks to model different systems and answer queries about them.

After this, the book gets into the meat of its primary focus, efficient probabilistic inference in the context of Bayesian Networks.

It lays out various algorithms for exact inference using jointrees or recursive conditioning, and the complexity and trade-offs of the different approaches.

It further details further refinements that can reduce networks in some cases for even better performance.

After this, it details approximate inference techniques including sampling and belief propagation.

Chapter 14 on belief propagation is especially good, with its discussions on the semantics of belief propagation, generalized belief propagation, and an alternative formulation of generalized belief propagation edge deletion belief propagation.

The last few chapters also delve into learning Bayesian Networks structure and parameters.

All in all, this book will give an in depth knowledge of exact and approximate inference in Bayesian networks and a good overview of learning and applying these models to various domains.