Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) [Hardcover]

Robert G. Cowell (Author), Philip Dawid (Author), Steffen L. Lauritzen (Author), David J. Spiegelhalter (Author)

Book Description

ISBN-10: 0387987673 | ISBN-13: 978-0387987675 | Publication Date: July 16, 2007 | Edition: Corrected

Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

Editorial Reviews

Review

From the reviews: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION "This important book fills a void in the graphical Markov models literature. The authors have summarized their extensive and influential work in this area and provided a valuable resource both for educators and for practitioners."

From the Back Cover

Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems. This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature. Robert G. Cowell is a Lecturer in the Faculty of Actuarial Science and Insurance of the Sir John Cass Business School, City of London. He has been working on probabilistic expert systems since 1989. A. Philip Dawid is Professor of Statistics at Cambridge University. He has served as Editor of the Journal of the Royal Statistical Society (Series B), Biometrika and Bayesian Analysis, and as President of the International Society for Bayesian Analysis. He holds the Royal Statistical Society Guy Medal in Bronze and in Silver, and the Snedecor Award for the Best Publication in Biometry. Steffen L. Lauritzen is Professor of Statistics at the University of Oxford. He has served as Editor of the Scandinavian Journal of Statistics. He holds the Royal Statistical Society Guy Medal in Silver and is an Honorary Fellow of the same society. He has, jointly with David J. Spiegelhalter, received the American Statistical Association’s award for an "Outstanding Statistical Application." David J. Spiegelhalter is Winton Professor of the Public Understanding of Risk at Cambridge University and Senior Scientist in the MRC Biostatistics Unit, Cambridge. He has published extensively on Bayesian methodology and applications, and holds the Royal Statistical Society Guy Medal in Bronze and in Silver.

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Product Details

• Hardcover: 336 pages
• Publisher: Springer; Corrected edition (July 16, 2007)
• Language: English
• ISBN-10: 0387987673
• ISBN-13: 978-0387987675
• Product Dimensions: 9.3 x 6.4 x 1 inches
• Shipping Weight: 1.3 pounds (View shipping rates and policies)
• Average Customer Review: 2.3 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #2,422,381 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

16 of 20 people found the following review helpful:

5.0 out of 5 stars A comprehensive advanced study book for graduate students, July 19, 2000

By 

Fred Guo (USA) - See all my reviews

This review is from: Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) (Hardcover)

This book starts with basic probabilistic concepts, graph theory, junction trees, conditional independence, to advanced topics related to learning Bayesian network. Many practical examples are clear and helpful. Good for graduate students of computer science and statistics.

5 of 8 people found the following review helpful:

1.0 out of 5 stars Poor Presentation, February 26, 2006

By 

Chen, Yi (Beijing, China) - See all my reviews

This review is from: Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) (Hardcover)

This book focus on the inference process and parameter learning of bayesian netwroks, structure learning and model selection are only briefly sketched. Although incomplete, it covered enough materials as a introductory textbook in this field. Unfortuantely, however, its poor presentation may make it useless for either engineers or researchers in this field, the author hasn't given an understandable expliantion to any technical details, and the materials are not well organized.

I would like to recommend "learning bayesian network" , which covers almost all the necessary information as an introduction and all the materials are organized in an accessible way.

6 of 17 people found the following review helpful:

1.0 out of 5 stars Extremely technical book, June 11, 2002

By A Customer

This review is from: Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) (Hardcover)

Too synthetic and technical to be used as an introduction to the subject, even for experienced statisticians and graduate students.