Probabilistic Networks and Expert Systems (Information Science and Statistics) (Hardcover)

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

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."

Product Description
Winner of the 2002 DeGroot Prize. Awarded by the International Society for Bayesian Analysis to a book judged to represent an important, timely, thorough, and notably original contribution to the statistics literature.

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 book will be of interest to researchers and graduate students in artificial intelligence who desire an understanding of the mathematical and statistical basis of probabilistic expert systems, and to students and research workers in statistics wanting an introduction to this fascinating and rapidly developing field. The careful attention to detail will also make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems.

Robert G. Cowell is a Lecturer in the Faculty of Actuarial Science and Statistics of the Sir John Cass Business School, City of London. He has been working in the field of probabilistic expert systems for over a decade, and has published a number of research and tutorial articles in the area.

A. Philip Dawid is Pearson Professor of Statistics at University College London. He has served as Editor of the Journal of the Royal Statistical Society (Series B) and of Biometrika, 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 G. W. Snedecor Award for the Best Publication in Biometry.

Steffen L. Lauritzen is Professor of Mathematics and Statistics at the University of Aalborg. He has served as Editor of the Scandinavian Journal of Statistics. He holds the Royal Statistical 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 a Senior Scientist at the MRC Biostatistics Unit in the Cambridge University Institute of Public Health. 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: 340 pages
• Publisher: Springer; Corrected edition (May 20, 2003)
• 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.com Sales Rank: #1,270,463 in Books (See Bestsellers 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 "[e-mail]" (USA) - See all my reviews

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 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

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