Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks

Paperback | July 16, 2007

byRobert G. Cowell, Philip Dawid, Steffen L. Lauritzen

not yet rated|write a review
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.

Pricing and Purchase Info

$154.41 online
$154.95 list price
In stock online
Ships free on orders over $25

From the Publisher

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 ...

From the Jacket

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 ...

Format:PaperbackDimensions:336 pages, 9.25 × 6.1 × 0 inPublished:July 16, 2007Publisher:Springer New YorkLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0387718230

ISBN - 13:9780387718231

Look for similar items by category:

Customer Reviews of Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks

Reviews

Extra Content

Table of Contents

Introduction.- Logic, Uncertainty, and Probability.- Building and Using Probabilistic Networks.- Graph Theory.- Markov Properties on Graphs.- Discrete Networks.- Gaussian and Mixed Discrete-Gaussian Networks.- Discrete Multistage Decision Networks.- Learning About Probabilities.- Checking Models Against Data.- Structural Learning.

Editorial Reviews

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."