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Product Identifiers

PublisherSpringer New York

ISBN-100387718230

ISBN-139780387718231

eBay Product ID (ePID)60461807

Product Key Features

Number of PagesXii, 324 Pages

LanguageEnglish

Publication NameProbabilistic Networks and Expert Systems : Exact Computational Methods for Bayesian Networks

Publication Year2007

SubjectExpert Systems, Probability & Statistics / General, Intelligence (Ai) & Semantics

TypeTextbook

Subject AreaMathematics, Computers

AuthorPhilip Dawid, Robert G. Cowell, Steffen L. Lauritzen, David J. Spiegelhalter

SeriesInformation Science and Statistics Ser.

FormatTrade Paperback

Dimensions

Item Weight18.2 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Intended AudienceScholarly & Professional

ReviewsFrom 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 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."

Number of Volumes1 vol.

IllustratedYes

Table Of ContentLogic, 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.

SynopsisWINNER OF THE 2001 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., The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. The book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms., The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. This book will be an essential reference for people interested in artificial intelligence in both computer science and statistics., 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.

LC Classification NumberQA273.A1-274.9