Probabilistic networks and expert systems

Statistics for engineering and information science

Robert G. Cowell, Philip A. Dawid, Steffen L. Lauritzen, David J. Spielgelhalter

334 pages, parution le 01/01/1999

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Résumé

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.

Sommaire

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

A Conjugate Analysis for Discrete Data
B Gibbs Sampling
C Information and Software on the World Wide Web

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Robert G. Cowell, Philip A. Dawid, Steffen L. Lauritzen, David J. Spielgelhalter
Parution	01/01/1999
Nb. de pages	334
Format	16 x 24
Couverture	Relié
Poids	624g
Intérieur	Noir et Blanc
EAN13	9780387987675
ISBN13	978-0-387-98767-5