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Probabilistic Networks and Expert Systems (Statistics for Engineering and Information Science)

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Synopses & Reviews

Publisher Comments:

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.

Synopsis:

"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."--BOOK JACKET. "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."--BOOK JACKET.

Synopsis:

The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. This book should be a useful reference for people interested in artificial intelligence in both computer science and statistics.

Synopsis:

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.

Synopsis:

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.

Description:

Includes bibliographical references (p. [281]-305) and indexes.

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.

Product Details

ISBN:
9780387987675
Author:
Cowell, Robert G.
Author:
Lauritzen, Steffen L.
Author:
Dawid, A. Philip
Author:
Cowell
Author:
Dawid, Philip
Author:
Spiegelhalter, David J.
Author:
Spiegelhater, David J.
Publisher:
Springer
Location:
New York, NY
Subject:
Statistics
Subject:
Expert Systems
Subject:
Expert systems (computer science)
Subject:
Probabilities
Subject:
Probability & Statistics - General
Subject:
Artificial Intelligence - General
Subject:
Probabilistic Networks
Subject:
Intelligence (AI) & Semantics
Subject:
Operating Systems - General
Subject:
Bayesian network
Subject:
Graphical model
Subject:
Junction Tree
Subject:
Machine learning
Subject:
Probability propagation
Subject:
Statistical Theory and Methods
Subject:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Subject:
Probability Theory and Stochastic Processes
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Language, literature and biography
Subject:
mathematics and statistics
Subject:
Mathematical statistics
Subject:
Distribution (Probability theory)
Subject:
Artificial Intelligence
Copyright:
Edition Number:
1
Edition Description:
1999. Corr. 2nd
Series:
Information Science and Statistics
Publication Date:
June 1999
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
336
Dimensions:
235 x 155 mm 1440 gr

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Probabilistic Networks and Expert Systems (Statistics for Engineering and Information Science) New Hardcover
0 stars - 0 reviews
$209.75 In Stock
Product details 336 pages Springer-Verlag - English 9780387987675 Reviews:
"Synopsis" by , "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."--BOOK JACKET. "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."--BOOK JACKET.
"Synopsis" by , The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. This book should be a useful reference for people interested in artificial intelligence in both computer science and statistics.
"Synopsis" by , 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.
"Synopsis" by , 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.
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