» » Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)
eBook Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) download
IT
Author: Steffen L. Lauritzen,David J. Spiegelhalter,Robert G. Cowell
ISBN: 0387987673
Subcategory: Computer Science
Pages 324 pages
Publisher Springer; Corrected edition (May 20, 2003)
Language English
Category: IT
Rating: 4.6
Votes: 819
ePUB size: 1793 kb
FB2 size: 1350 kb
DJVU size: 1807 kb
Other formats: docx lrf azw lit

eBook Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics) download

by Steffen L. Lauritzen,David J. Spiegelhalter,Robert G. Cowell


Probabilistic expert systems are graphical networks which support the modeling . Winner of the 2002 DeGroot Prize

Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains. 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. 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.

Bayesian Belief Networks have been largely overlooked by Expert Systems practitioners on the grounds that they do not correspond to the human inference mechanism. In this paper, we introduce an explanation mechanism designed to generate intuitive yet probabilistically sound explanations of inferences drawn by a Bayesian Belief Network.

Probabilistic expert systems are graphical networks which support the .

Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Cowell is a Lecturer in the Faculty of Actuarial Science and Statistics of the Sir John Cass Business School, City of London.

Электронная книга "Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks", Robert G. Cowell, Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter

Электронная книга "Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks", Robert G. Spiegelhalter. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks" для чтения в офлайн-режиме.

Cowell, A. Philip Dawid, Steffen L. Jayanta K. Ghosh, 2008. See general information about how to correct material in RePEc.

Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks by Robert G. Cowell, A. Author & abstract. Handle: RePEc:bla:istatr:v:76:y:2008:i:2:p:306-307.

Probabilistic expert systems are graphical netwo. Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.

oceedings{ticNA, title {Probabilistic Networks and Expert Systems}, author {Robert G. Cowell and Steffen L. Lauritzen and A. P. David and David J. Spiegelhalter and Vijay K. Nair and Jerry Lawless and Michael I. Jordan}, year {1999} }. Robert . . Cowell, Steffen L. Lauritzen, +4 authors Michael I. Jordan. From the Publisher: Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation.

Robert G. Phillip Dawid, Steffen L. Lauritzen. 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

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

Probabilistic Networks and Expert Systems : Exact Computational Methods for Bayesian Networks. by Steffen L. Lauritzen and Philip Dawid.

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.

Taulkree
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.
Agalas
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.