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Bayesian artificial intelligence

Korb, Kevin B.

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Bayesian artificial intelligence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian artificial intelligence/ Kevin B. Korb, Ann E. Nicholson.
作者:	Korb, Kevin B.
其他作者:	Nicholson, Ann E.
出版者:	Boca Raton, FL :CRC Press, : c2011.,
面頁冊數:	1 online resource (xxvii, 463 p.) :ill.
內容註:	I. PROBABILISTIC REASONING: Bayesian reasoning -- Introducing Bayesian networks -- Inference in Bayesian networks -- Decision networks -- Applications of Bayesian networks -- II. LEARNING CAUSAL MODELS: Learning probabilities -- Bayesian network classifiers -- Learning linear causal models -- Learning discrete causal structure -- III. KNOWLEDGE ENGINEERING: Knowledge engineering with Bayesian networks -- KEBN case studies.
標題:	Bayes Theorem. -
電子資源:	http://www.crcnetbase.com/isbn/9781439815915

Bayesian artificial intelligence
Korb, Kevin B.

Bayesian artificial intelligence[electronic resource] /Kevin B. Korb, Ann E. Nicholson. - 2nd ed. - Boca Raton, FL :CRC Press,c2011. - 1 online resource (xxvii, 463 p.) :ill. - Chapman & Hall/CRC computer science and data analysis series. - Series in computer science and data analysis..

Includes bibliographical references and index.

I. PROBABILISTIC REASONING: Bayesian reasoning -- Introducing Bayesian networks -- Inference in Bayesian networks -- Decision networks -- Applications of Bayesian networks -- II. LEARNING CAUSAL MODELS: Learning probabilities -- Bayesian network classifiers -- Learning linear causal models -- Learning discrete causal structure -- III. KNOWLEDGE ENGINEERING: Knowledge engineering with Bayesian networks -- KEBN case studies.

"Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The books website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text"--Provided by publisher.Subjects--Topical Terms:

709996
Bayes Theorem.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA179.5 / .K67 2011

Dewey Class. No.: 519.5/42

National Library of Medicine Call No.: QA 179.5

Bayesian artificial intelligence
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050 0 0 $a QA179.5 $b .K67 2011
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100 1 $a Korb, Kevin B. $3 1070456
245 1 0 $a Bayesian artificial intelligence $h [electronic resource] / $c Kevin B. Korb, Ann E. Nicholson.
250 $a 2nd ed.
260 $a Boca Raton, FL : $b CRC Press, $c c2011.
300 $a 1 online resource (xxvii, 463 p.) : $b ill.
490 1 $a Chapman & Hall/CRC computer science and data analysis series
504 $a Includes bibliographical references and index.
505 0 $a I. PROBABILISTIC REASONING: Bayesian reasoning -- Introducing Bayesian networks -- Inference in Bayesian networks -- Decision networks -- Applications of Bayesian networks -- II. LEARNING CAUSAL MODELS: Learning probabilities -- Bayesian network classifiers -- Learning linear causal models -- Learning discrete causal structure -- III. KNOWLEDGE ENGINEERING: Knowledge engineering with Bayesian networks -- KEBN case studies.
520 $a "Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The books website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text"--Provided by publisher.
520 $a "The second edition of this bestseller provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. This edition contains a new chapter on Bayesian network classifiers and a new section on object-oriented Bayesian networks, along with new applications and case studies. It includes a new section that addresses foundational problems with causal discovery and Markov blanket discovery and a new section that covers methods of evaluating causal discovery programs. The book also offers more coverage on the uses of causal interventions to understand and reason with causal Bayesian networks. Supplemental materials are available on the book's website"--Provided by publisher.
588 $a Description based on print version record.
650 2 $a Bayes Theorem. $3 709996
650 2 $a Statistics at Topic. $3 2018153
650 2 $a Artificial Intelligence. $3 769149
650 2 $a Neural Networks (Computer) $3 712252
650 0 $a Bayesian statistical decision theory $x Data processing. $3 731262
650 0 $a Machine learning. $3 533906
650 0 $a Neural networks (Computer science) $3 532070
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Nicholson, Ann E. $3 1535373
830 0 $a Series in computer science and data analysis. $3 939289
856 4 0 $u http://www.crcnetbase.com/isbn/9781439815915