嶺東科技大學 |

Bayesian artificial intelligence

Record Type:	Electronic resources : monographic
Author:	KorbKevin B.,
Secondary Intellectual Responsibility:	NicholsonAnn E.,
Place of Publication:	Boca Raton, FL
Published:	CRC Press;
Year of Publication:	c2011
Edition:	2nd ed
Description:	1 online resource (xxvii, 463 p.)ill :
Series:	Chapman & Hall/CRC computer science and data analysis series
Subject:	Bayes Theorem -
Subject:	Statistics at Topic -
Subject:	Artificial Intelligence -
Subject:	Neural Networks (Computer) -
Subject:	Bayesian statistical decision theory - Data processing -
Subject:	Machine learning -
Subject:	Neural networks (Computer science) -
Subject:	Electronic books -
Online resource:	http://www.crcnetbase.com/isbn/978-1-4398-1591-5
Notes:	Description based on print version record
Summary:	"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
ISBN:	hbk.
ISBN:	hbk.
ISBN:	9781439815922electronic bk.
ISBN:	1439815925electronic bk.
Content Note:	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

Bayesian artificial intelligence
Korb, Kevin B.

Bayesian artificial intelligence / 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).
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.
Description based on print version record.
Includes bibliographical references and index.
ISBN 9781439815922ISBN 1439815925
Bayes TheoremStatistics at TopicArtificial IntelligenceNeural Networks (Computer)Bayesian statistical decision theoryMachine learningNeural networks (Computer science)Electronic books -- Data processing

Nicholson, Ann E.

Bayesian artificial intelligence
LDR:03810clm2 2200325 4500 001 290227
009 ocn758511962
010 1 $z 9781439815915 $b hbk.
010 1 $z 1439815917 $b hbk.
010 1 $a 9781439815922 $b electronic bk.
010 1 $a 1439815925 $b electronic bk.
100 $a 20140307d2011 k y0engy01 b
101 0 $a eng
102 $a us $b fl
105 $a a a 001yy
200 1 $a Bayesian artificial intelligence $f Kevin B. Korb, Ann E. Nicholson
204 1 $a electronic resource
205 $a 2nd ed
210 $a Boca Raton, FL $d c2011 $c CRC Press
215 1 $a 1 online resource (xxvii, 463 p.) $c ill
225 2 $a Chapman & Hall/CRC computer science and data analysis series
300 $a Description based on print version record
320 $a Includes bibliographical references and index
327 1 $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
330 $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
330 $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
410 0 $1 2001 $a Chapman & Hall/CRC computer science and data analysis series
606 $a Bayes Theorem $2 mesh $3 339998
606 $a Statistics at Topic $2 mesh $3 341451
606 $a Artificial Intelligence $2 mesh $3 271724
606 $a Neural Networks (Computer) $2 mesh $3 341452
606 $a Bayesian statistical decision theory $x Data processing $2 lc $3 341453
606 $a Machine learning $2 lc $3 271642
606 $a Neural networks (Computer science) $3 119513
606 $a Electronic books $2 lc $3 321766
676 $a 519.5/42 $v 22
680 $a QA179.5 $b K67 2011
686 $a QA 179.5
700 1 $a Korb $b Kevin B. $3 341449
702 1 $a Nicholson $b Ann E. $3 341450
856 4 0 $u http://www.crcnetbase.com/isbn/978-1-4398-1591-5