000			03438nam a2200301 a 4500
001			vtls000055493
003			KUKTEM
005			20251125093021.0
008			110912t2010 flua f 001 0 eng d
020			_a9781439815915 (hardback)
039		9	_a201207091552 _bFida _c201206291620 _dasmadi _c201206081059 _dasmadi _c201205141141 _dasmadi _y201109121230 _zsafura
040			_aUMP
090			_aQA279.5 .K67 2010
100	1		_aKorb, Kevin B.
245	1	0	_aBayesian artificial intelligence / _cKevin B. Korb, Ann E. Nicholson
250			_a2nd ed.
260			_aBoca Raton, FL : _bCRC Press, _cc2010
300			_axxvii, 463 p. : _bill. ; _c24 cm.
490	0		_aChapman & hall/crc computer science & data analysis ; _v16
504			_aIncludes bibliographical references and index
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"-- _cProvided 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"-- _cProvided by publisher
650		0	_aBayesian statistical decision theory _xData processing
650		0	_aMachine learning
650		0	_aNeural networks (Computer science)
999			_aVIRTUA40 _c60560 _d60566
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2500*2600*3000*4900*5040*5200*5201*6500*6501*6502*9992
942	0	0	_01