000			03575nam a2200361 a 4500
005			20250930132644.0
008			111031s2011 flua b 001 0 eng
020			_a9781439815915 (hbk.) _cRM258.50
039		9	_a201111211600 _bzabidah _c201111211600 _dzabidah _y10-31-2011 _zfarid
040			_aUKM
090			_aQA279.5.K677 2011
090			_aQA279.5 _b.K677 2011
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, _c2011.
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) _960531
700	1		_aNicholson, Ann E.
907			_a.b15185953 _b2021-05-28 _c2019-11-12
942			_c01 _n0 _kQA279.5.K677 2011
914			_avtls003481874
990			_aza
991			_aFakulti Sains & Teknologi (FST)
998			_at _b2011-05-10 _cm _da _feng _gflu _y0 _z.b15185953
999			_c502812 _d502812