000			03087nam a2200397 i 4500
001			CR9780511811357
003			UkCbUP
005			20240730160801.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s2009||||enk o ||1 0|eng|d
020			_a9780511811357 (ebook)
020			_z9780521884389 (hardback)
020			_z9781107678422 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA279.5 _b.D37 2009
082	0	0	_a519.5/42 _222
100	1		_aDarwiche, Adnan, _d1966- _eauthor. _974708
245	1	0	_aModeling and reasoning with Bayesian networks / _cAdnan Darwiche.
246	3		_aModeling & Reasoning with Bayesian Networks
264		1	_aCambridge : _bCambridge University Press, _c2009.
300			_a1 online resource (xii, 548 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aIntroduction -- Propositional logic -- Probability calculus -- Bayesian networks -- Building Bayesian networks -- Inference by variable elimination -- Inference by factor elimination -- Inference by conditioning -- Models for graph decomposition -- Most likely instantiations -- The complexity of probabilistic inference -- Compiling Bayesian networks -- Inference with local structure -- Approximate inference by belief propagation -- Approximate inference by stochastic sampling -- Sensitivity analysis -- Learning : the maximum likelihood approach -- Learning : the Bayesian approach.
520			_aThis book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
650		0	_aBayesian statistical decision theory _xGraphic methods. _974709
650		0	_aInference. _915305
650		0	_aProbabilities. _94604
650		0	_aModeling. _974710
776	0	8	_iPrint version: _z9780521884389
856	4	0	_uhttps://doi.org/10.1017/CBO9780511811357
942			_cEBK
999			_c84220 _d84220