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008			150619s2015 xxk| s |||| 0|eng d
020			_a9781447166993 _z978-1-4471-6699-3
024	7		_a10.1007/978-1-4471-6699-3 _2doi
040			_aDLC _beng _erda _cDLC _dTR-AnTOB
041	0		_aeng
050		4	_aQA276-280
072		7	_aUYAM _2bicssc
072		7	_aCOM077000 _2bisacsh
072		7	_aUYAM _2thema
072		7	_aUFM _2thema005.55 _223
100	1		_aSucar, Luis Enrique _eauthor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9152020
245	1	0	_aProbabilistic Graphical Models : _bPrinciples and Applications / _cby Luis Enrique Sucar.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2015.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
505	0		_aPart I: Fundamentals -- Introduction -- Probability Theory -- Graph Theory -- Part II: Probabilistic Models -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Part III: Decision Models -- Decision Graphs -- Markov Decision Processes -- Part IV: Relational and Causal Models -- Relational Probabilistic Graphical Models -- Graphical Causal Models.
520			_aThis accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Describes the practical application of the different techniques Examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models Provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter Suggests possible course outlines for instructors in the preface This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.
650		0	_aArtificial intelligence _91543
650		0	_aPattern recognition systems _91133
650		0	_aMathematical statistics _9496
650		0	_aElectrical engineering _916722
650		0	_aProbabilities _9818
655		0	_aElectronic books _92032
710	2		_aSpringerLink (Online service) _959873
856	4	0	_uhttps://doi.org/10.1007/978-1-4471-6699-3 _3Springer eBooks _zOnline access link to the resource
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999			_c200434077 _d52289