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001			978-3-030-61943-5
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020			_a9783030619435 _9978-3-030-61943-5
024	7		_a10.1007/978-3-030-61943-5 _2doi
050		4	_aQA76.9.M35
050		4	_aQA276-280
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072		7	_aCOM014000 _2bisacsh
072		7	_aUYAM _2thema
072		7	_aPBT _2thema
082	0	4	_a004.0151 _223
100	1		_aSucar, Luis Enrique. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aProbabilistic Graphical Models _h[electronic resource] : _bPrinciples and Applications / _cby Luis Enrique Sucar.
250			_a2nd ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXXVIII, 355 p. 167 illus., 144 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6594
505	0		_aIntroduction -- Probability Theory -- Graph Theory -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Decision Graphs -- Markov Decision Processes -- Partially Observable Markov Decision Processes -- Relational Probabilistic Graphical Models -- Graphical Causal Models -- Causal Discovery -- Deep Learning and Graphical Models -- A Python Library for Inference and Learning -- Glossary -- Index.
520			_aThis fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises. 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 Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors 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, andphysics. 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	_aComputer science _xMathematics.
650		0	_aMathematical statistics.
650		0	_aArtificial intelligence.
650		0	_aPattern recognition systems.
650		0	_aProbabilities.
650		0	_aElectrical engineering.
650	1	4	_aProbability and Statistics in Computer Science.
650	2	4	_aArtificial Intelligence.
650	2	4	_aAutomated Pattern Recognition.
650	2	4	_aProbability Theory.
650	2	4	_aElectrical and Electronic Engineering.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030619428
776	0	8	_iPrinted edition: _z9783030619442
776	0	8	_iPrinted edition: _z9783030619459
830		0	_aAdvances in Computer Vision and Pattern Recognition, _x2191-6594
856	4	0	_uhttps://doi.org/10.1007/978-3-030-61943-5
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c185296 _d185296