000			04604nam a22004455i 4500
999			_c334688 _d334688 _x1
001			334688
003			ES-MaUEC
005			20230102114736.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			201223s2021 gw a s |||| 0|eng d
020			_a9783030619435
024	7		_a10.1007/978-3-030-61943-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA279 _b2021 EB
100	1		_aSucar, Luis Enrique _eautor _0(orcid)0000-0002-3685-5567 _1https://orcid.org/0000-0002-3685-5567 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aProbabilistic Graphical Models _bPrinciples and Applications _cby Luis Enrique Sucar
250			_aSecond edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXVIII, 355 páginas) _b167 ilustraciones, 144 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aAdvances in Computer Vision and Pattern Recognition _x2191-6586
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	3		_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, 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.
988			_aSpringer_Computer_2021
650		7	_2embne _9678614 _aModelado gráfico (Estadística)
650		7	_2embne _aIncertidumbre (Teoría de la información) _9667868
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9783030619428
776	0	8	_iPrinted edition: _z9783030619442
776	0	8	_iPrinted edition: _z9783030619459
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-61943-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _ek _zSI