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001433270 020__ $$a9783030619435$$q(electronic bk.)
001433270 020__ $$a3030619435$$q(electronic bk.)
001433270 020__ $$a9783030619442$$q(print)
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001433270 0247_ $$a10.1007/978-3-030-61943-5$$2doi
001433270 035__ $$aSP(OCoLC)1228878871
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001433270 049__ $$aISEA
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001433270 1001_ $$aSucar, L. Enrique,$$d1957-$$eauthor.
001433270 24510 $$aProbabilistic graphical models :$$bprinciples and applications /$$cLuis Enrique Sucar.
001433270 250__ $$aSecond edition.
001433270 264_1 $$aCham :$$bSpringer,$$c[2021]
001433270 300__ $$a1 online resource :$$billustrations (some color)
001433270 336__ $$atext$$btxt$$2rdacontent
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001433270 338__ $$aonline resource$$bcr$$2rdacarrier
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001433270 347__ $$bPDF
001433270 4901_ $$aAdvances in computer vision and pattern recognition,$$x2191-6586
001433270 504__ $$aIncludes bibliographical references and index.
001433270 5050_ $$aIntroduction -- Probability Theory -- Graph Theory -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Decision Graphs -- Markov Decision Processes -- Partially Observable Markov Decision Processes -- Relational Probabilistic Graphical Models -- Graphical Causal Models -- Causal Discovery -- Deep Learning and Graphical Models -- A Python Library for Inference and Learning -- Glossary -- Index.
001433270 506__ $$aAccess limited to authorized users.
001433270 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, 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.
001433270 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 26, 2021).
001433270 650_0 $$aUncertainty (Information theory)
001433270 650_0 $$aGraphical modeling (Statistics)
001433270 650_6 $$aIncertitude (Théorie de l'information)
001433270 650_6 $$aModèles graphiques (Statistique)
001433270 655_0 $$aElectronic books.
001433270 77608 $$iPrint version:$$z3030619427$$z9783030619428$$w(OCoLC)1196240420
001433270 830_0 $$aAdvances in computer vision and pattern recognition.$$x2191-6586
001433270 852__ $$bebk
001433270 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-61943-5$$zOnline Access$$91397441.1
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001433270 980__ $$aBIB
001433270 980__ $$aEBOOK
001433270 982__ $$aEbook
001433270 983__ $$aOnline
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