Probabilistic Graphical Models: Principles and Applications - Couverture souple

Sucar, Luis Enrique Enrique

9781447170549: Probabilistic Graphical Models: Principles and Applications

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ISBN 10 : 1447170547 ISBN 13 : 9781447170549

Editeur : Springer, 2016

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This 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. Features: presents a unified framework encompassing all of the main classes of PGMs; 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.

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�diteurSpringer
Date d'�dition2016
ISBN 10 1447170547
ISBN 13 9781447170549
ReliureBroch�
Langueanglais
Nombre de pages280
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9781447166986: Probabilistic Graphical Models: Principles and Applications

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ISBN 10 : 1447166981 ISBN 13 : 9781447166986
Editeur : Springer London Ltd, 2015
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Probabilistic Graphical Models

Luis Enrique Sucar

Edit� par Springer London, 2016

ISBN 10 : 1447170547 ISBN 13 : 9781447170549

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes exercises, suggestions for research projects, and example applications throughout the bookPresents the main classes of PGMs under a single, unified frameworkCovers both the fundamental aspects and some of the latest developments in. N� de r�f. du vendeur 458490217

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Probabilistic Graphical Models

Luis Enrique Sucar

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ISBN 10 : 1447170547 ISBN 13 : 9781447170549

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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. Features: presents a unified framework encompassing all of the main classes of PGMs; 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. 280 pp. Englisch. N� de r�f. du vendeur 9781447170549

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Probabilistic Graphical Models

Luis Enrique Sucar

Edit� par Springer London, Springer London Okt 2016, 2016

ISBN 10 : 1447170547 ISBN 13 : 9781447170549

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Taschenbuch. Etat : Neu. Neuware -This 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. Features: presents a unified framework encompassing all of the main classes of PGMs; 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 280 pp. Englisch. N� de r�f. du vendeur 9781447170549

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Probabilistic Graphical Models : Principles and Applications

Luis Enrique Sucar

Edit� par Springer London, Springer London, 2016

ISBN 10 : 1447170547 ISBN 13 : 9781447170549

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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. Features: presents a unified framework encompassing all of the main classes of PGMs; 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. N� de r�f. du vendeur 9781447170549

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