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Descripción Taschenbuch. Condición: 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. Nº de ref. del artículo: 9781447170549
Descripción Taschenbuch. Condición: 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. Nº de ref. del artículo: 9781447170549
Descripción Condición: New. Book is in NEW condition. 1.12. Nº de ref. del artículo: 1447170547-2-1
Descripción Paperback. Condición: Brand New. reprint edition. 277 pages. 9.25x6.10x0.63 inches. In Stock. Nº de ref. del artículo: zk1447170547
Descripción Condición: 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. Nº de ref. del artículo: 458490217