Da: HPB-Red, Dallas, TX, U.S.A.
EUR 36,60
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Aggiungi al carrellohardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 47,69
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Da: Books Puddle, New York, NY, U.S.A.
EUR 49,22
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Da: Basi6 International, Irving, TX, U.S.A.
EUR 53,50
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Da: Majestic Books, Hounslow, Regno Unito
EUR 48,29
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 56,40
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 49,46
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Da: California Books, Miami, FL, U.S.A.
EUR 62,24
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 61,26
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 51,59
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 69,13
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 70,97
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Da: Chiron Media, Wallingford, Regno Unito
EUR 56,15
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 61,88
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,21
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 74,60
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 62,35
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 64,66
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Da: California Books, Miami, FL, U.S.A.
EUR 85,13
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Editore: Springer International Publishing, 2021
ISBN 10: 3030619451 ISBN 13: 9783030619459
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.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 PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludesa new chapter introducing deep neural networks and their relation with probabilistic graphical modelsCovers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes,Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis 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 NationalInstitute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.He received the National Science Prize en 2016.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 74,55
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Aggiungi al carrelloCondizione: New. In English.
Editore: Springer London, Springer London, 2016
ISBN 10: 1447170547 ISBN 13: 9781447170549
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 55,39
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: 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.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 72,41
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Da: Revaluation Books, Exeter, Regno Unito
EUR 82,42
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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 277 pages. 9.25x6.10x0.63 inches. In Stock.
Da: Russell Books, Victoria, BC, Canada
EUR 86,49
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Aggiungi al carrelloHardcover. Condizione: New. 2nd ed. 2021. Special order direct from the distributor.
Editore: Springer International Publishing, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 69,54
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.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 PGMsExplores the fundamental aspects of representation, inference and learning for each techniqueExamines new material on partially observable Markov decision processes, and graphical modelsIncludesa new chapter introducing deep neural networks and their relation with probabilistic graphical modelsCovers multidimensional Bayesian classifiers, relational graphical models, and causal modelsProvides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projectsDescribes classifiers such as Gaussian Naive Bayes,Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian NetworksOutlines the practical application of the different techniquesSuggests possible course outlines for instructorsThis 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 NationalInstitute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.He received the National Science Prize en 2016.
Editore: Springer-Nature New York Inc, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 105,32
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Aggiungi al carrelloHardcover. Condizione: Brand New. 2nd edition. 355 pages. 9.50x6.25x1.00 inches. In Stock.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 107,15
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 97,42
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 128,37
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