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Probabilistic Graphical Models Principles by Sucar Luis (30 results)
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Probabilistic Graphical Models : Principles and Applications
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Probabilistic Graphical Models : Principles and Applications
Published by Springer International Publishing, 2021
ISBN 10: 3030619451 ISBN 13: 9783030619459
Language: English
£ 47.99
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Add to basketTaschenbuch. Condition: 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.
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Probabilistic Graphical Models : Principles and Applications
Published by Springer London, Springer London, 2016
ISBN 10: 1447170547 ISBN 13: 9781447170549
Language: English
£ 49.69
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Add to basketTaschenbuch. Condition: 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.
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Probabilistic Graphical Models : Principles and Applications
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Probabilistic Graphical Models : Principles and Applications
Published by Springer International Publishing, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Language: English
£ 62.39
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Add to basketBuch. Condition: 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.
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Probabilistic Graphical Models: Principles and Applications
Published by Springer Nature Switzerland AG, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Language: English
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Probabilistic Graphical Models: Principles and Applications
Published by Springer-Nature New York Inc, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Language: English
Hardcover. Condition: Brand New. 2nd edition. 355 pages. 9.50x6.25x1.00 inches. In Stock.
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Probabilistic Graphical Models: Principles and Applications
Published by Springer Nature Switzerland AG, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Language: English
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Probabilistic Graphical Models: Principles and Applications
Published by Springer-Nature New York Inc, 2020
ISBN 10: 3030619427 ISBN 13: 9783030619428
Language: English
Hardcover. Condition: Brand New. 2nd edition. 355 pages. 9.50x6.25x1.00 inches. In Stock. This item is printed on demand.
