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Hardcover. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.
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hardcover. Condition: 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!
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Condition: As New. Unread book in perfect condition.
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Condition: New. pp. 355.
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Condition: New. 2nd ed. 2021 edition NO-PA16APR2015-KAP.
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Condition: New. pp. 355.
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RoboCup 2012: Robot Soccer World Cup XVI: Robot Soccer World Cup XVI (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence)
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Robocup 2012 : Robot Soccer World Cup XVI
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Condition: New. pp. 355.
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Probabilistic Graphical Models : Princip
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Robocup 2012 : Robot Soccer World Cup XVI
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Condition: New. pp. 408.
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Probabilistic Graphical Models: Principles and Applications (Advances in Computer Vision and Pattern Recognition)
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Paperback. Condition: Brand New. reprint edition. 277 pages. 9.25x6.10x0.63 inches. In Stock.
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Paperback. Condition: Brand New. 2013 edition. 404 pages. 9.37x6.14x0.94 inches. In Stock.
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Probabilistic Graphical Models
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2022
ISBN 10: 3030619451 ISBN 13: 9783030619459
Condition: New.
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Taschenbuch. Condition: Neu. Probabilistic Graphical Models | Principles and Applications | Luis Enrique Sucar | Taschenbuch | xxiv | Englisch | 2016 | Springer | EAN 9781447170549 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Probabilistic Graphical Models : Principles and Applications
Language: English
Published by Springer International Publishing, 2021
ISBN 10: 3030619451 ISBN 13: 9783030619459
Taschenbuch. 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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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book includes the thoroughly refereed post-conference proceedings of the 16th Annual RoboCup International Symposium, held in Mexico City, Mexico, in June 2012. The 24 revised papers presented together with nine champion team papers and one best paper award were carefully reviewed and selected from 64 submissions. The papers present current research and educational activities within the fields of Robotics and Artificial Intelligence with a special focus to robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment, and applications.
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Probabilistic Graphical Models : Principles and Applications
Book 48 of 86: Advances in Computer Vision and Pattern RecognitionLanguage: English
Published by Springer London, Springer London, 2016
ISBN 10: 1447170547 ISBN 13: 9781447170549
Taschenbuch. 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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Taschenbuch. Condition: Neu. Probabilistic Graphical Models | Principles and Applications | Luis Enrique Sucar | Taschenbuch | xxviii | Englisch | 2021 | Springer | EAN 9783030619459 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
