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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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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press (edition 1), 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Hardcover. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press (edition 1), 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Hardcover. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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hardcover. Condition: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
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hardcover. Condition: Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning, 23)
Language: English
Published by Morgan & Claypool Publishers, 2013
ISBN 10: 162705197X ISBN 13: 9781627051972
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Condition: New. x, 315 pages, illustrations some color.
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Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Condition: New. x, 315 pages, illustrations some color First edition Includes bibliographical references and index.
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Machine Learning and Probabilistic Graphical Models for Decision Support Systems
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Machine Learning and Probabilistic Graphical Models for Decision Support Systems
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Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Language: English
Published by Taylor & Francis Ltd, 2024
ISBN 10: 1032039507 ISBN 13: 9781032039503
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Machine Learning and Probabilistic Graphical Models for Decision Support Systems
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Probabilistic Graphical Models â" Principles and Techniques (Adaptive Computation and Machine Learning series)
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Add to basketPaperback. Condition: Brand New. 330 pages. 10.00x7.00x10.00 inches. In Stock.
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Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning, 23, Band 23)
Language: English
Published by Morgan & Claypool Publishers, 2013
ISBN 10: 162705197X ISBN 13: 9781627051972
paperback. Condition: Gut. 192 Seiten; 9781627051972.3 Gewicht in Gramm: 500.
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Hardcover. Condition: Sehr gut. Gebraucht - Sehr gut Sg - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
