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Probabilistic Graphical Models Principles by Koller Daphne (26 results)
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hardcover. Condition: Good.
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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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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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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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Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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Condition: Very good. This book a is a comprehensive, landmark textbook that provides a general framework for constructing and using probabilistic models of complex systems. Its primary focus is on how to represent and reason about uncertainty in complex, real-world domains like computer vision, robotics, and computational biology. The book is structured around the three fundamental cornerstones of the probabilistic graphical model (PGM) framework: Representation: Discusses various models, including Bayesian Networks (directed graphs) and Undirected Markov Networks, as ways to compactly encode joint probability distributions over many variables using conditional independence assumptions. Inference: Details the algorithms and techniques (both exact and approximate, like belief propagation and sampling methods) for answering probabilistic queries, such as finding the probability of an event given some evidence. Learning: Covers methods for automatically constructing the models from data, including estimating model parameters and learning the underlying graph structure. Finally, the book extends the framework to cover advanced topics such as causal reasoning and decision making under uncertainty. It is widely regarded as a definitive reference for students and researchers in artificial intelligence and machine learning.
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Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
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Condition: New.
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Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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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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Probabilistic Graphical Models: Principles And Techniques
Published by Mit Press, 2009
Language: English
Hardcover. Condition: New. ISBN:9780262013192.
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Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
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Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Probabilistic Graphical Models Principles and Techniques
Seller: Buchpark, Maidenhead, Berkshire, United Kingdom
Seller rating 4 out of 5 stars
Condition: Very Good. Condition: Very Good, Pages: 1270, Size: 23.6x20.9x5.
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PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND TECHNIQUES
Published by The MIT Press Bookstore, 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Language: English
Condition: New. pp. 1280.
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PROBABILISTIC GRAPHICAL MODELS: PRINCIPLES AND TECHNIQUES
Published by The MIT Press Bookstore, 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Language: English
Condition: New. pp. 1280.
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International EditionHardcover. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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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.
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Buch. Condition: Neu. Probabilistic Graphical Models | Principles and Techniques | Daphne Koller (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2009 | MIT Press Ltd | EAN 9780262013192 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Probabilistic Graphical Models : Principles and Techniques
Published by MIT Press Ltd Jul 2009, 2009
ISBN 10: 0262013193 ISBN 13: 9780262013192
Language: English
Buch. Condition: Neu. Neuware - 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.
