Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) - Hardcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
About the Author
Daphne Koller is Professor in the Department of Computer Science at Stanford University.
Nir Friedman is Professor in the Department of Computer Science and Engineering at Hebrew University.
"About this title" may belong to another edition of this title.
Search results for Probabilistic Graphical Models: Principles and Techniques...
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Seller: Your Online Bookstore, Houston, TX, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Good. Seller Inventory # 0262013193-3-35417405
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Published by
The MIT Press, 2009
ISBN 10: 0262013193
ISBN 13: 9780262013192
Used
Hardcover
First Edition
Seller: Textbooks_Source, Columbia, MO, U.S.A.
Seller rating 5 out of 5 stars
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). Seller Inventory # 000967006U
Stock Image
Probabilistic Graphical Models
Seller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Seller Inventory # 00093811185
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Seller: HPB-Red, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
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! Seller Inventory # S_447976625
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Seller: Bellwetherbooks, McKeesport, PA, U.S.A.
Seller rating 5 out of 5 stars
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. Seller Inventory # MIT-HCc-VG-0262013193
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Seller: Treasure Island, Waltham, MA, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Fine. in great condition. Seller Inventory # mon0000001132
Seller Image
Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
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. Seller Inventory # 6241447-5
Seller Image
Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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. Seller Inventory # 6241447-5
Buy Used
US$ 101.32
Shipping:
US$ 19.73
From United Kingdom to U.S.A.
Quantity: 5 available
Seller Image
Probabilistic Graphical Models : Principles and Techniques
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 6241447
Stock Image
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Published by
The MIT Press, 2009
ISBN 10: 0262013193
ISBN 13: 9780262013192
Used
Hardcover
First Edition
Seller: ReviBlio, Barcelona, B, Spain
Seller rating 4 out of 5 stars
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. Seller Inventory # ABE-1760092237570