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Adaptive Computation and Machine Learning Ser.: Probabilistic Graphical Models : Principles and Techniques by Nir Friedman and Daphne Koller (2009, Hardcover)

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Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

About this product

Product Identifiers

PublisherMIT Press

ISBN-100262013193

ISBN-139780262013192

eBay Product ID (ePID)73169822

Product Key Features

Number of Pages1270 Pages

Publication NameProbabilistic Graphical Models : Principles and Techniques

LanguageEnglish

Publication Year2009

SubjectProgramming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis

TypeTextbook

AuthorNir Friedman, Daphne Koller

Subject AreaMathematics, Computers

SeriesAdaptive Computation and Machine Learning Ser.

FormatHardcover

Dimensions

Item Height2 in

Item Weight74.1 Oz

Item Length9.2 in

Item Width8.2 in

Additional Product Features

Intended AudienceTrade

LCCN2009-008615

Reviews"This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning. As such, it is likely to become a definitive reference for all those who work in this area. Detailed worked examples and case studies also make the book accessible to students." -Kevin Murphy, Department of Computer Science, University of British Columbia

Dewey Edition22

IllustratedYes

Dewey Decimal519.5/420285

SynopsisA general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions., 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.

LC Classification NumberQA279.5.K65 2010