Probabilistic Graphical Models: Principles and Techniques (Google eBook)

Front Cover
MIT Press, 2009 - Computers - 1231 pages
4 Reviews

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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Review: Probabilistic Graphical Models: Principles and Techniques

User Review  - Chetan - Goodreads

Read this as part of a coursera course given by Daphne Koller. Comprehensive introduction to techniques and methodology used to practically use PGMs. Read full review

Review: Probabilistic Graphical Models: Principles and Techniques

User Review  - James - Goodreads

Extremely thorough, though it tends to describe everything abstractly with the occasional very simple worked example--a math textbook to be sure. Practitioners may find a better book on the subject out there but I got a lot out of the selection of chapters I read. Read full review

Contents

1 Introduction
1
2 Foundations
15
Part I Representation
43
Part II Inference
285
Part III Learning
695
Part IV Actions and Decisions
1007
Appendix A Background Material
1135
Bibliography
1171
Notation Index
1209
Subject Index
1213
Copyright

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About the author (2009)

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.

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