Probabilistic Graphical Models

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.

ISBN 0262013193   Volumes 1 ISBN13 9780262013192 (What's this?)   Weight (grammes) 2111 Publisher MIT Press Ltd   Published in Cambridge, Mass. Imprint MIT Press   Series title Adaptive Computation and Machine Learning Series Format Hardback   Height (mm) 229 Publication date 16 Nov 2009   Width (mm) 203 Library of Congress 2009008615   Spine width (mm) 43 DEWEY 519.5420285   Academic level General DEWEY edition DC22   Interest age From 18 Pages 1280

		List of Figures
		List of Algorithms
		List of Boxes
1		Introduction
2		Foundations
I		Representation
3		The Bayesian Network Representation
4		Undirected Graphical Models
5		Local Probabilistic Models
6		Template-Based Representations
7		Gaussian Network Models
8		The Exponential Family
II		Inference
9		Exact Inference: Variable Elimination
10		Exact Inference: Clique Trees
11		Inference as Optimization
12		Particle-Based Approximate Inference
13		MAP Inference
14		Inference in Hybrid Networks
15		Inference in Temporal Models
III		Learning
16		Learning Graphical Models: Overview
17		Parameter Estimation
18		Structure Learning in Bayesian Networks
19		Partially Observed Data
20		Learning Undirected Models
IV		Actions and Decisions
21		Causality
		More...

"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

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