Product Description

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.

Adaptive Computation and Machine Learning series

Product Details

• Amazon Sales Rank: #5468 in Books
• Published on: 2009-08-31
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 1208 pages

Review
"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

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.

Customer Reviews

A comprehensive and tutorial introduction to the subject
I have read this book in bits and pieces and find it extremely useful. Finally, we got a book that can be used in classroom settings. There are some typos (hence four stars) that will hopefully get fixed in the future editions. The book also has a lot of new insights to offer that can only be gleaned from the vast existing literature on the topic with excruciating labor. Agreed that this book is pricey but for what it has to offer, I think it was money well spent.

Milestone work!
Gives you systematic view of the subject.
Every chapter is with clear explaination, up-to-date expamples and full algorithm implemention by pseudocodes.
A must have for computer scientist who want to enter this field.