Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Hardcover]

Daphne Koller (Author), Nir Friedman (Author)

Book Description

ISBN-10: 0262013193 | ISBN-13: 978-0262013192 | Publication Date: July 31, 2009 | Edition: 1

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

Editorial Reviews

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

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.

Product Details

• Reading level: Ages 18 and up
  
• Hardcover: 1280 pages
• Publisher: The MIT Press; 1 edition (July 31, 2009)
• Language: English
• ISBN-10: 0262013193
• ISBN-13: 978-0262013192
• Product Dimensions: 9.1 x 8.3 x 2 inches
• Shipping Weight: 4.6 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #3,274 in Books (See Top 100 in Books)
  • #1 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning
  • #7 in Books > Computers & Technology > Programming > Algorithms
  • #51 in Books > Professional & Technical > Professional Science > Mathematics

Customer Reviews

Most Helpful Customer Reviews

40 of 49 people found the following review helpful:

5.0 out of 5 stars Brilliant Tome on Graphical Representation, Reasoning and Machine Learning, March 24, 2010

By 

Dr. Kasumu Salawu (Atlanta, Georgia; USA) - See all my reviews

This review is from: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) (Hardcover)

Stanford professor, Daphne Koller, and her co-author, Professor Nir Friedman, employed graphical models to motivate thoroughgoing explorations of representation, inference and learning in both Bayesian networks and Markov networks. They do their own bidding at the book's web page, [...], by giving readers a panoramic view of the book in an introductory chapter and a Table of Contents. On the same page, there is a link to an extensive Errata file which lists all the known errors and corrections made in subsequent printings of the book - all the corrections had been incorporated into the copy I have. The authors painstakingly provided necessary background materials from both probability theory and graph theory in the second chapter. Furthermore, in an Appendix, more tutorials are offered on information theory, algorithms and combinatorial optimization. This book is an authoritative extension of Professor Judea Pearl's seminal work on developing the Bayesian Networks framework for causal reasoning and decision making under uncertainty. Before this book was published, I sent an e-mail to Professor Koller requesting some clarification of her paper on object-oriented Bayesian networks; she was most generous in writing an elaborate reply with deliberate speed.

8 of 11 people found the following review helpful:

5.0 out of 5 stars Free online Stanford course by one of this text's authors!, November 21, 2011

By 

Rafael Espericueta (Bakersfield, CA USA) - See all my reviews
(REAL NAME)   

Amazon Verified Purchase

This review is from: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) (Hardcover)

Interestingly, one of the author's of this text is teaching a free online course on Probabilistic Graphical Models, starting in January 2012. I just signed up!

Google it and you'll find it...

22 of 32 people found the following review helpful:

4.0 out of 5 stars A comprehensive and tutorial introduction to the subject, October 26, 2009

By 

spikedlatte (Baltimore, MD) - See all my reviews

Amazon Verified Purchase

This review is from: Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) (Hardcover)

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.