Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning) |  | Authors: Daphne Koller, Nir Friedman
Publisher: The MIT Press
List Price: $95.00
Buy New: $76.00
as of 11/20/2009 05:21 CST details
You Save: $19.00 (20%)
New (11) Used (6) from $76.00
Seller: Amazon.com
Rating:  2 reviews
Sales Rank: 14610
Media: Hardcover
Pages: 1208
Number Of Items: 1
Shipping Weight (lbs): 4.6
Dimensions (in): 9.1 x 8.3 x 2
ISBN: 0262013193
Dewey Decimal Number: 519.5420285
EAN: 9780262013192
ASIN: 0262013193
Publication Date: August 31, 2009
Shipping: Eligible for FREE Super Saver Shipping
Availability: Usually ships in 24 hours
| |
| Similar Items:
| |
| Editorial Reviews:
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
|
| Customer Reviews:
 A comprehensive and tutorial introduction to the subject October 26, 2009
Delip Rao (Baltimore, MD)
1 out of 3 found this review helpful
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! September 28, 2009
董喆 (Beijing, China)
3 out of 8 found this review helpful
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.
|
|
|
|
