- Series: Adaptive Computation and Machine Learning series
- Hardcover: 1270 pages
- Publisher: The MIT Press; 1 edition (July 31, 2009)
- Language: English
- ISBN-10: 0262013193
- ISBN-13: 978-0262013192
- Product Dimensions: 8 x 1.7 x 9 inches
- Shipping Weight: 4.8 pounds (View shipping rates and policies)
- Average Customer Review: 44 customer reviews
Amazon Best Sellers Rank:
#66,983 in Books (See Top 100 in Books)
- #10 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Computer Vision & Pattern Recognition
- #12 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing
- #16 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory
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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) 1st Edition
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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.
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Book Review of "Probabilistic graphical models" by Koller and Friedman
Artificial Intelligence Journal, 174(2): 145--146, 2010.