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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)
The hard cover book is completely awful. The dimensions are incredibly awkward to hold, carry and read. Read morePublished 4 months ago by timers
A very good book completely spoilt by the Kindle Format. Here are my gripes:
- Its directly taken from a PDF format and not adopted to work well in the Kindle... Read more
This is the book that I'm looking for. This book is well organized and provides a ton of examples which make understanding easier. Read morePublished 12 months ago by Lee, Doo Youl
This popular book makes a noble attempt at unifying the many different types of probabilistic models used in artificial intelligence. Read morePublished 19 months ago by CJ
I just started reading this book for a course I want to do the coming semester. It seems like a very interesting book, exploring in depth probabilistic graphical models. Read morePublished 22 months ago by Klim
Super comprehensive but super dry. Not an interesting read on its own. More like a reference manual. Informative but boring.Published on November 7, 2013 by Phillip C. Adkins
Fantastic....great text. At just the right level.
(The use of pseudo code really helps the process of understanding the material)
A great review of the book from Kevin Murphy appeared in Artificial Intelligence Journal.
Book Review of "Probabilistic graphical models" by Koller and... Read more
If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models. Read morePublished on September 1, 2013 by Kevin S. Gray