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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) Hardcover – July 31, 2009

ISBN-13: 978-0262013192 ISBN-10: 0262013193 Edition: 1st

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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 1280 pages
  • Publisher: The MIT Press; 1 edition (July 31, 2009)
  • Language: English
  • ISBN-10: 0262013193
  • ISBN-13: 978-0262013192
  • Product Dimensions: 9.2 x 8.2 x 2 inches
  • Shipping Weight: 4.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (26 customer reviews)
  • Amazon Best Sellers Rank: #49,637 in Books (See Top 100 in Books)

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

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.

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Customer Reviews

I have not read most of the books but have read enough to write positive things about it.
S. L.
Well organized, clearly explained, most importantly, with human readable examples ,not only complex math formulas, which I hate for most of books.
Omii
If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models.
Kevin S. Gray

Most Helpful Customer Reviews

71 of 85 people found the following review helpful By Dr. Kasumu Salawu on March 24, 2010
Format: 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.
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17 of 18 people found the following review helpful By S. Arikan on September 23, 2012
Format: Hardcover
If you're trying to learn probabilistic graphical models on your own, this is the best book you can buy.
The introduction to fundamental probabilistic concepts is better than most probability books out there and the rest of the book has the same quality and in-depth approach. References, discussions and examples are all chosen so that you can take this book as the centre of your learning and make a jump to more detailed treatment of any topic using other resources.

Another huge plus is Professor Daphne Koller's online course material. Her course for probabilistic models is available online, and watching the videos alongside the book really helps sometimes.

If you have a strong mathematical background, you may find the book a little bit too pedagogic for your taste, but if you're looking for a single resource to learn the topic on your own, then this book is what you need.

The only problem with it is that it is a big book to carry around, and if you buy the Kindle edition for the iPad, you'll have to zoom into pages to read comfortably(or maybe I have bad eye sight), and Kindle app on iPad does not keep the zoom level across pages. So my experience is, zoom, pan, read, change page, zoom, pan, go back to previous page to see something, zoom, pan... You get the idea. I'd gladly pay more for a pdf version which I could read with other software on the iPad. Even though my reading experience has been a bit unpleasant due to Kindle app, the book deserves five stars, since it is the content that matters.
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6 of 7 people found the following review helpful By catwings on February 1, 2013
Format: Hardcover Verified Purchase
I bought this book to use for the Coursera course on PGM taught by the author. It was essential to being able to follow the course. I would not say that it is an easy book to pick up and learn from. It was a good reference to use to get more details on the topics covered in the lectures.
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6 of 7 people found the following review helpful By Patrick Linnen on November 21, 2012
Format: Hardcover Verified Purchase
with an eye to taking the course. Very informative. Although the phrase "in context" covers a multitude of sins. I'd prefer the distinction between the the distribution of an intersection of random variables (where comma's are used as a short-hand) and joint distributions a bit clearer.

Aside, I managed to find an error not listed on the errata web page for the book. The equation for MAP queries on page 26 has it as the maximal assignment of a JOINT distribution, while on the next page it is the maximal assignment of a CONDITIONAL distribution (I believe this is the correct one). This was a little confusing until I read page 26 a bit closer.

Before you ask, yes I do read Math textbooks for pleasure.
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30 of 41 people found the following review helpful By spikedlatte on October 26, 2009
Format: Hardcover Verified Purchase
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.
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36 of 51 people found the following review helpful By Samantha Atkins on April 15, 2012
Format: Kindle Edition Verified Purchase
It seems to want to be viewed in some PDF ish format on the Kindle Reader on my iPad. This means I can't set the font to something readable. I can pinch zoom each page but this makes for a not happy reading experience. Amazon should warn people when a book is in a different format or views differently. If they did so then I certainly missed it.
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59 of 87 people found the following review helpful By Jamesqf on March 11, 2012
Format: Hardcover Verified Purchase
I purchased this book as a text for the Stanford online course in PGM, which as of this writing is at least six weeks late in starting. While waiting for the course, I've tried to struggle through the first chapters on my own, with zero success. After wading through about the first third of the book, and skimming the rest, I must say that if I had to implement a program using PGM to solve some problem, I wouldn't have the foggiest idea how to even begin.

This book may well be a good reference for someone who already has a deep background in machine learning & artificial intelligence, but it emphatically is not of any use to the novice in the field(1). It contains many proofs of theorems, numerous long-winded "explanations" (most of which I don't understand), some algorithms set out in an obfuscated format(2) that I thought had died out about the time I got my BS, but (as far as I've been able to discover) not one line of actual code, nor any implementation, even of the simple "Hello, World" sort.

(1) For background, I have a couple of decades of programming experience, most of it in numerical modelling and parallel applications.

(2) A LaTeX cheat sheet for the symbols used would be a useful addition to future editions of the text.
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