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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) 1st Edition

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Daphne Koller (Author)
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Daphne Koller (Author),
Nir Friedman (Author)
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Nir Friedman (Author)
4.0 out of 5 stars 35 customer reviews
ISBN-13: 978-0262013192
ISBN-10: 0262013193
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  • Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
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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: 8 x 1.7 x 9 inches
  • Shipping Weight: 4.7 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (35 customer reviews)
  • Amazon Best Sellers Rank: #67,934 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starsProbably the best book for the topic, hard to read with Kindle app on Ipad
By Data_Guy_from_London 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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5.0 out of 5 starsBrilliant Tome on Graphical Representation, Reasoning and Machine Learning
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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3.0 out of 5 starsA useful, comprehensive reference book; awkward to read
By ZZ on April 27, 2014
Format: Kindle Edition Verified Purchase
This popular book makes a noble attempt at unifying the many different types of probabilistic models used in artificial intelligence. It seems like a good reference manual for people who are already familiar with the fundamental concepts of commonly used probabilistic graphical models. However, it contains a lot of rambling and jumping between concepts that will quickly confuse a reader who is not already familiar with the subject. While the book appears to be systematic in introducing the subject with mathematical rigor (definitions and theorems), it actually skips a lot of fundamental concepts and leaves a lot of important proofs as exercises. I would recommend that a beginner in the subject start with another book like that by Jordan and Bishop, while keeping this book around as a reference manual or bank of practice problems for further study. The Coursera class on this subject is much easier to follow than this book is.
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5.0 out of 5 starsA Superb Book
By Kevin S. Gray on September 1, 2013
Format: Hardcover Verified Purchase
If you want a very close look under the hood of Bayesian Networks, I can highly recommend Probabilistic Graphical Models. It's extremely comprehensive (1,200+ pages), well structured and clearly written. Theory, computation and application - including how to think about causation - are all covered in depth. Not light reading and not suited for those with limited stats background, but all in all one of the best textbooks on analytics topics I've ever read. Very impressive.
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5.0 out of 5 starsExcellent self study book for probabilistic graphical models
By Krysta on September 4, 2016
Format: Hardcover Verified Purchase
This is a great book on the topic, regardless of whether you are new to probabilistic graphical models or have some familiarity with them but would like a deeper exploration of theory and/or implementation. I have read a number of books and papers on this topic (including Barber's and Bishop's) and I much prefer this one. Dr. Koller's style of writing is to start with simple theory and examples and walk the reader up to the full theory, while adding reminders of relevant topics covered elsewhere. She accomplishes this without condescending to or belittling the reader, or being overly verbose; each of the 1200 pages is concise and well edited. There is an OpenClassroom course that accompanies the book (CS 228), which I highly recommend viewing, as it contains that same style of teaching but in a different format and often with a somewhat different approach.
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5.0 out of 5 starsI am reading through it
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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Most Recent Customer Reviews

5.0 out of 5 starsFive Stars

great book.

Published 2 months ago by Jay
5.0 out of 5 starsDetailed and complex

Very detailed and in depth material. Not recommended as an introduction to probabilistic models.

Published 6 months ago by AB
2.0 out of 5 starsMath but without intuition

The book is not easy to read. The authors did a very poor job of explaining the intuition behind math. I am not sure how many people buy this book but never read it. Read more

Published 8 months ago by Xerophyte
2.0 out of 5 starsNot at all readable or intuitive. Follows the Lemma ...

Not at all readable or intuitive. Follows the Lemma - Proof motif of most abstract math texts. Don't buy this unless you're looking for an extremely theoretical treatment of the... Read more

Published 9 months ago by Amazon Customer
5.0 out of 5 starsPerfect book, very detailed and very readable with lots ...

Perfect book, very detailed and very readable with lots of real world examples ... Of course reading it takes time ... not for faint heart ...

Published 9 months ago by mehdi
1.0 out of 5 starsTerrible Hard Cover Format

The hard cover book is completely awful. The dimensions are incredibly awkward to hold, carry and read. Read more

Published 15 months ago by timers
3.0 out of 5 starsA very good book completely spoilt by the Kindle Format

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

Published 20 months ago by Amazon Customer
5.0 out of 5 starsA good thing is that you can take free video lecture ...

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 more

Published 22 months ago by Lee, Doo Youl
5.0 out of 5 starsThe best way to learn about graphical models

This was the book that really got me into AI research. Clearly written and detailed. I especially like that variational inference is taught using discrete variables so you don't... Read more

Published on April 2, 2014 by Ian Goodfellow
4.0 out of 5 starsInteresting, but tough

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 more

Published on January 15, 2014 by Klim

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