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

by Daphne Koller (Author), Nir Friedman (Author)

36 customer reviews

ISBN-13: 978-0262013192

ISBN-10: 0262013193

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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.

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 (36 customer reviews)
Amazon Best Sellers Rank: #87,443 in Books (See Top 100 in Books)
- #12 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Machine Theory
- #13 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing
- #14 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Computer Vision & Pattern Recognition

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Top Customer Reviews

Probably the best book for the topic, hard to read with Kindle app on Ipad

ByArikanson 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.

0Comment 31 people found this helpful. Was this review helpful to you?YesNo

Brilliant Tome on Graphical Representation, Reasoning and Machine Learning

ByDr. Kasumu Salawuon 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.

44 comments 78 people found this helpful. Was this review helpful to you?YesNo

used for Coursera PGM course

Bycatwingson February 1, 2013

Format: HardcoverVerified 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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The best way to learn about graphical models

ByIan Goodfellowon April 2, 2014

Format: Kindle EditionVerified Purchase

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 need to learn both variational inference and calculus of variations at the same time.

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A useful, comprehensive reference book; awkward to read

ByZZon April 27, 2014

Format: Kindle EditionVerified 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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Excellent self study book for probabilistic graphical models

ByKrystaon September 4, 2016

Format: HardcoverVerified 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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A Superb Book

ByKevin S. Grayon September 1, 2013

Format: HardcoverVerified 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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Most Recent Customer Reviews

Amazing book

Fantastic diligence by the authors and making the subject clear.

Published 1 day ago by Daniel

Five Stars

great book.

Published 3 months ago by Jay

Detailed and complex

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

Published 6 months ago by AB

Math 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.

Published 9 months ago by Xerophyte

Not 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

Perfect 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 10 months ago by mehdi

Terrible Hard Cover Format

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

Published 1 year ago by timers

A 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 1 year ago by Amazon Customer

A 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 1 year ago by Lee, Doo Youl

Interesting, 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.

Published on January 15, 2014 by Klim

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

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