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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Hardcover]

by Daphne Koller, Nir Friedman
4.1 out of 5 stars  See all reviews (25 customer reviews)

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Book Description

July 31, 2009 0262013193 978-0262013192 1

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.


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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) + Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) + Pattern Recognition and Machine Learning (Information Science and Statistics)
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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.

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 (25 customer reviews)
  • Amazon Best Sellers Rank: #47,174 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews
70 of 84 people found the following review helpful
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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14 of 14 people found the following review helpful
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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30 of 41 people found the following review helpful
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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3 of 3 people found the following review helpful
4.0 out of 5 stars Excellent reference December 9, 2012
Format:Hardcover|Verified Purchase
Very complete reference on the subject. Sometimes it sacrifices readability for rigor. I wish it included more references to open source software/libraries so that the user could try out PGM first-hand.
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5 of 6 people found the following review helpful
5.0 out of 5 stars I am reading through it 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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4 of 5 people found the following review helpful
4.0 out of 5 stars used for Coursera PGM course 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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34 of 49 people found the following review helpful
2.0 out of 5 stars bad kindle format 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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1 of 1 people found the following review helpful
5.0 out of 5 stars A Superb Book 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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Most Recent Customer Reviews
5.0 out of 5 stars The 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 21 days ago by Ian Goodfellow
4.0 out of 5 stars 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. Read more
Published 3 months ago by Klim
4.0 out of 5 stars Informative but boring
Super comprehensive but super dry. Not an interesting read on its own. More like a reference manual. Informative but boring.
Published 5 months ago by Phillip C. Adkins
5.0 out of 5 stars great text
Fantastic....great text. At just the right level.
(The use of pseudo code really helps the process of understanding the material)

Cheers
Published 5 months ago by c_eusebi
4.0 out of 5 stars An advanced book - Phd students or researchers would benefit more
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
Published 7 months ago by Safiye
5.0 out of 5 stars Review Probabilistic Graphical Models
The book integrates several ideas into a well defined concept.
It is easy to read, with good examples and exercises.
Published 8 months ago by ittai Artzi
2.0 out of 5 stars There is no real Kindle format available, but an adapted PDF!
I am not reviewing the book itself, which I just bought and started reading. I am reviewing the Kindle version product. Read more
Published 12 months ago by Marcos Silva
4.0 out of 5 stars Very good book on PGMs.
This is a great book for everyone, who wants to understand probabilitstic graphical models in details,
including Bayesian/Markov Networks, inference and learning from... Read more
Published 21 months ago by Daniel Korzekwa
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