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Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series) [Print Replica] [Kindle Edition]

Daphne Koller , Nir Friedman
4.5 out of 5 stars  See all reviews (2 customer reviews)

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  • Print Replica:
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  • Print ISBN-10: 0262013193
  • Print ISBN-13: 978-0262013192
  • Edition: 1
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Book Description

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.

Product Description


"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

  • Format: Kindle Edition
  • File Size: 169582 KB
  • Print Length: 1280 pages
  • Publisher: The MIT Press; 1 edition (31 July 2009)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B007CNRD62
  • Text-to-Speech: Enabled
  • Word Wise: Not Enabled
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #420,773 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most Helpful Customer Reviews
4 of 4 people found the following review helpful
5.0 out of 5 stars A masterpiece 15 Aug. 2012
This is the most complete book so far I've read on the topic with excellent description of the algorithms and models and a thorough presentation of proof. It's orientated toward Probabilistic Graphical Models only (as the title says), which means you cannot expect to read too much about other aspect of the classical Bayesian theory (I advise Christian Robert's book: A Bayesian choice, for that purpose).

The book is graduate level and needs the reader to have solid skills in linear algebra, probabilities and statistics to make the most of it.

What I really like about this book is the fact it only focus on one topic: Graphical models and do not try to cover Machine Learning in general. The consequence of that is its thorough treatment of many aspects of graphical models which is rare in the literature. That's why I highly and warmly recommend this book.
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0 of 1 people found the following review helpful
4.0 out of 5 stars Bayes network, Graph models 27 Nov. 2011
By Payam
This is a good reference book on probabilistic graph algorithms. The book is well written and covers computation and machine learning subjects to medium depth level. Good value for the money spent
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Most Helpful Customer Reviews on (beta) 4.0 out of 5 stars  27 reviews
23 of 24 people found the following review helpful
5.0 out of 5 stars Probably the best book for the topic, hard to read with Kindle app on Ipad 23 Sept. 2012
By S. Arikan - Published on
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.
74 of 89 people found the following review helpful
5.0 out of 5 stars Brilliant Tome on Graphical Representation, Reasoning and Machine Learning 24 Mar. 2010
By Dr. Kasumu Salawu - Published on
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.
9 of 10 people found the following review helpful
4.0 out of 5 stars used for Coursera PGM course 1 Feb. 2013
By catwings - Published on
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.
30 of 41 people found the following review helpful
4.0 out of 5 stars A comprehensive and tutorial introduction to the subject 26 Oct. 2009
By spikedlatte - Published on
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.
3 of 3 people found the following review helpful
5.0 out of 5 stars The best way to learn about graphical models 2 April 2014
By Ian Goodfellow - Published on
Format:Kindle Edition|Verified 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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