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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning) Paperback – 1 Sep 1988

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Product details

  • Paperback: 574 pages
  • Publisher: Morgan Kaufmann (1 Sept. 1988)
  • Language: English
  • ISBN-10: 1558604790
  • ISBN-13: 978-1558604797
  • Product Dimensions: 15.2 x 3.3 x 22.9 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 548,620 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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About the Author

By Judea Pearl

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Top Customer Reviews

Format: Paperback Verified Purchase
This book is really good to start reading about uncertainty modelling. It was part of the recommended readings in my course and it is the best one.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 4.2 out of 5 stars 11 reviews
10 of 10 people found the following review helpful
5.0 out of 5 stars only 8 reviews so far? 3 Oct. 2010
By King Yin Yan - Published on Amazon.com
Format: Paperback Verified Purchase
Of course, this book is a classic, and the low number of reviews is only because it was published in the 80s.

This book has revolutionized the field of AI, and made Bayesian networks ubiquitous in computer science today (though, BNs were first proposed in 1970 by Suppes or perhaps even earlier).

[ Interestingly, Suppes used BNs to deal with causality. ]

I think part of this book's originality is the use of a mathematical theory (ie, probability theory) into AI. A similar and earlier revolutionary step was taken by John McCarthy in his use of formal logic in AI.

Chapter 5 is actually about what I'd call probabilistic abduction, but the naming of the chapter is a bit misleading.

There are now newer and perhaps better texts on BNs, eg: "Learning BNs" by Neapolitan, the tome by Koller and Friedman (MIT Press), and Darwiche
30 of 34 people found the following review helpful
5.0 out of 5 stars Fantastic!!! 14 Mar. 2001
By Paul D. Tozour - Published on Amazon.com
Format: Paperback Verified Purchase
This book is an absolutely essential book for AI programming. I've found no better book for explaining the recent advances in probability theory and its relevance to real-life, practical artificial intelligence development. It's written in a very down-to-earth and highly entertaining style with plenty of examples.
I've been looking for a good introduction to Bayes nets for a long time, and this one is by far the best and most comprehensive.
Probability is increasingly becoming one of the major foundations of effective artificial intelligence, and I strongly recommend this book to anyone with an interest in AI or probability theory.
0 of 2 people found the following review helpful
5.0 out of 5 stars gives a great overview into the Bayesian thinking and methods 31 Dec. 2014
By yan virin - Published on Amazon.com
Format: Paperback Verified Purchase
Very interesting book, gives a great overview into the Bayesian thinking and methods.
10 of 11 people found the following review helpful
4.0 out of 5 stars Elegant Discussion On Probabilistic Reasoning And Uncertainty 13 Dec. 2005
By Adnan Masood, PhD - Published on Amazon.com
Format: Paperback
Pearl's "Probabilistic Reasoning in Intelligent Systems" is elegantly done seminal work on uncertainty, probabilistic reasoning and all things related inference. As the author says, "This book is a culmination of an investigation into the applicability of probabilistic methods to task requiring automated reasoning under uncertainty", it covers topics on all level i.e. basic ideas, technical and substantive discussions and advanced research. However, my impression of book's target audiences is researchers and readers with a advance understanding of these topics.

"Probabilistic Reasoning in Intelligent Systems" provides very comprehensive and detailed discussion on topics like why uncertainty is important, probabilistic reasoning for query answering system, Markov and Bayesian networks etc; It goes beyond the text and into philosophical discussion as well, for instance it talks about what Bayesian rule's mathematical representation actually mean. The topic "Learning structures from data" is a good discussion of belief networks. As an advance text book, it's equipped with theorem proofs, exercises but not very many examples which disappoints. The book covers default logic very well; topics like semantics for default reasoning, casualty modularity and tree structures, evidential reasoning in taxonomic hierarchies, decision analysis, and autonomous propagation as a computational paradigm are some of the well discussed ones. I particularly enjoyed the Bayesian vs. Dempster-Shafer formulism, probabilistic treatment of the Yale shooting problem and dialogue between logicist and probablist, the concluding discussion.

I'd recommend this book as a secondary resource for advance researchers in the field of probability and uncertainty.
38 of 42 people found the following review helpful
5.0 out of 5 stars A seminal work 9 Mar. 2000
By Aaron D'Souza - Published on Amazon.com
Format: Paperback
One of the best references on probability theory and uncertain reasoning, this book is one of my most prized. It's lucid enough to be an excellent textbook for the novice, and thorough enough to be a valuable reference for the experienced. It's a book that will always remind me (lest I forget) of the importance of probabilistic reasoning in AI.
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