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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning) 1st Edition

by Judea Pearl (Author)
4.2 out of 5 stars 11 customer reviews
ISBN-13: 978-1558604797
ISBN-10: 1558604790
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  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Series in Representation and Reasoning)
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Product Details

  • Series: Morgan Kaufmann Series in Representation and Reasoning
  • Paperback: 552 pages
  • Publisher: Morgan Kaufmann; 1 edition (September 15, 1988)
  • Language: English
  • ISBN-10: 1558604790
  • ISBN-13: 978-1558604797
  • Product Dimensions: 6 x 1.3 x 9 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (11 customer reviews)
  • Amazon Best Sellers Rank: #947,994 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starsA seminal work
By Aaron D'Souza on March 8, 2000
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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5.0 out of 5 starsFantastic!!!
By Paul D. Tozour on March 14, 2001
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.
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5.0 out of 5 starsonly 8 reviews so far?
By King Yin Yan on October 2, 2010
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
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4.0 out of 5 starsElegant Discussion On Probabilistic Reasoning And Uncertainty
By Adnan Masood, PhD on December 12, 2005
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.
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5.0 out of 5 starsgives a great overview into the Bayesian thinking and methods
By yan virin on December 31, 2014
Format: Paperback Verified Purchase
Very interesting book, gives a great overview into the Bayesian thinking and methods.
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1.0 out of 5 starsTo reiterate moussie99, Not so much
By Andrew on April 19, 2006
Format: Paperback
I've read the first half of this book twice now (once for a class, once to pass M.S. test). The book is terrible. There are virtually no examples to help you learn how to construct a Bayesian network. I'm serious. Virtually the only example is actually a homework question at the end of a chapter and the question is wrong!!! (there are dependcies in the table given for the joint distribution, but the acyclic graph shows them as independencies).

This book was written in defense of Bayesian Networks as a "Reasonable" graphical model. At the time, perhaps it was needed, but today we accept them as useful and move on unless we are trying to model medical diagnosis. For this reason the book is written with proofs where there should be examples (and perhaps references to an appendix). Please don't prove to me that Bayesian networks are reasonable, show me how they are useful!

To reiterate, you will learn how to create and use Bayesian networks from somewhere else, even if you buy this book.

Oh, and my FAVORITE example is the Prisoner's Paradox. He uses this example to show relationships that should be representable in a graphical model. But the whole point of the paradox is that humans are VERY bad at thinking in this manner. Though Pearl makes general claims as to the similarity between Bayesian Networks and the way humans think (doctors performing medical diagnosis is not a normal human task!), this example shows the opposite. It is called a paradox because it is unintuitive, weakening claims as to the likeness of Bayesian Networks to human thought.
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