Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Paperback)

~ (Author) "Reasoning about any realistic domain always requires that some simplifications be made..." (more)
Key Phrases: triangularity axiom, complete probabilistic model, causal basin, Belief Updating By Network Propagation, Dry Bonus, North Holland (more...)

Editorial Reviews

Product Description

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.

Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

From the Back Cover

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.

Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

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Product Details

• Paperback: 552 pages
• Publisher: Morgan Kaufmann; 1 edition (September 15, 1988)
• Language: English
• ISBN-10: 1558604790
• ISBN-13: 978-1558604797
• Product Dimensions: 8.9 x 6 x 1.2 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)
• Average Customer Review: 3.9 out of 5 stars  See all reviews (8 customer reviews)
• Amazon.com Sales Rank: #291,481 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

34 of 38 people found the following review helpful:

5.0 out of 5 stars A seminal work, March 8, 2000

By	Aaron D'Souza (Los Angeles, CA) - See all my reviews

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.

25 of 29 people found the following review helpful:

5.0 out of 5 stars Fantastic!!!, March 14, 2001

By	Paul D. Tozour (Austin, TX USA) - See all my reviews

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.

14 of 15 people found the following review helpful:

5.0 out of 5 stars Outstanding introduction to the field, June 28, 2007

By	G. Snider (Palo Alto, CA USA) - See all my reviews (REAL NAME)

Recently I needed to learn the principles of Bayesian networks quickly, so I bought three books: this one by Pearl, "Pattern Recognition and Machine Learning" by Bishop, and "Bayesian Artificial Intelligence" by Korb and Nicholson. Each has a very different audience and different set of strengths.

The Bishop book would probably be a great text for a serious student with a year to spend learning the theory of machine learning. But I found it a bit too concise, with a bias towards an "algebraic" description rather than a "geometric" one (my preference). I wound up spending a lot of time trying to translate equations into mental pictures in order to grasp the concepts. Too much work, so I dropped this after a couple of days.

Next I tackled the Korb and Nicholson book. This one's aimed at the application engineer who wants to get a network up and running quickly, and is not too concerned about how it works. I've been in that position many times in my career, and have always welcomed books like this for giving me a quick start into a new field. But this time I needed to really understand how Bayesian networks worked, and for this the Korb and Nicholson book is not great. In the first 9 pages of chapter 3 they try to explain the belief propagation algorithm, but their hearts just weren't it in--I found their explanation to be unintelligible. (I suspect most readers just skim this to get to the applications.) So after several days of struggling and getting nowhere, I tossed this aside as well.

The Pearl book was the only left; I put it off to last since I was initially somewhat intimidated by it. After all, this is one of the books that kicked off the "Bayesian revolution," so I was fearing a foundational math book consisting of one dry theorem after another. Not so! Although you have to read 174 pages to get through the belief propagation algorithm for trees, this took far less time than reading the first 62 pages of Korb and Nicholson, which cover roughly the same ground. The reason: Pearl is a gifted teacher and writer. His explanations are a series of baby steps, leaving nothing out, never assuming the reader will make "obvious" inferences, and supplying motivation every step of the way. Although he doesn't have a lot of figures in the book, the ones he does have are excellent, and by the time I hit page 175, I had a clear picture in my head of not just how it all worked, but why it worked. In fact, after just two days of reading, I was able to implement the belief propagation algorithm in software in an afternoon (I tested the software with examples from the Korb and Nicholson book, so that book was ultimately useful). Pearl made the subject seem almost obvious. If you are looking for a book to help you get canned Bayesian software up and running for an application quickly, this is not it. But if you want to really understand how these things work, and don't have a lot of time available, I cannot imagine a better book than this.