- Hardcover: 552 pages
- Publisher: Morgan Kaufmann Pub; 2nd edition (August 1988)
- Language: English
- ISBN-10: 0934613737
- ISBN-13: 978-0934613736
- Product Dimensions: 22.6 x 14.6 x 3.2 cm
- Shipping Weight: 762 g
- Average Customer Review: 3.7 out of 5 stars See all reviews (3 customer reviews)
- Amazon Bestsellers Rank: #1,133,212 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Details |
Product Description
Product Description
Textbook offers an accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. For graduate-level courses in AI, operations research, and applied probability. Annotation copyright Book News, Inc. Portland, Or.
Inside This Book
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First Sentence
Reasoning about any realistic domain always requires that some simplifications be made. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Reasoning about any realistic domain always requires that some simplifications be made. Read the first page
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Concordance
Browse Sample PagesConcordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most helpful customer reviews
1 of 1 people found the following review helpful
5.0 out of 5 stars
A seminal work,
By Aaron D'Souza (Los Angeles, CA) - See all my reviews
This review is from: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (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.
1.0 out of 5 stars
Not so much,
By
This review is from: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Paperback)
I used this text in addtition to a few others for a course in probablistic reasoning (Bayes nets, etc.) and found that it was very unhelpful. The explanations were very poor and many parts were difficult to read. Also, there weren't very many examples and those that were provided were not very detailed. If you're looking for a text to learn probabilistic reasoning I would suggest trying a different book. Pearl's book could be useful as a 2nd or 3rd reference but not for the primary text. 2 thumbs down.
5.0 out of 5 stars
Fantastic!!!,
By Paul D. Tozour (Austin, TX USA) - See all my reviews
This review is from: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Paperback)
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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