Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference. Book • Authors: Judea Pearl. Browse book content. About the book. Sep 1, Vladik Kreinovich, Book review: Uncertain Reasoning Edited by Glenn Shafer and Judea Pearl (Morgan Kaufmann Publishers, Inc., San Mateo. Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie.

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Probabilistic Reasoning in Intelligent Itnelligent 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 judex 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: 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 Jjdea will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and probabilixtic 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 prboabilistic used as an excellent text for graduate-level courses in AI, operations research, or applied probability. Enter your jduea number or email address below and we’ll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer – no Kindle device required. To get the free app, enter your mobile phone number.

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Read more Read less. Discover Prime Book Box for Kids. Kindle Cloud Reader Read instantly in your browser. Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. The Book of Why: The New Science of Cause and Effect. Causal Inference in Statistics: Where and How Civilizations Get Stuck. A Visual Introduction For Beginners.

Editorial Reviews 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. Product details File Size: Morgan Kaufmann; 1 edition June 28, Publication Date: June 28, Sold by: Share your thoughts with other customers.


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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

There was a problem filtering reviews right now. Please try again later. Of course, this book is a classic, and the low number of reviews is only because jueea 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 by Suppes or perhaps even earlier. 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: I purchased this book because of my interest in artificial intelligence. It is the classic on probabilistic reasoning. It was a tough read for me, likely because I had forgotten much of what I knew about probability, and tend to think in terms of Newtonian cause and effect.

Also it was not directly related to neural network models, which is my key AI interest. Anyway, I never finished it. But it helped me prepare mentally for an online course in AI I completed successfully. This and other works on probability really helped me in picking stocks to invest in, especially biotechnology stocks.

I think about many things in a consciously probabilistic way now. I can see why some Romans worshipped Fortuna. There is causation, but a lot of outcomes are probabilistic. This book is really about reasoning, and there is not that much higher math in it. It is the complexity of the reasoning that gave me pause. If you have never studied Bayesian logic, and want to predict the future, he covers this in Chapter 2, which alone made the book worthwhile for me. 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 inteloigent 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. One person found this helpful. Best resource for learning message-passing algorithms, specially for causal relations or directed acyclic graphs. It was soft cover though. In one word, Excellent!!! Very interesting book, gives a great overview into the Bayesian thinking and methods.


Pearl’s “Probabilistic Probabbilistic 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.

However, my impression of book’s target audiences is researchers and readers with a advance understanding of these topics. 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 rexsoning 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.

Jdea 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. See all 13 reviews.

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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl

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