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

Probabilistic Reasoning in Intelligent Systems

Networks of Plausible Inference

Paperback Published: 31st May 1997
ISBN: 9781558604797
Number Of Pages: 552

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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.

Uncertainty In AI Systems: An Overview
Bayesian Inference
Markov and Bayesian Networks: Two Graphical Representations of Probabilistic Knowledge
Belief Updating by Network Propagation
Distributed Revision of Composite Beliefs
Decision and Control
Taxonomic Hierarchies, Continuous Variables, and Uncertain Probabilities
Learning Structure from Data
Non-Bayesian Formalisms for Managing Uncertainty
Logic and Probability: The Strange Connection
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9781558604797
ISBN-10: 1558604790
Series: Morgan Kaufmann Series in Representation and Reasoning
Audience: Tertiary; University or College
Format: Paperback
Language: English
Number Of Pages: 552
Published: 31st May 1997
Publisher: Elsevier Science & Technology
Country of Publication: US
Dimensions (cm): 22.9 x 15.5  x 2.9
Weight (kg): 0.84

Earn 226 Qantas Points
on this Book

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