Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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Elsevier, Jun 28, 2014 - Computers - 552 pages
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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.

 

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Contents

AN OVERVIEW
1
Chapter 2 BAYESIAN INFERENCE
29
Chapter 3 MARKOV AND BAYESIAN NETWORKS
77
Chapter 4 BELIEF UPDATING BY NETWORK PROPAGATION
143
Chapter 5 DISTRIBUTED REVISION OF COMPOSITE BELIEFS
239
Chapter 6 DECISION AND CONTROL
289
Chapter 7 TAXONOMIC HIERARCHIES CONTINUOUS VARIABLES AND UNCERTAIN PROBABILITIES
333
Chapter 8 LEARNING STRUCTURE FROM DATA
381
Chapter 9 NONBAYESIAN FORMALISMS FOR MANAGING UNCERTAINTY
415
THE STRANGE CONNECTION
467
Exercises
518
Bibliography
521
Author Index
539
Subject Index
545
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About the author (2014)

Judea and Ruth Pearl, the parents of Daniel Pearl, are cofounders of the Daniel Pearl Foundation (www.danielpearl.org). The Foundation's mission is to promote cross-cultural understanding through journalism, music and innovative communications.

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