Probabilistic Reasoning in Intelligent Systems by Judea Pearl

Finicky Bee
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Item specifics

Condition
Good: A book that has been read but is in good condition. Very minimal damage to the cover including ...
Edition
2nd Printing
ISBN
9781558604797
Subject Area
Computers, Philosophy
Publication Name
Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference
Publisher
Elsevier Science & Technology
Item Length
9 in
Subject
Intelligence (Ai) & Semantics, General
Publication Year
1997
Type
Textbook
Format
Trade Paperback
Language
English
Author
Judea Pearl
Item Weight
26.6 Oz
Item Width
6 in
Number of Pages
552 Pages
Category

About this product

Product Identifiers

Publisher
Elsevier Science & Technology
ISBN-10
1558604790
ISBN-13
9781558604797
eBay Product ID (ePID)
1056568

Product Key Features

Number of Pages
552 Pages
Language
English
Publication Name
Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference
Subject
Intelligence (Ai) & Semantics, General
Publication Year
1997
Type
Textbook
Subject Area
Computers, Philosophy
Author
Judea Pearl
Format
Trade Paperback

Dimensions

Item Weight
26.6 Oz
Item Length
9 in
Item Width
6 in

Additional Product Features

Intended Audience
Scholarly & Professional
Dewey Edition
19
Illustrated
Yes
Dewey Decimal
006.3
Table Of Content
Chapter 1 Uncertainty In AI Systems: An Overview Chapter 2 Bayesian Inference Chapter 3 Markov and Bayesian Networks: Two Graphical Representations of Probabilistic Knowledge Chapter 4 Belief Updating by Network Propagation Chapter 5 Distributed Revision of Composite Beliefs Chapter 6 Decision and Control Chapter 7 Taxonomic Hierarchies, Continuous Variables, and Uncertain Probabilities Chapter 8 Learning Structure from Data Chapter 9 Non-Bayesian Formalisms for Managing Uncertainty Chapter 10 Logic and Probability: The Strange Connection
Synopsis
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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