Dekorationsartikel gehören nicht zum Leistungsumfang.
Probabilistic Reasoning in Intelligent Systems
Networks of Plausible Inference
Taschenbuch von Judea Pearl
Sprache: Englisch

74,20 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 1-2 Wochen

Kategorien:
Beschreibung
"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.

"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.

Inhaltsverzeichnis
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
Details
Erscheinungsjahr: 1997
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 584
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781558604797
ISBN-10: 1558604790
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Pearl, Judea
Hersteller: Elsevier Science & Technology
Maße: 230 x 155 x 26 mm
Von/Mit: Judea Pearl
Erscheinungsdatum: 31.05.1997
Gewicht: 0,753 kg
preigu-id: 126698587
Inhaltsverzeichnis
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
Details
Erscheinungsjahr: 1997
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 584
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781558604797
ISBN-10: 1558604790
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Pearl, Judea
Hersteller: Elsevier Science & Technology
Maße: 230 x 155 x 26 mm
Von/Mit: Judea Pearl
Erscheinungsdatum: 31.05.1997
Gewicht: 0,753 kg
preigu-id: 126698587
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte