Probabilistic Expert Systems
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Expert Systems And Probabilistic Network Models
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Author : Enrique Castillo
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Expert Systems And Probabilistic Network Models written by Enrique Castillo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Probabilistic Expert Systems
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Author : Glenn Shafer
language : en
Publisher: SIAM
Release Date : 1996-01-01
Probabilistic Expert Systems written by Glenn Shafer and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-01-01 with Computers categories.
Probabilistic Expert Systems emphasizes the basic computational principles that make probabilistic reasoning feasible in expert systems. The key to computation in these systems is the modularity of the probabilistic model. Shafer describes and compares the principal architectures for exploiting this modularity in the computation of prior and posterior probabilities. He also indicates how these similar yet different architectures apply to a wide variety of other problems of recursive computation in applied mathematics and operations research. The field of probabilistic expert systems has continued to flourish since the author delivered his lectures on the topic in June 1992, but the understanding of join-tree architectures has remained missing from the literature. This monograph fills this void by providing an analysis of join-tree methods for the computation of prior and posterior probabilities in belief nets. These methods, pioneered in the mid to late 1980s, continue to be central to the theory and practice of probabilistic expert systems. In addition to purely probabilistic expert systems, join-tree methods are also used in expert systems based on Dempster-Shafer belief functions or on possibility measures. Variations are also used for computation in relational databases, in linear optimization, and in constraint satisfaction. This book describes probabilistic expert systems in a more rigorous and focused way than existing literature, and provides an annotated bibliography that includes pointers to conferences and software. Also included are exercises that will help the reader begin to explore the problem of generalizing from probability to broader domains of recursive computation.
Probabilistic Networks And Expert Systems
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Author : Robert G. Cowell
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-16
Probabilistic Networks And Expert Systems written by Robert G. Cowell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-16 with Computers categories.
Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.
Probabilistic Reasoning In Expert Systems
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Author : Richard E. Neapolitan
language : en
Publisher: Wiley-Interscience
Release Date : 1990-03-16
Probabilistic Reasoning In Expert Systems written by Richard E. Neapolitan and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-03-16 with Computers categories.
Addresses the use probability theory as a tool for designing with and implementing uncertainity reasoning. Provides many concrete algorithms, explores techniques for solving multimembership classification problems not based directly on causal networks, and offers practical recommendations, matching specific methods with sample expert systems.
Probabilistic Networks And Expert Systems
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Author : Robert G. Cowell
language : en
Publisher: Springer
Release Date : 2007-07-25
Probabilistic Networks And Expert Systems written by Robert G. Cowell and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-25 with Mathematics categories.
Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.
Uncertain Information Processing In Expert Systems
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Author : Petr Hajek
language : en
Publisher: CRC Press
Release Date : 1992-06-29
Uncertain Information Processing In Expert Systems written by Petr Hajek and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-06-29 with Computers categories.
Uncertain Information Processing in Expert Systems systematically and critically examines probabilistic and rule-based (compositional, MYCIN-like) systems, the two most important families of expert systems dealing with uncertainty. The book features a detailed introduction to probabilistic systems (including methods using graphical models and methods of knowledge integration), an analysis of compositional systems based on algebraic considerations, an application of graphical models, and the Dempster-Shafer theory of evidence and its use in expert systems. The book will be useful to anyone working in artificial intelligence, statistical computing, symbolic logic, and expert systems.
Probabilistic Methods In Expert Systems
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Author : Romano Scozzafava
language : en
Publisher:
Release Date : 1993
Probabilistic Methods In Expert Systems written by Romano Scozzafava and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Expert systems (Computer science) categories.
Probabilistic Reasoning In Intelligent Systems
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Author : Judea Pearl
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Probabilistic Reasoning In Intelligent Systems written by Judea Pearl and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
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 Similarity Networks
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Author : David E. Heckerman
language : en
Publisher: MIT Press (MA)
Release Date : 1991
Probabilistic Similarity Networks written by David E. Heckerman and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
In this remarkable blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems—expert systems that encode knowledge in a decision-theoretic framework. Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation. He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases. Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user. He concludes with a formal evaluation of the power of his methods for building normative expert systems. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University. Contents: Introduction. Similarity Networks and Partitions: A Simple Example. Theory of Similarity Networks. Pathfinder: A Case Study. An Evaluation of Pathfinder. Conclusions and Future Work.
Uncertain Information Processing In Expert Systems
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Author :
language : en
Publisher:
Release Date : 1992
Uncertain Information Processing In Expert Systems written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Expert systems (Computer science) categories.