Probabilistic expert systems:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia, Pa.
Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104)
1996
|
| Schriftenreihe: | CBMS-NSF regional conference series in applied mathematics
67 |
| Schlagworte: | |
| Online-Zugang: | TUM01 UBW01 UBY01 UER01 Volltext |
| Beschreibung: | Mode of access: World Wide Web. - System requirements: Adobe Acrobat Reader Includes bibliographical references (s. 69-77) and index Preface -- Chapter 1. Multivariate probability. Probability distributions; Marginalization; Conditionals; Continuation; Posterior distributions; Expectation; Classifying probability Distributions; A limitation -- Chapter 2. Construction sequences. Multiplying conditionals; DAGs and belief nets; Bubble graphs; Other graphical representations -- Chapter 3. Propagation in join trees. Variable-by-variable summing out; The elementary architecture; The Shafer-Shenoy architecture; The Lauritzen-Spiegelhalter architecture; The Aalborg architecture; COLLECT and DISTRIBUTE; Scope and alternatives -- Chapter 4. Resources and references. Meetings; Software; Books; Review articles; Other sources -- Index 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 |
| Beschreibung: | 1 Online-Ressource (viii, 80 Seiten) |
| ISBN: | 0898713730 9780898713732 |
| DOI: | 10.1137/1.9781611970043 |
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| 500 | |a Preface -- Chapter 1. Multivariate probability. Probability distributions; Marginalization; Conditionals; Continuation; Posterior distributions; Expectation; Classifying probability Distributions; A limitation -- Chapter 2. Construction sequences. Multiplying conditionals; DAGs and belief nets; Bubble graphs; Other graphical representations -- Chapter 3. Propagation in join trees. Variable-by-variable summing out; The elementary architecture; The Shafer-Shenoy architecture; The Lauritzen-Spiegelhalter architecture; The Aalborg architecture; COLLECT and DISTRIBUTE; Scope and alternatives -- Chapter 4. Resources and references. Meetings; Software; Books; Review articles; Other sources -- Index | ||
| 500 | |a 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 | ||
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Datensatz im Suchindex
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| id | DE-604.BV039747296 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:10:18Z |
| institution | BVB |
| isbn | 0898713730 9780898713732 |
| language | English |
| lccn | 96018757 |
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| series | CBMS-NSF regional conference series in applied mathematics |
| series2 | CBMS-NSF regional conference series in applied mathematics |
| spelling | Shafer, Glenn 1946- Verfasser (DE-588)131924060 aut Probabilistic expert systems Glenn Shafer Philadelphia, Pa. Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104) 1996 1 Online-Ressource (viii, 80 Seiten) txt rdacontent c rdamedia cr rdacarrier CBMS-NSF regional conference series in applied mathematics 67 Mode of access: World Wide Web. - System requirements: Adobe Acrobat Reader Includes bibliographical references (s. 69-77) and index Preface -- Chapter 1. Multivariate probability. Probability distributions; Marginalization; Conditionals; Continuation; Posterior distributions; Expectation; Classifying probability Distributions; A limitation -- Chapter 2. Construction sequences. Multiplying conditionals; DAGs and belief nets; Bubble graphs; Other graphical representations -- Chapter 3. Propagation in join trees. Variable-by-variable summing out; The elementary architecture; The Shafer-Shenoy architecture; The Lauritzen-Spiegelhalter architecture; The Aalborg architecture; COLLECT and DISTRIBUTE; Scope and alternatives -- Chapter 4. Resources and references. Meetings; Software; Books; Review articles; Other sources -- Index 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 Expert systems (Computer science) Probabilities Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf Expertensystem (DE-588)4113491-6 gnd rswk-swf Expertensystem (DE-588)4113491-6 s Wahrscheinlichkeit (DE-588)4137007-7 s DE-604 Erscheint auch als Druck-Ausgabe, Paperback 0898713730 Erscheint auch als Druck-Ausgabe, Paperback 9780898713732 CBMS-NSF regional conference series in applied mathematics 67 (DE-604)BV046682627 67 https://doi.org/10.1137/1.9781611970043 Verlag Volltext |
| spellingShingle | Shafer, Glenn 1946- Probabilistic expert systems CBMS-NSF regional conference series in applied mathematics Expert systems (Computer science) Probabilities Wahrscheinlichkeit (DE-588)4137007-7 gnd Expertensystem (DE-588)4113491-6 gnd |
| subject_GND | (DE-588)4137007-7 (DE-588)4113491-6 |
| title | Probabilistic expert systems |
| title_auth | Probabilistic expert systems |
| title_exact_search | Probabilistic expert systems |
| title_full | Probabilistic expert systems Glenn Shafer |
| title_fullStr | Probabilistic expert systems Glenn Shafer |
| title_full_unstemmed | Probabilistic expert systems Glenn Shafer |
| title_short | Probabilistic expert systems |
| title_sort | probabilistic expert systems |
| topic | Expert systems (Computer science) Probabilities Wahrscheinlichkeit (DE-588)4137007-7 gnd Expertensystem (DE-588)4113491-6 gnd |
| topic_facet | Expert systems (Computer science) Probabilities Wahrscheinlichkeit Expertensystem |
| url | https://doi.org/10.1137/1.9781611970043 |
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| work_keys_str_mv | AT shaferglenn probabilisticexpertsystems |