Bayesian analysis in expert systems
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert systems. Using a real, moderately complex, medical example we illustrate how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief netwo...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Institute of Mathematical Statistics
1993
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| Subjects: |
| _version_ | 1797067727457746944 |
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| author | Spiegelhalter, D Dawid, A Lauritzen, S Cowell, R |
| author_facet | Spiegelhalter, D Dawid, A Lauritzen, S Cowell, R |
| author_sort | Spiegelhalter, D |
| collection | OXFORD |
| description | We review recent developments in applying Bayesian probabilistic and statistical ideas to expert systems. Using a real, moderately complex, medical example we illustrate how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context. Exact probabilistic inference on individual cases is possible using a general propagation procedure. When data on a series of cases are available, Bayesian statistical techniques can be used for updating the original subjective quantitative inputs, and we present a sets of diagnostics for identifying conflicts between the data and the prior specification. A model comparison procedure is explored, and a number of links made with mainstream statistical methods. Details are given on the use of Dirichlet prior distributions for learning about parameters and the process of transforming the original graphical model to a junction tree as the basis for efficient computation. |
| first_indexed | 2024-03-06T22:00:27Z |
| format | Journal article |
| id | oxford-uuid:4e655c9a-0c2d-43b6-b1cc-397e24c04d12 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:00:27Z |
| publishDate | 1993 |
| publisher | Institute of Mathematical Statistics |
| record_format | dspace |
| spelling | oxford-uuid:4e655c9a-0c2d-43b6-b1cc-397e24c04d122022-03-26T16:01:00ZBayesian analysis in expert systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4e655c9a-0c2d-43b6-b1cc-397e24c04d12ProbabilityStatistics (see also social sciences)EnglishOxford University Research Archive - ValetInstitute of Mathematical Statistics1993Spiegelhalter, DDawid, ALauritzen, SCowell, RWe review recent developments in applying Bayesian probabilistic and statistical ideas to expert systems. Using a real, moderately complex, medical example we illustrate how qualitative and quantitative knowledge can be represented within a directed graphical model, generally known as a belief network in this context. Exact probabilistic inference on individual cases is possible using a general propagation procedure. When data on a series of cases are available, Bayesian statistical techniques can be used for updating the original subjective quantitative inputs, and we present a sets of diagnostics for identifying conflicts between the data and the prior specification. A model comparison procedure is explored, and a number of links made with mainstream statistical methods. Details are given on the use of Dirichlet prior distributions for learning about parameters and the process of transforming the original graphical model to a junction tree as the basis for efficient computation. |
| spellingShingle | Probability Statistics (see also social sciences) Spiegelhalter, D Dawid, A Lauritzen, S Cowell, R Bayesian analysis in expert systems |
| title | Bayesian analysis in expert systems |
| title_full | Bayesian analysis in expert systems |
| title_fullStr | Bayesian analysis in expert systems |
| title_full_unstemmed | Bayesian analysis in expert systems |
| title_short | Bayesian analysis in expert systems |
| title_sort | bayesian analysis in expert systems |
| topic | Probability Statistics (see also social sciences) |
| work_keys_str_mv | AT spiegelhalterd bayesiananalysisinexpertsystems AT dawida bayesiananalysisinexpertsystems AT lauritzens bayesiananalysisinexpertsystems AT cowellr bayesiananalysisinexpertsystems |