Open Access
August, 1993 Bayesian Analysis in Expert Systems
David J. Spiegelhalter, A. Philip Dawid, Steffen L. Lauritzen, Robert G. Cowell
Statist. Sci. 8(3): 219-247 (August, 1993). DOI: 10.1214/ss/1177010888


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 set 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.


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David J. Spiegelhalter. A. Philip Dawid. Steffen L. Lauritzen. Robert G. Cowell. "Bayesian Analysis in Expert Systems." Statist. Sci. 8 (3) 219 - 247, August, 1993.


Published: August, 1993
First available in Project Euclid: 19 April 2007

zbMATH: 0955.62523
MathSciNet: MR1243594
Digital Object Identifier: 10.1214/ss/1177010888

Keywords: Bayes factors , Conditional independence , Dirichlet distribution , graphical models , junction tree , local computation , monitors , prediction , prequential analysis , subjective probability , Triangulation , unsupervised learning

Rights: Copyright © 1993 Institute of Mathematical Statistics

Vol.8 • No. 3 • August, 1993
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