Techniques for Bayesian analysis of expert systems

David Spiegelhalter; Steffen L. Lauritzen

Techniques for Bayesian analysis of expert systems

David Spiegelhalter, Steffen L. Lauritzen

4 Citationer (Scopus)

Abstract

A causal network is frequently used as a representation for qualitative medical knowledge,
in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.

Originalsprog	Engelsk
Tidsskrift	Annals of Mathematics and Artificial Intelligence
Vol/bind	2
Sider (fra-til)	353-366
ISSN	1012-2443
Status	Udgivet - 1990
Udgivet eksternt	Ja

Citationsformater

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abstract = "A causal network is frequently used as a representation for qualitative medical knowledge,in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.",

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TY - JOUR

T1 - Techniques for Bayesian analysis of expert systems

AU - Spiegelhalter, David

AU - Lauritzen, Steffen L.

PY - 1990

Y1 - 1990

N2 - A causal network is frequently used as a representation for qualitative medical knowledge,in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.

AB - A causal network is frequently used as a representation for qualitative medical knowledge,in which conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. For probabilities temporarily assumed known, we describe efficient algorithms for propagating the effects of multiple items of evidence around multiply-connected networks and hence providing precise probabilistic revision of beliefs concerning the current patient. As a database accumulates we also require the quantitative aspects of the model to be updated, as well as to learn about the qualitative structure, and we suggest some formal statistical tools for these problems.

M3 - Journal article

SN - 1012-2443

VL - 2

SP - 353

EP - 366

JO - Annals of Mathematics and Artificial Intelligence

JF - Annals of Mathematics and Artificial Intelligence

ER -