Bayesian Analysis in Expert Systems

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Standard

Bayesian Analysis in Expert Systems. / SPIEGELHALTER, DJ; DAWID, AP; Lauritzen, Steffen L.; COWELL, RG.

I: Statistical Science, Bind 8, Nr. 3, 1993, s. 219-247.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

SPIEGELHALTER, DJ, DAWID, AP, Lauritzen, SL & COWELL, RG 1993, 'Bayesian Analysis in Expert Systems', Statistical Science, bind 8, nr. 3, s. 219-247. https://doi.org/10.1214/ss/1177010888

APA

SPIEGELHALTER, DJ., DAWID, AP., Lauritzen, S. L., & COWELL, RG. (1993). Bayesian Analysis in Expert Systems. Statistical Science, 8(3), 219-247. https://doi.org/10.1214/ss/1177010888

Vancouver

SPIEGELHALTER DJ, DAWID AP, Lauritzen SL, COWELL RG. Bayesian Analysis in Expert Systems. Statistical Science. 1993;8(3):219-247. https://doi.org/10.1214/ss/1177010888

Author

SPIEGELHALTER, DJ ; DAWID, AP ; Lauritzen, Steffen L. ; COWELL, RG. / Bayesian Analysis in Expert Systems. I: Statistical Science. 1993 ; Bind 8, Nr. 3. s. 219-247.

Bibtex

@article{e631c58d557d4145899f209ed56daaa5,
title = "Bayesian Analysis in Expert Systems",
abstract = "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.",
author = "DJ SPIEGELHALTER and AP DAWID and Lauritzen, {Steffen L.} and RG COWELL",
year = "1993",
doi = "10.1214/ss/1177010888",
language = "English",
volume = "8",
pages = "219--247",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "3",

}

RIS

TY - JOUR

T1 - Bayesian Analysis in Expert Systems

AU - SPIEGELHALTER, DJ

AU - DAWID, AP

AU - Lauritzen, Steffen L.

AU - COWELL, RG

PY - 1993

Y1 - 1993

N2 - 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.

AB - 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.

U2 - 10.1214/ss/1177010888

DO - 10.1214/ss/1177010888

M3 - Journal article

VL - 8

SP - 219

EP - 247

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 3

ER -

ID: 128007334