Using sensitivity analysis for efficient quantification of a belief network

Veerle M.H. Coupé*, Niels Peek, Jaap Ottenkamp, J. Dik F. Habbema

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorly-informed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the well-informed network quantification; these influential parameters were found by performing one-way sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highly-informed network parameters to obtain a satisfying network quantification. It is therefore concluded that sensitivity analysis can be used to improve the efficiency of quantifying a belief network. Copyright (C) 1999 Elsevier Science B.V.

Original languageEnglish
Pages (from-to)223-247
Number of pages25
JournalArtificial Intelligence in Medicine
Issue number3
Publication statusPublished - Nov 1999

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