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Using sensitivity analysis for efficient quantification of a belief network.

V M Coupé·N Peek·J Ottenkamp·J D Habbema
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Abstract

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.

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AlgorithmsArtificial IntelligenceHeart Septal Defects, VentricularHeart VentriclesHumansModels, TheoreticalNeural Networks, ComputerJournal ArticleResearch Support, Non-U.S. Gov't

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V M Coupé, N Peek, J Ottenkamp, J D Habbema. 1999. Using sensitivity analysis for efficient quantification of a belief network.. https://doi.org/10.1016/s0933-3657(99)00024-x

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