A sensitivity analysis of probabilistic sensitivity analysis in terms of the density function for the input variables

Probabilistic sensitivity analysis (SA) allows to incorporate background knowledge on the considered input variables more easily than many other existing SA techniques. Incorporation of such knowledge is performed by constructing a joint density function over the input domain. However, it rarely happens that available knowledge directly and uniquely translates into such a density function. A naturally arising question is then to what extent the choice of density function determines the values of the considered sensitivity measures. In this paper we perform simulation studies to address this question. Our empirical analysis suggests some guidelines, but also cautions to practitioners in the field of probabilistic SA.

Publication year:2017

Keywords:probabilistic sensitivity analysis, agent-based models, Gaussian process emulation, mean effect, sensitivity index, Probabilistic sensitivity analysis

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BOF-publication weight:1

CSS-citation score:1

Authors from:Higher Education

Accessibility:Open