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Sensitivity Analysis of Stochastic Simulators with Information Theory

Abstract

The increased computational power available today has made the use of computer models or simulators common in many fields. While there is a widely adopted set of tools for the analysis of simulators, there are still many unsolved problems when dealing with these models. Specifically, the traditional methods for sensitivity analysis of deterministic computer models, based on functional ANOVA decompositions, do not generalize well to simulators with stochastic or nondeterministic output. This paper presents a methodological solution for conducting sensitivity analysis on computer models with stochastic output through the use of information theory and Bayesian density regression. The presented method is applied to the inputs of a near-fault ground motion stochastic simulator of Dabaghi et. al (2011).

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