Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments

Youngjun Choe (), Henry Lam and Eunshin Byon
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Youngjun Choe: University of Washington
Henry Lam: Columbia University
Eunshin Byon: University of Michigan

Methodology and Computing in Applied Probability, 2018, vol. 20, issue 4, 1155-1172

Abstract: Abstract Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become more realistic. To speed up, importance sampling coupled with near-optimal sampling allocation for these experiments is recently proposed to efficiently estimate the probability associated with the stochastic system output. In this study, we establish the central limit theorem for the probability estimator from such procedure and construct an asymptotically valid confidence interval to quantify estimation uncertainty. We apply the proposed approach to a numerical example and present a case study for evaluating the structural reliability of a wind turbine.

Keywords: Central limit theorem; Confidence interval; Importance sampling; Monte Carlo simulation; Variance reduction; 65C05; 68U20; 62F12 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11009-017-9599-7

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