Stochastic kriging for simulation metamodeling

Abstract

We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation out-put performance measures as functions of the controllable design or decision variables. To accomplish this we charac-terize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, de-rive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method. 1

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