2010
DOI: 10.1287/opre.1090.0754
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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 output performance measures as functions of the controllable design or decision variables, or uncontrollable environmental variables. To accomplish this, we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknow… Show more

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Cited by 527 publications
(374 citation statements)
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“…In recent years, there has been an increasing interest in the study of "stochastic" simulators, whose outputs can only be observed in the presence of noise. Examples of such simulators can be found in a wide range of applications, including nuclear safety assessment [9], discrete event simulation [1], acoustic wave propagation in turbulent fluids [17], airfoil optimization [23], design of composite materials [32,31] and experimental measurements in mechanical engineering [4].…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, there has been an increasing interest in the study of "stochastic" simulators, whose outputs can only be observed in the presence of noise. Examples of such simulators can be found in a wide range of applications, including nuclear safety assessment [9], discrete event simulation [1], acoustic wave propagation in turbulent fluids [17], airfoil optimization [23], design of composite materials [32,31] and experimental measurements in mechanical engineering [4].…”
Section: Introductionmentioning
confidence: 99%
“…We derived a new kernel regression estimator with respect to this distance replacing the L 2 distance by the Hellinger distance in equation (3). The kernel estimator thus takes the following form:…”
Section: Kernel Regression With the Hellinger Distancementioning
confidence: 99%
“…We provide a brief overview of how this is accomplished, but space prevents us from discussing many important practical details in how the prior should be chosen. We point the interested reader to the textbook Rasmussen and Williams (2006), and to work on kriging (Cressie 1993) and stochastic kriging (Ankenman et al 2008, Ankenman et al 2010.…”
Section: Bayesian Inferencementioning
confidence: 99%