TY - GEN
T1 - Stochastic kriging for simulation metamodeling
AU - Ankenman, Bruce
AU - Nelson, Barry L.
AU - Staum, Jeremy
PY - 2008
Y1 - 2008
N2 - 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. To accomplish this we characterize 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, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.
AB - 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. To accomplish this we characterize 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, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.
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U2 - 10.1109/WSC.2008.4736089
DO - 10.1109/WSC.2008.4736089
M3 - Conference contribution
AN - SCOPUS:60749134718
SN - 9781424427086
T3 - Proceedings - Winter Simulation Conference
SP - 362
EP - 370
BT - Proceedings of the 2008 Winter Simulation Conference, WSC 2008
T2 - 2008 Winter Simulation Conference, WSC 2008
Y2 - 7 December 2008 through 10 December 2008
ER -