Stochastic kriging for simulation metamodeling

Bruce E Ankenman*, Barry L Nelson, Jeremy C Staum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Scopus citations

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. 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 Winter Simulation Conference, WSC 2008
Pages362-370
Number of pages9
DOIs
StatePublished - Dec 1 2008
Event2008 Winter Simulation Conference, WSC 2008 - Miami, FL, United States
Duration: Dec 7 2008Dec 10 2008

Other

Other2008 Winter Simulation Conference, WSC 2008
CountryUnited States
CityMiami, FL
Period12/7/0812/10/08

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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