2009Unpublished venueRequires access

Better simulation metamodeling: The why, what, and how of stochastic kriging

Jeremy Staum

Open publisher page 69 citations

Abstract

Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.

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What this paper is about

Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.

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OpenAlex reports 69 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.

Key concepts: Metamodeling, Kriging, Stochastic modelling, Computer science, Stochastic simulation, Variogram, Stochastic process, Mathematical optimization

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