Kriging: Methods and Applications

J.P.C. Kleijnen

Kriging: Methods and Applications

J.P.C. Kleijnen

Department of Management

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Abstract

In this chapter we present Kriging— also known as a Gaussian process (GP) model— which is a mathematical interpolation method. To select the input combinations to be simulated, we use Latin hypercube sampling (LHS); we allow uniform and non-uniform distributions of the simulation inputs. Besides deterministic simulation we discuss random simulation, which requires adjusting the design and analysis. We discuss sensitivity analysis of simulation models, using "functional analysis of variance" (FANOVA)— also known as Sobol sensitivity indexes. Finally, we discuss
optimization of the simulated system, including "robust" optimization.

Original language	English
Place of Publication	Tilburg
Publisher	CentER, Center for Economic Research
Number of pages	17
Volume	2017-047
Publication status	Published - 21 Nov 2017

Publication series

Name	CentER Discussion Paper
Volume	2017-047

Keywords

Gaussian process
Latin hypercube
deterministic simulation
random simulation
sensitivity analysis
optimization

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2017-047Submitted manuscript, 269 KB

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AB - In this chapter we present Kriging— also known as a Gaussian process (GP) model— which is a mathematical interpolation method. To select the input combinations to be simulated, we use Latin hypercube sampling (LHS); we allow uniform and non-uniform distributions of the simulation inputs. Besides deterministic simulation we discuss random simulation, which requires adjusting the design and analysis. We discuss sensitivity analysis of simulation models, using "functional analysis of variance" (FANOVA)— also known as Sobol sensitivity indexes. Finally, we discussoptimization of the simulated system, including "robust" optimization.

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KW - random simulation

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KW - optimization

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Kriging: Methods and Applications

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Keywords

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