Kriging: Methods and Applications

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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 languageEnglish
Place of PublicationTilburg
PublisherCentER, Center for Economic Research
Number of pages17
Volume2017-047
Publication statusPublished - 21 Nov 2017

Publication series

NameCentER Discussion Paper
Volume2017-047

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Keywords

  • Gaussian process
  • Latin hypercube
  • deterministic simulation
  • random simulation
  • sensitivity analysis
  • optimization

Cite this

Kleijnen, J. P. C. (2017). Kriging: Methods and Applications. (CentER Discussion Paper; Vol. 2017-047). Tilburg: CentER, Center for Economic Research.