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.
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 |
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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.