Kriging: Methods and Applications

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.

Keywords: Gaussian process, Latin hypercube, deterministic simulation, random simulation, sensitvity analysis

JEL Classification: C0, C1, C9, C15, C44