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Stochastic intrinsic Kriging for simulation metamodeling
Author(s) -
Mehdad Ehsan,
Kleijnen Jack P.C.
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2300
Subject(s) - kriging , metamodeling , variogram , stochastic simulation , geostatistics , gaussian process , computer science , gaussian , stochastic process , mathematical optimization , mathematics , random field , deterministic simulation , algorithm , statistics , machine learning , physics , quantum mechanics , programming language , spatial variability
Kriging (or a Gaussian process) provides metamodels for deterministic and random simulation models. Actually, there are several types of Kriging; the classic type is the so‐called universal Kriging, which includes ordinary Kriging. These classic types require estimation of the trend in the input‐output data of the underlying simulation model; this estimation weakens the Kriging metamodel. We therefore consider the so‐called intrinsic Kriging (IK), which originated in geostatistics, and derive IK types for deterministic simulations and random simulations, respectively. Moreover, for random simulations, we derive experimental designs that specify the number of replications that varies with the input combination of the simulation model. To compare the performance of IK and classic Kriging, we use several numerical experiments with deterministic simulations and random simulations, respectively. These experiments show that IK gives better metamodels, in most experiments.