Abstract
Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes. This paper derives the best linear unbiased prediction for three popular classes of multiple-level Gaussian process models. The predictors all have explicit expressions at each untried point. Empirical best linear unbiased predictors are also provided by plug-in methods with generalized maximum likelihood estimators of unknown parameters.
Highlights
With the rapid development of computer technology, computer experiments have been widely used in engineering and science [1]
A Bayesian approach is described to predict and analyze complex computer codes which can be run at different levels of sophistication [2]
A novel approach is taken to integrate data from approximate and detailed simulations to build a surrogate model that describes the relationship between output and input parameters [3]
Summary
With the rapid development of computer technology, computer experiments have been widely used in engineering and science [1]. It is a growing trend to study the complex system which contains multifidelity computer codes with different levels of accuracy. A Bayesian approach is described to predict and analyze complex computer codes which can be run at different levels of sophistication [2]. A class of nonstationary Gaussian process models are proposed to link the computer outputs of different mesh densities [5]. The purpose of this article is to find BLUPs for multifidelity computer experiments.
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