Best Linear Unbiased Prediction for Multifidelity Computer Experiments

Abstract EN

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.