Abstract
ABSTRACTThis paper proposes a Bayesian integrative analysis method for linking multi-fidelity computer experiments. Instead of assuming covariance structures of multivariate Gaussian process models, we handle the outputs from different levels of accuracy as independent processes and link them via a penalization method that controls the distance between their overall trends. Based on the priors induced by the penalty, we build Bayesian prediction models for the output at the highest accuracy. Simulated and real examples show that the proposed method is better than existing methods in terms of prediction accuracy for many cases.
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