Easy-to-Interpret Gaussian Process Models for Computer
Experiments
Description
Fit model for datasets with easy-to-interpret Gaussian process modeling, predict responses for new inputs.
The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
The output variable of the datasets is a scalar (quantitative).
The optimization of the likelihood function can be chosen by the users (see the documentation of EzGP_fit()).
The modeling method is published in "EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors"
by Qian Xiao, Abhyuday Mandal, C. Devon Lin, and Xinwei Deng (2022) .