Learning Heteroscedastic Gaussian Processes for Complex Datasets
Alexis Boukouvalas, Dan Cornford · 2009
In this report we present a novel method of performing Gaussian Process (GP) regression on complex datasets with replicated observations while addressing the issue of the placement of training set points. Such datasets can be found in numerous areas of engineering and science. Our motivating example comes from the eld of computer experiments where