Adaptive Design of Experiments Based on Gaussian Processes
Evgeny Burnaev, Maxim Panov
Statistical Learning and Data Sciences
We consider a problem of adaptive design of experiments for Gaussian process regression. We introduce a Bayesian framework, which provides theoretical justification for some well-know heuristic criteria from the literature and also gives an opportunity to derive some new criteria. We also perform testing of methods in question on a big set of multidimensional functions.