Adaptive Design of Experiments Based on Gaussian Processes

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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.

Keywords: Adaptive Design of Experiments, Approximation, Gaussian Processes


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