Adaptive Design of Experiments Based on Gaussian Processes

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Authors:

Evgeny Burnaev, Maxim Panov

Journal:

Statistical Learning and Data Sciences

Abstract:

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: Active learning, Computer experiments, Design of experiments, Gaussian processes

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