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.
CITATION STYLE
Burnaev, E., & Panov, M. (2015). Adaptive design of experiments based on gaussian processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9047, pp. 116–125). Springer Verlag. https://doi.org/10.1007/978-3-319-17091-6_7
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