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
- Sequential design
- Gaussian processes
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Burnaev, E., Panov, M. (2015). Adaptive Design of Experiments Based on Gaussian Processes. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_7
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DOI: https://doi.org/10.1007/978-3-319-17091-6_7
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