Bayesian optimization with informative parametric models via sequential Monte Carlo
Bayesian optimization (BO) has been a successful approach to optimize expensive functions whose prior knowledge can be specified by means of a probabilistic model. Due to their expressiveness and tractable closed-form predictive distributions, Gaussian process (GP) surrogate models have been the def...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2022-01-01
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| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673622000053/type/journal_article |