Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
We introduce a class of acquisition functions for sample selection that lead to faster convergence in applications related to Bayesian experimental design and uncertainty quantification. The approach follows the paradigm of active learning, whereby existing samples of a black-box function are utiliz...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Society for Industrial & Applied Mathematics (SIAM)
2022
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| Online Access: | https://hdl.handle.net/1721.1/139638 |