Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification

DOI10.1137/20M1347486zbMath1464.62356arXiv2006.12394OpenAlexW3163205789MaRDI QIDQ4995116

Themistoklis P. Sapsis, Antoine Blanchard

Publication date: 23 June 2021

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.12394

zbMATH Keywords

importance samplingrare eventsactive learninguncertainty quantificationoptimal experimental design

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Sampling theory, sample surveys (62D05) Extreme value theory; extremal stochastic processes (60G70) Statistics of extreme values; tail inference (62G32) Hydrology, hydrography, oceanography (86A05)

Related Items (3)

APIK: Active Physics-Informed Kriging Model with Partial Differential Equations ⋮ Multifidelity Bayesian Experimental Design to Quantify Rare-Event Statistics ⋮ Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems

Uses Software

• Python
• t-SNE
• SMAC

Cites Work

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