Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
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A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
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Bayesian Active Learning for Bayesian Model Updating: the Art of Acquisition Functions and Beyond
Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
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Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.