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 neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
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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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Physics-informed neural particle flow for the Bayesian update step
A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.