Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

arXiv:2305.20004v3 Announce Type: replace-cross
Abstract: Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard because it alleviates ill-posedness issues and quantifies epistemic uncertainty. Since analytical posteriors are not typically available, one resorts to Markov chain Monte Carlo sampling or approximate variational inference. However, inference needs to be rerun from scratch for each new set of data. This drawback limits the applicability of the Bayesian formulation …

abstract arxiv bayesian cs.lg data engineering experimental flow gold guides inference parameters physics.data-an replace science solve standard stat.ml type uncertainty