Priors learned from legacy reconstructions inherit undetectable overconfidence

Excerpt

arXiv:2607.21721v3 Announce Type: replace-cross Abstract: Where truths are scarce (e.g., seismic and medical imaging), a prior for an ill-posed inverse problem is trained on an archive of legacy reconstructions---an older method's outputs---and its uncertainty is treated as data-driven. In the population limit, an archive of posterior samples is the regularizer that produced it, advanced one expectation-maximization step toward the truth. On directions the operator resolves, it improves the assu