Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators

ArXi:2602.21426v2 Announce Type: replace We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our method belongs to the family of independence Metropolis-Hastings (IMH) sampling algorithms, which are common in Bayesian inference. Relying on the existence of an approximate posterior distribution that is cheaper to sample from but may have significant bias, we