Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers.

ABSTRACT: In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. While algorithms developed for this purpose can produce high-quality, diverse point estimates of the unknown parameters of interest, they are often tested on problems where the prior distribution is analytically unknown, making it difficult to assess their performance in providing rigorous uncertainty quantification. Motivated by this challenge, this work introduces three benchmark problems for evaluating the performance of diffusion model b

SUBMITTER: Crafts ES 

PROVIDER: S-EPMC12456369 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature