Supplementary Data for "“Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems?”

This dataset contains approximate posterior samples from algorithms in the Bayesian Inverse Problem Solvers through Diffusion Annealing (BIPSDA) framework (code at: https://doi.org/10.5281/zenodo.14908137). In particular, four HDF5 files corresponding to the stylized inpainting studies (low and high noise regimes), stylized x-ray tomography study, and stylized phase retrieval study are provided. Each file contains 18 datasets. These include datasets containing the ground truth values of the parameter of interest, the observed measurements, and the reference ground truth posterior samples for each of the 100 posterior trials we conducted. The remaining 15 datasets correspond to the BIPSDA algorithm runs; the naming convention is the BIPSDA algorithm name followed by an option that indicates whether the analytic or learned prior score was used in the algorithm run. In each dataset, the leading dimension corresponds to the posterior sampling trial number.