Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers

Evan Scope Crafts

doi:10.1109/OJSP.2025.3597867

Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers

2025-01-01

0

OA

AI

DOI：10.1109/OJSP.2025.3597867

Original

Save

Abstract

En 中文

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 based samplers. The benchmark problems, which are inspired by problems in image inpainting, x-ray tomography, and phase retrieval, have a posterior density that is analytically known. In this setting, approximate ground-truth posterior samples can be obtained, enabling principled evaluation of the performance of posterior sampling algorithms. This work also introduces a general framework for diffusion model based posterior sampling, Bayesian Inverse Problem Solvers through Diffusion Annealing (BIPSDA). This framework unifies several recently proposed diffusion-model-based posterior sampling algorithms and contains novel algorithms that can be realized through flexible combinations of design choices. We tested the performance of a set of BIPSDA algorithms, including previously proposed state-of-the-art approaches, on the proposed benchmark problems. The results provide insight into the strengths and limitations of existing diffusion-model based posterior samplers, while the benchmark problems provide a testing ground for future algorithmic developments.

Keywords:

Bayesian inference

diffusion models

generative AI

optimization

machine learning

posterior probability

uncertainty quantification

Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers

Abstract

Journal

Organization

Cited Papers