Publications
Publication Information
Title | Accelerating Markov Chain Monte Carlo sampling with diffusion models |
Authors | N. Hunt-Smith, W. Melnitchouk, F. Ringer, N. Sato, A. Thomas, M. J. White |
JLAB number | JLAB-THY-23-3900 |
LANL number | (None) |
Other number | DOE/OR/23177-7034 |
Document Type(s) | (Journal Article) |
Associated with EIC: | Yes |
Supported by Jefferson Lab LDRD Funding: | No |
Funding Source: | Nuclear Physics (NP) |
Journal Compiled for Computer Physics Communication Volume 296 Page(s) 109059 Refereed | |
Publication Abstract: | Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion. |
Experiment Numbers: | other |
Group: | THEORY CENTER |
Document: | |
DOI: | https://doi.org/10.1016/j.cpc.2023.109059 |
Accepted Manuscript: | 1-s2.0-S0010465523004046-main.pdf |
Supporting Documents: | |
Supporting Datasets: |