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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: pdf
DOI: https://doi.org/10.1016/j.cpc.2023.109059
Accepted Manuscript: 1-s2.0-S0010465523004046-main.pdf
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