STI Publications - View Publication Form #19360
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Publication Information
| Title | Accelerating Markov Chain Monte Carlo sampling with diffusion models | ||||
| 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 fun | ||||
| Author(s) | N. Hunt-Smith, Wally Melnitchouk, F. Ringer, N. Sato, A. Thomas, M. J. White | ||||
| Publication Date | March 2024 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | University of Adelaide, Adelaide, Australia | ||||
| Affiliation | Theory & Comp Physics / THEORY CENTER / THEORY CENTER | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | Yes | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | Computer Physics Communication |
| Refereed | Yes |
| Volume | 296 |
| Issue | |
| Page(s) | 109059 |
Attachments/Datasets/DOI Link
| Document(s) |
Diffusion_Model_Sampling.pdf
(STI Document)
1-s2.0-S0010465523004046-main.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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