Markov Chain Monte Carlo with Diffusion Paths

Jul 14, 2026

Researchers introduce MAD-Path, a new Markov Chain Monte Carlo (MCMC) method that samples from multimodal distributions by interpolating along diffusion paths rather than using traditional tempering. This approach preserves the relative weights of modes and improves mixing, with a Metropolis adjustment ensuring the target distribution remains invariant even with approximate intermediate scores. Experimental results demonstrate that MAD-Path achieves better exploration and more accurate mode-weight estimation compared to tempering-based MCMC methods.

Why it matters: MAD-Path addresses a longstanding challenge in statistical inference by providing a more reliable and principled method for sampling from complex, multimodal Bayesian posteriors.

Full story at: arXiv Statistical ML ↗