SOURCE-LINKED INTELLIGENCE
Exact Global MCMC with Denoising Diffusion
This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional target densities. The method is motivated by the observation that sequentially applying a forward and reverse diffusion process defines a Markov chain with a target stationary distribution for an ideal denoiser trained on samples of the target distribution. This observation can be made exact for any denoiser by applying a Metropolis-Hastings step whose acceptance ratio includes the density of the forward and reverse paths of a discrete time SDE app
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T19:24:36.000Z
First collected: 2026-09-21T06:21:59.299Z. This is not the publication date.