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Parallel tempering

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papersTODAY 04:00 UTC

IsingFormer: Learned Proposals Augment Parallel Tempering for MCMC

A new arXiv paper proposes adding a global proposal move to parallel tempering, in which a learned model suggests state changes across finite temperatures. The aim is to improve mixing in Markov chain Monte Carlo sampling and optimization, where generative models have shown promise but remain difficult to integrate with standard MCMC. The authors present this as a way to combine learned proposals with established tempering methods rather than replace them.

mcmc/sampling/generative-models/research