AI RESEARCH
Accelerated Parallel Tempering via Neural Transports
arXiv CS.LG
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ArXi:2502.10328v4 Announce Type: replace-cross Marko Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeting high-dimensional, multimodal, or complex target distributions. Parallel Tempering (PT) enhances MCMC's sample efficiency through annealing and parallel computation, propagating samples from tractable reference distributions to intractable targets via state swapping across interpolating distributions.