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On Cyclical MCMC Sampling
March 4, 2024, 5:43 a.m. | Liwei Wang, Xinru Liu, Aaron Smith, Yves Atchade
stat.ML updates on arXiv.org arxiv.org
Abstract: Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning. The algorithm works by generating a nonhomogeneous Markov chain that tracks -- cyclically in time -- tempered versions of the target distribution. We show in this work that cyclical MCMC converges to the desired probability distribution in settings where the Markov kernels used are fast …
abstract algorithm arxiv challenge deep learning framework markov mcmc multimodal novel posterior sampling stat.co stat.ml the algorithm type versions
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