Local-Global MCMC kernels: the best of both worlds. (arXiv:2111.02702v3 [stat.ML] UPDATED)

Recent works leveraging learning to enhance sampling have shown promising
results, in particular by designing effective non-local moves and global
proposals. However, learning accuracy is inevitably limited in regions where
little data is available such as in the tails of distributions as well as in
high-dimensional problems. In the present paper we study an Explore-Exploit
Markov chain Monte Carlo strategy ($Ex^2MCMC$) that combines local and global
samplers showing that it enjoys the advantages of both approaches. We prove
$V$-uniform geometric …

arxiv global mcmc