Non-asymptotic error bounds for scaled underdamped Langevin MCMC

Zajic, Tim

arXiv.org Machine Learning 

Recent works have derived non - asymptotic upper bounds for convergence of underdamped Langevin MCMC. We revisit these bound and consider introducing scaling terms in the underlying underdamped Langevin equation. In particular, we provide conditions under which an appropria te scaling allows to improve the error bounds in terms of the condition number of the underlying density of interest.

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