User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient

29 Sep 2017Arnak S. DalalyanAvetik G. Karagulyan

In this paper, we study the problem of sampling from a given probability density function that is known to be smooth and strongly log-concave. We analyze several methods of approximate sampling based on discretizations of the (highly overdamped) Langevin diffusion and establish guarantees on its error measured in the Wasserstein-2 distance... (read more)

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