# Non-asymptotic bounds for sampling algorithms without log-concavity

21 Aug 2018Mateusz B. MajkaAleksandar MijatovićLukasz Szpruch

Discrete time analogues of ergodic stochastic differential equations (SDEs) are one of the most popular and flexible tools for sampling high-dimensional probability measures. Non-asymptotic analysis in the $L^2$ Wasserstein distance of sampling algorithms based on Euler discretisations of SDEs has been recently developed by several authors for log-concave probability distributions... (read more)

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