讲座内容：

Generating samples from a high dimensional probability distribution is a fundamental task with wide-ranging applications in the area of scientific computing, statistics and machine learning. This talk will focus on high dimensional sampling algorithms based on time discretizations of stochastic differential equations (SDEs). New error bounds will be then provided for the considered sampling algorithms without log-concavity, where the convergence rate and the dimension dependence are explicitly revealed. Numerical experiments will be finally presented to corroborate the theoretical findings.