User-friendly error bounds for sampling from a strongly log-concave density

We will present new bounds on the sampling error in the case where the target distribution has a smooth and log-concave density. These bounds are established for the Langevin Monte Carlo and its discretized versions involving the Hessian matrix of the log-density. We will also discuss the case where accurate evaluation of the gradient is impossible.