Seminar: "Skew-symmetric sampling schemes and locally balancing algorithms"

AUEB STATISTICS SEMINAR SERIES 2023-2024

Georgios Vasdekis, University College London

Skew-symmetric sampling schemes and locally balancing algorithms

Room Troias Amphitheater

ABSTRACT

Locally balancing algorithms are a new class of MCMC algorithms, recently introduced in (Livingstone and Zanella, 2022). One of these algorithms, the Barker algorithm, has been shown to be robust to heteroskedasticity of the posterior target and the step size of the algorithm. At the same time, the algorithm seems to preserve high dimensional properties of state of the art MCMC, making it an interesting alternative to the existing literature. In the first part of the talk we will review the main results on these locally balancing algorithms. It turns out that in order to sample from the Barker algorithm, one can use ideas of sampling from skew-symmetric distributions. We will transfer these ideas in the context of discretising and simulating from diffusion processes and we will suggest a new class of unadjusted MCMC algorithms, which are robust with respect to the step size.

This is joint work with S. Livingstone, N. Nusken and R. Zhang.