Optimal Langevin Samplers (Grigorios A. Pavliotis, Imperial College London)

15.03.2019 11:00

Sampling from a probability distribution in a high dimensional spaces is a standard problem in computational statistical mechanics, Bayesian statistics and other applications. A standard approach for doing this is by constructing an appropriate Markov process that is ergodic with respect to the measure from which we wish to sample. In this talk we will present a class of sampling schemes based on Langevin-type stochastic differential equations. We will show, in particular, nonreversible Langevin samplers, i.e. stochastic dynamics that do not satisfy detailed balance, have, in general, better properties than their reversible counterparts, in the sense of accelerating convergence to equilibrium and of reducing the asymptotic variance. Numerical schemes for such nonreversible samplers will be discussed and the connection with nonequilibrium statistical mechanics will be made.


Room 17, Séminaire d'analyse numérique

Organisé par

Section de mathématiques


Grigorios A. Pavliotis , Imperial College London

entrée libre


Catégorie: Séminaire

Mots clés: analyse numérique