Seminar by Grigoris Pavliotis - Accelerating convergence and reducing variance for Langevin samplers   17 June 2015

14:00 - 16:00


Grigoris Pavliotis, Imperial College

Markov Chain Monte Carlo (MCMC) is a standard methodology for sampling from probability distributions (known up to the normalization constant) in high dimensions. There are (infinitely) many different Markov chains/diffusion processes that can be used to sample from a given distribution. To reduce the computational complexity, it is necessary to consider Markov chains that converge as quickly as possible to theĀ  target distribution and that have a small asymptotic variance. In this talk I will present some recent results on accelerating convergence to equilibrium and on reducing the asymptotic variance for a class of Langevin-based MCMC algorithms.

Name University Dates of visit
Grigoris Pavliotis Imperial College 17/06/2015 - 17/06/2015
Total Guests : 1
Name University Dates of visit
Total Guests : 0