Pre-meeting (DeMO) at 3 pm
Presenters: Chris Sherlock and Pierre Jacob

Chair: Ioanna Manolopoulou

Discussion paper at 5 pm

Speakers: Pierre E. Jacob and John O’Leary (Harvard University, Cambridge) and Yves F. Atchadé (Boston University)

Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to 1. MCMC estimators are generally biased after any fixed number of iterations. We propose to remove this bias by using couplings of Markov chains together with a telescopic sum argument of Glynn and Rhee. The resulting unbiased estimators can be computed independently in parallel. We discuss practical couplings for popular MCMC algorithms. We establish the theoretical validity of the estimators proposed and study their efficiency relative to the underlying MCMC algorithms. Finally, we illustrate the performance and limitations of the method on toy examples, on an Ising model around its critical temperature, on a high dimensional variable-selection problem, and on an approximation of the cut distribution arising in Bayesian inference for models made of multiple modules.

Preprint discussion papers available here

This event will be followed by a drinks reception     

Registration alongside tea and coffee from 4.30 pm.

Attendance is free and open to all, whether fellows of the RSS or not, but pre-registration is required.