Orateur
Pierre JACOB
(ESSEC Business School)
Description
How to parallelize computation and how to diagnose convergence remain largely open questions regarding MCMC. Since Glynn & Rhee (Journal of Applied Probability, Vol. 51A, 2014), various advances based on couplings of MCMC algorithms have been proposed. The key is the design of coupled chains that, if properly constructed, can be employed to construct estimators that do not suffer from the usual "burn-in" or initialization bias. These methods are distinct from coupling from the past, and appear much more widely applicable, but do not yield perfect samples. This presentation provides an overview of this family of techniques and recent developments.
