Wednesday 11 March 2020, 3:30 PM – 4:30 PM

A talk by Pierre Jacob, Associate Professor of Statistics at Harvard University

Various tasks in statistics involve numerical integration, for which Markov chain Monte Carlo (MCMC) methods are state-of-the-art. MCMC methods yield estimators that converge to integrals of interest in the limit of the number of iterations. This iterative asymptotic justification is not ideal; first, it stands at odds with current trends in computing hardware, with increasingly parallel architectures; secondly, the choice of “burn-in” or “warm-up” is arduous.

This talk will describe recently proposed estimators that are unbiased for the expectations of interest while having a finite computing cost and a finite variance. They can thus be generated independently in parallel and averaged over. The method also provides practical upper bounds on the distance (e.g. total variation) between the marginal distribution of the chain at a finite step and its invariant distribution. The key idea is to generate “faithful” couplings of Markov chains, whereby pairs of chains coalesce after a random number of iterations.

This talk will provide an overview of this line of research, joint work with John O’Leary, Yves Atchadé and many others.

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The Jean Golding Institute has teamed up with the Heilbronn Institute for Mathematical Research to showcase the latest research in Data Science – methodology with roots in Mathematics and Computer Science with important applied implications.  

The series will feature a range of internationally regarded high-profile speakers on topics that will be relevant to a broad audience.