Bayes Reading Group: Charles Margossian (CCM)

Friday Feb 23, 2024, 11:00 AM → 12:00 PM America/New_York

3rd Floor Conference Room/3-Flatiron Institute (162 5th Avenue)

Discussion Lead: Charles Margossian [CCM]

Topics: Biswas et al.2019. Estimating Convergence of Markov chains with L-lag couplings

Link:https://proceedings.neurips.cc/paper_files/paper/2019/file/aec851e565646f6835e915293381e20a-Paper.pdf

Abstract: Markov chain Monte Carlo (MCMC) methods generate samples that are asymptotically distributed from a target distribution of interest as the number of iterations goes to infinity. Various theoretical results provide upper bounds on the distance between the target and marginal distribution after a fixed number of iterations. These upper bounds are on a case by case basis and typically involve intractable quantities, which limits their use for practitioners. We introduce L-lag couplings to generate computable, non-asymptotic upper bound estimates for the total variation or the Wasserstein distance of general Markov chains. We apply L-lag couplings to the tasks of (i) determining MCMC burn-in, (ii) comparing different MCMC algorithms with the same target, and (iii) comparing exact and approximate MCMC. Lastly, we (iv) assess the bias of sequential Monte Carlo and self-normalized importance samplers.