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MIA Talks

Large-scale Bayesian inference for GWAS with coupled Markov chain Monte Carlo

December 9, 2020
Dept. of Statistics, Harvard University

This talk will cover Markov chain Monte Carlo (MCMC) methods with “couplings” as a tool in large-scale Bayesian computation. Determining the burn-in period of MCMC is a recurring concern for practitioners of Bayesian inference in settings such as GWAS. We explain how couplings can diagnose convergence and give practical burn-in guidance for MCMC algorithms. The first half of the talk will focus on a simpler, pedagogical example. The second half will introduce coupled MCMC methods for high-dimensional Bayesian regression. We will conclude with a surprise: even in GWAS settings, with dimension ~100000, our MCMC algorithms can converge in only ~750 steps.