Double-Parallel Monte Carlo for Bayesian Analysis of Big Data

This paper proposes a simple, practical and efficient MCMC algorithm for Bayesian analysis of big data. The proposed algorithm suggests to divide the big dataset into some smaller subsets and provides a simple method to aggregate the subset posteriors to approximate the full data posterior. To further speed up computation, the proposed algorithm employs the population stochastic approximation Monte Carlo (Pop-SAMC) algorithm, a parallel MCMC algorithm, to simulate from each subset posterior. Since this algorithm consists of two levels of parallel, data parallel and simulation parallel, it is coined as â Double Parallel Monte Carloâ . The validity of the proposed algorithm is justified mathematically and numerically. ; Non UBC ; Unreviewed ; Author affiliation: University of Florida ; Graduate