Scalable MetropolisHastings for Exact Bayesian Inference with Large Datasets
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Proceedings of the 36th International Conference on Machine Learning, PMLR 97:13511360, 2019.
Abstract
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods such as MetropolisHastings is too computationally intensive to handle large datasets, since the cost per step usually scales like $O(n)$ in the number of data points $n$. We propose the Scalable MetropolisHastings (SMH) kernel that only requires processing on average $O(1)$ or even $O(1/\sqrt{n})$ data points per step. This scheme is based on a combination of factorized acceptance probabilities, procedures for fast simulation of Bernoulli processes, and control variate ideas. Contrary to many MCMC subsampling schemes such as fixed stepsize Stochastic Gradient Langevin Dynamics, our approach is exact insofar as the invariant distribution is the true posterior and not an approximation to it. We characterise the performance of our algorithm theoretically, and give realistic and verifiable conditions under which it is geometrically ergodic. This theory is borne out by empirical results that demonstrate overall performance benefits over standard MetropolisHastings and various subsampling algorithms.
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