Control variates for stochastic gradient MCMC

Baker, Jack and Fearnhead, Paul and Fox, Emily B. and Nemeth, Christopher (2019) Control variates for stochastic gradient MCMC. Statistics and Computing, 29 (3). pp. 599-615. ISSN 0960-3174

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It is well known that Markov chain Monte Carlo (MCMC) methods scale poorly with dataset size. A popular class of methods for solving this issue is stochastic gradient MCMC (SGMCMC). These methods use a noisy estimate of the gradient of the log-posterior, which reduces the per iteration computational cost of the algorithm. Despite this, there are a number of results suggesting that stochastic gradient Langevin dynamics (SGLD), probably the most popular of these methods, still has computational cost proportional to the dataset size. We suggest an alternative log-posterior gradient estimate for stochastic gradient MCMC which uses control variates to reduce the variance. We analyse SGLD using this gradient estimate, and show that, under log-concavity assumptions on the target distribution, the computational cost required for a given level of accuracy is independent of the dataset size. Next we show that a different control variate technique, known as zero variance control variates, can be applied to SGMCMC algorithms for free. This post-processing step improves the inference of the algorithm by reducing the variance of the MCMC output. Zero variance control variates rely on the gradient of the log-posterior; we explore how the variance reduction is affected by replacing this with the noisy gradient estimate calculated by SGMCMC.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
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Uncontrolled Keywords:
?? stat.cocs.lgstat.mlcomputational theory and mathematicstheoretical computer sciencestatistics and probabilitystatistics, probability and uncertainty ??
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Deposited On:
26 Jun 2017 13:46
Last Modified:
15 Jul 2024 17:03