Stochastic gradient Markov chain Monte Carlo

Nemeth, Christopher and Fearnhead, Paul (2019) Stochastic gradient Markov chain Monte Carlo. arxiv.org.

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Abstract

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing exact inference requires all of the data to be processed at each iteration of the algorithm. For large data sets, the computational cost of MCMC can be prohibitive, which has led to recent developments in scalable Monte Carlo algorithms that have a significantly lower computational cost than standard MCMC. In this paper, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC) which utilises data subsampling techniques to reduce the per-iteration cost of MCMC. We provide an introduction to some popular SGMCMC algorithms and review the supporting theoretical results, as well as comparing the efficiency of SGMCMC algorithms against MCMC on benchmark examples. The supporting R code is available online.

Item Type:
Journal Article
Journal or Publication Title:
arxiv.org
Subjects:
ID Code:
136519
Deposited By:
Deposited On:
04 Sep 2019 12:20
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Jul 2020 08:29