Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth
Title: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
Abstract: This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large data sets using stochastic gradient Markov chain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings, are known to run prohibitively slowly as the data set size increases. SGMCMC solves this issue by only using a subset of data at each iteration. SGMCMC requires calculating gradients of the log-likelihood and log-priors, which can be time consuming and error prone to perform by hand. The sgmcmc package calculates these gradients itself using automatic differentiation, making the implementation of these methods much easier. To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework. SGMCMC has become widely adopted in the machine learning literature, but less so in the statistics community. We believe this may be partly due to lack of software; this package aims to bridge this gap.

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Paper: sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo     Download PDF (Downloads: 256)
Supplements:
sgmcmc_0.2.5.tar.gz: R source package Download (Downloads: 19; 89KB)
v91i03-replication.zip: Replication materials Download (Downloads: 16; 30MB)

DOI: 10.18637/jss.v091.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.