文章基本信息

标题：sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
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作者：Jack Baker ; Paul Fearnhead ; Emily B. Fox 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：91
期号：1
页码：1-27
DOI：10.18637/jss.v091.i03
出版社：University of California, Los Angeles
摘要：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.
关键词：R; stochastic gradient Markov chain Monte Carlo; big data; MCMC; stochastic gradient Langevin dynamics; stochastic gradient Hamiltonian Monte Carlo; stochastic gradient
其他关键词：R;stochastic gradient Markov chain Monte Carlo;big data;MCMC;stochastic gradient Langevin dynamics;stochastic gradient Hamiltonian Monte Carlo;stochastic gradient Nos'e-Hoover thermostat