  This paper introduces the R package sgmcmc; which can be used for Bayesian
inference on problems with large datasets 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 dataset 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.
