Stochastic Gradient Markov Chain Monte Carlo

Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using 'TensorFlow' <>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency.


Reference manual

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0.1.0 by Jack Baker, 2 months ago

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Authors: Jack Baker [aut, cre, cph], Christopher Nemeth [aut, cph], Paul Fearnhead [aut, cph], Emily B. Fox [aut, cph], STOR-i [cph]

Documentation:   PDF Manual  

GPL-3 license

Depends on tensorflow

Suggests testthat, MASS, knitr, ggplot2, rmarkdown

System requirements: TensorFlow (

See at CRAN