sgmcmc: Stochastic Gradient Markov Chain Monte Carlo
Version 0.2.0

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. References: M. Welling, Y. W. Teh (2011) ; T. Chen, E. B. Fox, C. E. Guestrin (2014) ; N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, H. Neven (2014) ; J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth (2017) .

Package details

AuthorJack Baker [aut, cre, cph], Christopher Nemeth [aut, cph], Paul Fearnhead [aut, cph], Emily B. Fox [aut, cph], STOR-i [cph]
Date of publication2017-09-26 17:14:03 UTC
MaintainerJack Baker <[email protected]>
LicenseGPL-3
Version0.2.0
URL https://github.com/STOR-i/sgmcmc
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("sgmcmc")

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sgmcmc documentation built on Sept. 27, 2017, 1:02 a.m.