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' <https://www.tensorflow.org/>, 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) <http://www.icml-2011.org/papers/398_icmlpaper.pdf>; T. Chen, E. B. Fox, C. E. Guestrin (2014) <arXiv:1402.4102>; N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, H. Neven (2014) <https://papers.nips.cc/paper/5592-bayesian-sampling-using-stochastic-gradient-thermostats>; J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth (2017) <arXiv:1706.05439>. For more details see <doi:10.18637/jss.v091.i03>.
| Version: | 0.2.5 |
| Depends: | R (≥ 3.0), tensorflow |
| Imports: | utils, reticulate |
| Suggests: | testthat, MASS, knitr, ggplot2, rmarkdown |
| Published: | 2019-10-24 |
| Author: | Jack Baker [aut, cre, cph], Christopher Nemeth [aut, cph], Paul Fearnhead [aut, cph], Emily B. Fox [aut, cph], STOR-i [cph] |
| Maintainer: | Jack Baker <jackbaker92 at mail.com> |
| BugReports: | https://github.com/STOR-i/sgmcmc/issues |
| License: | GPL-3 |
| URL: | https://github.com/STOR-i/sgmcmc |
| NeedsCompilation: | no |
| SystemRequirements: | TensorFlow (https://www.tensorflow.org/), TensorFlow Probability (https://www.tensorflow.org/probability/) |
| Citation: | sgmcmc citation info |
| Materials: | README NEWS |
| CRAN checks: | sgmcmc results |
| Reference manual: | sgmcmc.pdf |
| Vignettes: |
Gaussian Mixture Logistic Regression Multivariate Gaussian Bayesian Neural Network Getting Started |
| Package source: | sgmcmc_0.2.5.tar.gz |
| Windows binaries: | r-devel: sgmcmc_0.2.5.zip, r-release: sgmcmc_0.2.5.zip, r-oldrel: sgmcmc_0.2.5.zip |
| macOS binaries: | r-release: sgmcmc_0.2.5.tgz, r-oldrel: sgmcmc_0.2.5.tgz |
| Old sources: | sgmcmc archive |
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