The sgmcmc package implements some of the most popular stochastic gradient MCMC methods including SGLD, SGHMC, SGNHT. It also implements control variates as a way to increase the efficiency of these methods. The algorithms are implemented using TensorFlow which means no gradients need to be specified by the user as these are calculated automatically. It also means the algorithms are efficient.
The main functions of the package are sgld, sghmc and sgnht which implement the methods stochastic gradient Langevin dynamics, stochastic gradient Hamiltonian Monte Carlo and stochastic gradient Nose-Hoover Thermostat respectively. Also included are control variate versions of these algorithms, which uses control variates to increase their efficiency. These are the functions sgldcv, sghmccv and sgnhtcv.
Baker, J., Fearnhead, P., Fox, E. B., & Nemeth, C. (2017) control variates for stochastic gradient Langevin dynamics. Preprint.
Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. ICML (pp. 681-688).
Chen, T., Fox, E. B., & Guestrin, C. (2014). stochastic gradient Hamiltonian Monte Carlo. In ICML (pp. 1683-1691).
Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D., & Neven, H. (2014). Bayesian sampling using stochastic gradient thermostats. NIPS (pp. 3203-3211).
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