SGMCMC Functions for running standard SGMCMC algorithms
sgld	Stochastic Gradient Langevin Dynamics
sghmc	Stochastic Gradient Hamiltonian Monte Carlo
sgnht	Stochastic Gradient Nose Hoover Thermostat
SGMCMC with Control Variates Functions for running SGMCMC algorithms with improved efficiency using control variates
sgldcv	Stochastic Gradient Langevin Dynamics with Control Variates
sghmccv	Stochastic Gradient Hamiltonian Monte Carlo with Control Variates
sgnhtcv	Stochastic Gradient Nose Hoover Thermostat with Control Variates
Run SGMCMC algorithms step by step Functions to run SGMCMC algorithms step by step within a user-defined loop, similar to standard TensorFlow optimization methods. Useful for chains with a high storage cost
sgldSetup	Create an sgld object
sgldcvSetup	Create an sgldcv object
sghmcSetup	Create an sghmc object
sghmccvSetup	Create an sghmccv object
sgnhtSetup	Create an sgnht object
sgnhtcvSetup	Create an sgnhtcv object
initSess	Initialise TensorFlow session and sgmcmc algorithm
sgmcmcStep	Single step of sgmcmc
getParams	Get current parameter values
Installation Install python packages depended on by sgmcmc, TensorFlow and TensorFlow Probability.
installTF	Install TensorFlow and TensorFlow Probability
Datasets Download and load datasets used in the examples and vignettes
getDataset	Load example datasets