PYSGMCMC is a Python framework for Bayesian Deep Learning that focuses on Stochastic Gradient Markov Chain Monte Carlo methods.

• Complex samplers as black boxes, computing the next sample with corresponding costs of any MCMC sampler is as easy as:

sample, cost = next(sampler)

• Based on tensorflow that provides:

  • efficient numerical computation via data flow graphs
  • flexible computation environments (CPU/GPU support, desktop/server/mobile device support)
  • Linear algebra operations

The quick way:

pip3 install git+https://github.com/MFreidank/pysgmcmc

Our documentation can be found at http://pysgmcmc.readthedocs.io/en/latest/.