PYSGMCMC – Stochastic Gradient Markov Chain Monte Carlo Sampling

This package provides out-of-the-box implementations of various state-of-the-art Stochastic Gradient Markov Chain Monte Carlo sampling methods.

PYSGMCMC

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

Features

  • 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

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