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 for pytorch.
PYSGMCMC¶
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 pytorch that provides:
- efficient numerical computation via data flow graphs
- flexible computation environments (CPU/GPU support, desktop/server/mobile device support)
- Linear algebra operations