SGMCMCJax:a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms

Coullon, Jeremie and Nemeth, Christopher (2022) SGMCMCJax:a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms. Journal of Open Source Software, 7 (72). ISSN 2475-9066

Full text not available from this repository.


In Bayesian inference, the posterior distribution is the probability distribution over the model parameters resulting from the prior distribution and the likelihood. One can compute integrals over this distribution to obtain quantities of interest, such as the posterior mean and variance, or credible uncertainty regions. However, as these integrals are often intractable for problems of interest they require numerical methods to approximate them. Markov Chain Monte Carlo (MCMC) is currently the gold standard for approximating integrals needed in Bayesian inference. However, as these algorithms become prohibitively expensive for large datasets, stochastic gradient MCMC (SGMCMC) (Ma et al., 2015; Nemeth & Fearnhead, 2021) is a popular approach to approximate these integrals in these cases. This class of scalable algorithms uses data subsampling techniques to approximate gradient based sampling algorithms, and are regularly used to fit statistical models or Bayesian neural networks (BNNs). The SGMCMC literature develops new algorithms by finding novel gradient estimation techniques, designing more efficient diffusions, and finding more stable numerical discretisations to these diffusions. SGMCMCJax is a lightweight library that is designed to allow the user to innovate along these lines or use one of the existing gradient-based SGMCMC algorithms already included in the library. This makes SGMCMCJax very well suited for both research purposes and practical applications.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Open Source Software
ID Code:
Deposited By:
Deposited On:
20 Apr 2022 13:45
Last Modified:
21 Sep 2023 03:15