We present JaxSGMC, an application-agnostic library for stochastic gradient Markov chain Monte Carlo (SG-MCMC) in JAX. SG-MCMC schemes are uncertainty quantification (UQ) methods that scale to large datasets and high-dimensional models, enabling trustworthy neural network predictions via Bayesian deep learning. JaxSGMC implements several state-of-the-art SG-MCMC samplers to promote UQ in deep learning by reducing the barriers of entry for switching from stochastic optimization to SG-MCMC sampling. Additionally, JaxSGMC allows users to build custom samplers from standard SG-MCMC building blocks. Due to this modular structure, we anticipate that JaxSGMC will accelerate research into novel SG-MCMC schemes and facilitate their application across a broad range of domains.
View on arXiv@article{thaler2025_2505.11190,
title={ JaxSGMC: Modular stochastic gradient MCMC in JAX },
author={ Stephan Thaler and Paul Fuchs and Ana Cukarska and Julija Zavadlav },
journal={arXiv preprint arXiv:2505.11190},
year={ 2025 }
}