Software

GPJax

GPJax is a didactic Gaussian process package written entirely in Jax that is targetted at researchers wishing to develop their own custom Gaussian process models. The code is compatible with GPUs and TPUs and boasts favourable runtimes in comparison to other Python-based GP packages. In addition to regular GP regression, GPJax also supports inference in non-conjugate models and induces scalable approximations through sparse schemes. Custom GP modelling for graphs and Wasserstein barycentres is also supported.

See Github for the code repository and JOSS for the supporting paper.

GaussianProcesses.jl

This package provides functionality for Gaussian process models in the Julia programming language. We make use of core features of Julia: multiple dispatch and JIT compilation. This results in a highly intuitive API with exceptionally efficient computation. At current we enable inference in a broad range of models by supporting a large number of likelihood and kernel functions. Further, through implemented Markov Chain Monte-Carlo (MCMC) schemes such as Hamiltonian Monte-Carlo and elliptical slice sampling, we are able to support fully Bayesian inference. Finally, implementations of several sparsity inducing schemes allows for GP modelling to be conducted on big data problems.

See Github for the code repository and arXiv for the supporting paper.