Quickstart | Install guide | Documentation | Slack Community

GPJax aims to provide a low-level interface to Gaussian process (GP) models in Jax, structured to give researchers maximum flexibility in extending the code to suit their own needs. The idea is that the code should be as close as possible to the maths we write on paper when working with GP models.

Package organisation

Contributions

We would be delighted to receive contributions from interested individuals and groups. To learn how you can get involved, please read our guide for contributing. If you have any questions, we encourage you to open an issue. For broader conversations, such as best GP fitting practices or questions about the mathematics of GPs, we invite you to open a discussion.

Another way you can contribute to GPJax is through issue triaging. This can include reproducing bug reports, asking for vital information such as version numbers and reproduction instructions, or identifying stale issues. To get started, browse the open issues.

As a contributor to GPJax, you are expected to abide by our code of conduct. If you feel that you have either experienced or witnessed behaviour that violates this standard, then we ask that you report any such behaviours through this form or reach out to one of the project's gardeners.

Feel free to join our Slack Channel, where we can discuss the development of GPJax and broader support for Gaussian process modelling.

We appreciate all the contributors to GPJax who have helped to shape GPJax into the package it is today.

Supported methods and interfaces

Notebook examples

Getting started

• New to Gaussian Processes?
• Introduction to Kernels
• Regression
• Classification
• Count Data Regression
• Natural Gradients

Accelerating Gaussian processes

• Sparse Gaussian Process Regression
• Sparse Stochastic Variational Inference
• Natural Gradients in Practice
• Dual Parameterisation of Sparse GPs (t-SVGP)
• State-Space (Markovian) Gaussian Processes
• Scalable Multi-Output GPs with OILMM

Applied modelling

• Gaussian Processes Barycentres
• Graph Kernels
• Heteroscedastic Inference
• Multi-Output Gaussian Processes
• Orthogonal Additive Kernels
• Gaussian Processes for Vector Fields and Ocean Current Modelling
• Spatial Modelling with Composable Gaussian Processes
• Gridded Data with xarray
• UCI Data Benchmarking

Guides for customisation

• Kernel Guide
• Likelihood Guide
• Deep Kernel Learning
• Joint Inference with Numpyro
• Backend Module Design

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