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Getting started

Installation
Design Principles
The Sharp Bits
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

Reference

API Reference
- Dataset
- xarray
- Distributions
- Gaussian Processes
- Kernels
- Likelihoods
- Mean Functions
- Parameters
- Objectives
- Fitting
- Variational Families
- Models
- State-Space GPs
- Linear Algebra
- Integrators
- Scan
- Summary
- Typing
- Citation
Glossary

Migrations

Migrations

Project

Contributing
GPJax Governance Document
Code of Conduct
References

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