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GPJax
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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
  • UCI Data Benchmarking

Guides for customisation

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

Reference

  • API Reference
    • Dataset
    • 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
  1. GPJax /
  2. API Reference /
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