celerite2

celerite is an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension and this library, celerite2 is a re-write of the original celerite project to improve numerical stability and integration with various machine learning frameworks. This implementation includes interfaces in Python and C++, with full support for PyMC (v3 and v5+) and JAX.

This documentation won’t teach you the fundamentals of GP modeling but the best resource for learning about this is available for free online: Rasmussen & Williams (2006). Similarly, the celerite algorithm is restricted to a specific class of covariance functions (see the original paper for more information and a recent generalization for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, GPyTorch or tinygp might be good choices.

celerite2 is being actively developed in a public repository on GitHub so if you have any trouble, open an issue there.

User Guide

• Installation
  • Using pip
  • From Source
  • Testing
• Upgrading from celerite
  • Why does this even exist?
  • What interfaces are available in celerite2?
  • A simple example
• Citing celerite2

Tutorials

• Getting started
  • Maximum likelihood
  • Posterior inference using emcee
  • Posterior inference using PyMC
  • Posterior inference using numpyro
  • Comparison

API Details

• Python interface
  • GaussianProcess
  • Model building
• PyMC3 interface
  • GaussianProcess
  • PyMC3 support
• PyMC (v5+) interface
  • GaussianProcess
  • PyMC (v5+) support
• JAX interface
  • GaussianProcess
  • numpyro support
• C++ interface
  • Basic interface
  • Autodiff Interface
  • Terms

License & attribution

Copyright 2020-2022 Daniel Foreman-Mackey.

The source code is made available under the terms of the MIT license.

If you make use of this code, please cite the relevant papers: Citing celerite2

Changelog