2025 Python Packaging Survey is now live!  Take the survey now

Fast and scalable Gaussian Processes in 1D

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for danfm from gravatar.com danfm

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers
Report project as malware

Project description

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. Documentation for this version can be found here. This new implementation includes interfaces in Python and C++, with full support for PyMC (v3 and v4) 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 might be a good choice.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page