celerite¶
A scalable method for Gaussian Process regression. From French célérité.
celerite is a library for fast and scalable Gaussian Process (GP) Regression in one dimension with implementations in C++, Python, and Julia. The Python implementation is the most stable and it exposes the most features but it relies on the C++ implementation for computational efficiency. 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).
The celerite API is designed to be familiar to users of george and, like george, celerite is designed to efficiently evaluate the marginalized likelihood of a dataset under a GP model. This is then meant to be used alongside your favorite non-linear optimization or posterior inference library for the best results.
celerite is being actively developed in a public repository on GitHub so if you have any trouble, open an issue there.
Contributors¶
celerite is being developed by Dan Foreman-Mackey (@dfm) and Eric Agol (@EricAgol) with contributions from:
License & Attribution¶
Copyright 2016, 2017, Daniel Foreman-Mackey, Eric Agol and contributors.
The source code is made available under the terms of the MIT license.
If you make use of this code, please cite the following papers:
@article{genrp,
author = {Sivaram Ambikasaran},
title = {Generalized Rybicki Press algorithm},
year = {2015},
journal = {Numer. Linear Algebra Appl.},
volume = {22},
number = {6},
pages = {1102--1114},
doi = {10.1002/nla.2003},
url = {https://arxiv.org/abs/1409.7852}
}