Metadata-Version: 2.1
Name: celerite2
Version: 0.3.0
Summary: Fast and scalable Gaussian Processes in 1D
Home-page: https://celerite2.readthedocs.io
Author: Daniel Foreman-Mackey
Author-email: foreman.mackey@gmail.com
Maintainer: Daniel Foreman-Mackey
Maintainer-email: foreman.mackey@gmail.com
License: MIT
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
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License-File: LICENSE
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# 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](https://celerite.readthedocs.io) to improve

numerical stability and integration with various machine learning frameworks.

Documentation for this version can be found

[here](https://celerite2.readthedocs.io/en/latest/). 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)](http://www.gaussianprocess.org/gpml/). Similarly, the

_celerite_ algorithm is restricted to a specific class of covariance functions

(see [the original paper](https://arxiv.org/abs/1703.09710) for more information

and [a recent generalization](https://arxiv.org/abs/2007.05799) for extensions

to structured two-dimensional data). If you need scalable GPs with more general

covariance functions, [GPyTorch](https://gpytorch.ai/) might be a good choice.

