Metadata-Version: 2.1
Name: celerite2
Version: 0.2.0
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
Platform: UNKNOWN
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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# 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
Theano/PyMC3 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.


