celerite     \se.le.ʁi.te\     noun, archaic literary
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.

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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:

     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}


0.1.2 (2017-02-23)

  • Fixed bug when pickling Model objects.
  • Added sparse solver using Eigen/SparseLU

0.1.1 (2017-02-12)

  • Windows build support.
  • Faster solver for wide problems by linking to LAPACK.

0.1.0 (2017-02-10)

  • Initial stable release