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[ascl:1709.008] celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

celerite provides fast and scalable Gaussian Process (GP) Regression in one dimension and is implemented in C++, Python, and Julia. 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 be used alongside a non-linear optimization or posterior inference library for the best results.

Code site:
https://github.com/dfm/celerite https://github.com/ericagol/celerite.jl http://celerite.rtfd.io/
Appears in:
http://adsabs.harvard.edu/abs/2017arXiv170309710F
Bibcode:
2017ascl.soft09008F

ascl:1709.008
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