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celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

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Ada Coda
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celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

Postby Ada Coda » Sat Sep 09, 2017 10:04 am

celerite: Scalable 1D Gaussian Processes in C++, Python, and Julia

Abstract: 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.

Credit: Foreman-Mackey, Daniel; Agol, Eric; Ambikasaran, Sivaram; Angus, Ruth

Site: http://celerite.rtfd.io/
http://adsabs.harvard.edu/abs/2017arXiv170309710F

Bibcode: 2017ascl.soft09008F

Preferred citation method: http://adsabs.harvard.edu/abs/2017arXiv170309710F

ID: ascl:1709.008
Last edited by Ada Coda on Sat Sep 09, 2017 10:08 am, edited 1 time in total.
Reason: Updated code entry.

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