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 to improve numerical stability and integration with various machine learning frameworks. This implementation includes interfaces in Python and C++, with full support for Theano/PyMC3 and experimental interfaces in JAX, PyTorch, and TensorFlow.
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). Similarly, the celerite algorithm is restricted to a specific class of covariance functions (see the original paper for more information and a recent generalization for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, GPyTorch might be a good choice.
License & attribution¶
Copyright 2020 Daniel Foreman-Mackey.
The source code is made available under the terms of the MIT license.
If you make use of this code, please cite the relevant papers: Citing celerite2