Welcome to GPJax!
Welcome to GPJax!#
GPJax is a didactic Gaussian process library that supports GPU acceleration and just-in-time compilation. We seek to provide a flexible API as close as possible to how the underlying mathematics is written on paper to enable researchers to rapidly prototype and develop new ideas.
You can view the source code for GPJax here on Github.
‘Hello World’ example#
Defining a Gaussian process posterior is simple as typing the maths we would write on paper.
import gpjax as gpx kernel = gpx.kernels.RBF() prior = gpx.gps.Prior(kernel = kernel) likelihood = gpx.likelihoods.Gaussian(num_datapoints = 123) posterior = prior * likelihood
For comparison, the corresponding model could be written as
To learn more, checkout the regression notebook.
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James Hensman, Alexander Matthews, and Zoubin Ghahramani. Scalable variational Gaussian process classification. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, 351–360. PMLR, 2015.
Felix Leibfried, Vincent Dutordoir, ST John, and Nicolas Durrande. A tutorial on sparse Gaussian processes and variational inference. arXiv preprint arXiv:2012.13962, 2020.
Anton Mallasto and Aasa Feragen. Learning from uncertain curves: the 2-Wasserstein metric for Gaussian processes. Advances in Neural Information Processing Systems, 2017.
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