tinygp
Contents
tinygp¶
The tiniest of Gaussian Process libraries.
tinygp is an extremely lightweight library for building Gaussian Process (GP)
models in Python, built on top of jax. It is
not (yet?) designed to provide all the shiniest algorithms for scalable
computations (check out celerite2 or
GPyTorch if you need something like that), but I think it
has a nice interface, and it’s pretty fast. Thanks to jax, tinygp
supports things like GPU acceleration and automatic differentiation.
How to find your way around?
🖥 A good place to get started is with the Installation Guide and then the Tutorials. You might also be interested in the Why tinygp? page.
📖 For all the details, check out the User Guide, including the full API documentation.
💡 If you’re running into getting tinygp to do what you want, first check
out the Troubleshooting page, for some general tips and tricks.
🐛 If Troubleshooting doesn’t solve your problems, or if you find bugs, check out the Contributor Guide and then head on over to the GitHub issues page.
👈 Check out the sidebar to find the full table of contents.
Authors & license¶
Copyright 2021, 2022 Simons Foundation, Inc.
Built by Dan Foreman-Mackey and contributors (see the
contribution graph for the
most up-to-date list). Licensed under the MIT license (see LICENSE).