package gpr
GPR - Library and Application for Gaussian Process Regression
Install
Dune Dependency
mmottl.github.io
Readme
Changelog
LGPL-2.1-or-later WITH OCaml-LGPL-linking-exception License
Edit opam file
Versions (7)
Authors
Maintainers
Sources
gpr-1.3.0.tbz
sha256=a55b06ab5fe781d4a6e05cb4cba1f4f5a2e53ac8c9f4106ab4158845fd649f66
md5=2e0f581f098adcca18ede12fbddb7acf
Description
Gaussian process regression is a modern Bayesian approach to machine learning, and GPR implements some of the latest advances in this field.
Published: 02 Aug 2017
README
OCaml-GPR - Efficient Gaussian Process Regression in OCaml
This OCaml-library, which also comes with an elaborate example application, implements some of the newest approximation algorithms (e.g. SPGP) for scalable Gaussian process regression for arbitrary covariance functions. Here is an example graph showing the fit of such a sparse Gaussian process to a nonlinear function:
Please refer to the GPR manual for further details and to the online API documentation as programming reference.
Contact Information and Contributing
Please submit bugs reports, feature requests, contributions and similar to the GitHub issue tracker.
Up-to-date information is available at: https://mmottl.github.io/gpr
sectionYPositions = computeSectionYPositions($el), 10)"
x-init="setTimeout(() => sectionYPositions = computeSectionYPositions($el), 10)"
>
On This Page