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
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Versions (7)
Authors
Maintainers
Sources
gpr-1.3.1.tbz
sha256=28678f583a08b6470bda5f4ddf907b6492e0b73e3f58aa561af35beff344accb
md5=c1e0ce19e20c8a9718f237e028e56d36
Description
Gaussian process regression is a modern Bayesian approach to machine learning, and GPR implements some of the latest advances in this field.
Published: 11 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
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