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-1.5.0.tbz
sha256=8b62b7b1ba33f187c01809095492479ac299b9f03f950adea9fc6f70b8646970
sha512=c703978c62421ab3505198fb7d67b4b9a624f8ce0bb9f1a0f418180772f3e9b348257df6e59af440cec69de1dc4d404b52d6cdaad670c1796f558e5882a6d253
Description
Gaussian process regression is a modern Bayesian approach to machine learning, and GPR implements some of the latest advances in this field.
Published: 22 Nov 2019
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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