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-1.4.0.tbz
sha256=3d5601058dcb941e8c340bd306f532b228816b399f81dba3a13d997cdb187bed
md5=cfccc0b7f3ee1caf83e25fa0fb4b1a0a
Description
Gaussian process regression is a modern Bayesian approach to machine learning, and GPR implements some of the latest advances in this field.
Published: 20 Aug 2018
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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