package gpr

  1. Overview
  2. Docs
GPR - Library and Application for Gaussian Process Regression

Install

Dune Dependency

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

Dependencies (6)

  1. jbuilder >= "1.0+beta10"
  2. gsl
  3. lacaml >= "9.3.2" & < "10.0.0"
  4. core >= "v0.9.1" & < "v0.13"
  5. base-threads
  6. ocaml >= "4.04"

Dev Dependencies

None

Used by

None

Conflicts

None

OCaml

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