package gpr

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GPR - Library and Application for Gaussian Process Regression

Install 

dune-project
 Dependency

mmottl.github.io Readme Changelog LGPL-2.1-or-later WITH OCaml-LGPL-linking-exception License Edit opam file Versions (3)

Authors

  1. M
    Markus Mottl <markus.mottl@gmail.com>

Maintainers

  1. M
    Markus Mottl <markus.mottl@gmail.com>

Sources

gpr-1.5.2.tbz
sha256=0886ef0e74d1980fc138ad0acbead229ae902f8ab244fd0717c7ff0cc698705b
sha512=d2fb5ba194f6ee9fcfb16a54f7f2f1018e09ea1d8ace359a1f2b92a102a78dfb019cd243a1c7caff51e3dc62453316624f7f2635c6a241508dcd3f48f0550e92

Description

Gaussian process regression is a modern Bayesian approach to machine learning, and GPR implements some of the latest advances in this field.

Added to opam-repository:

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:

Sparse Gaussian Process

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, and contributions to the GitHub issue tracker.

Up-to-date information is available at: https://mmottl.github.io/gpr

Dependencies (6)

  1. gsl >= "1.24.0"
  2. lacaml >= "11.0.0"
  3. core_unix >= "v0.13"
  4. core >= "v0.13"
  5. ocaml >= "4.08"
  6. dune >= "2.7"

Dev Dependencies (1)

  1. odoc with-doc

Optional Dependencies

None

Used by

None

Conflicts

None

Build status

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Version OCaml 4.11 OCaml 4.14 OCaml 5.2 OCaml 5.4 OCaml 5.5
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