gpr

GPR - Library and Application for Gaussian Process Regression
README

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

Install
Published
22 Nov 2019
Sources
gpr-1.5.0.tbz
sha256=8b62b7b1ba33f187c01809095492479ac299b9f03f950adea9fc6f70b8646970
sha512=c703978c62421ab3505198fb7d67b4b9a624f8ce0bb9f1a0f418180772f3e9b348257df6e59af440cec69de1dc4d404b52d6cdaad670c1796f558e5882a6d253
Dependencies
gsl
>= "1.24.0"
lacaml
>= "11.0.0"
core
>= "v0.13" & < "v0.15"
dune
>= "1.10"
ocaml
>= "4.08"
Reverse Dependencies