HasGP: A Haskell library for inference using Gaussian processes

[ ai, classification, datamining, gpl, library, statistics ] [ Propose Tags ]

A Haskell library implementing algorithms for supervised learning, roughly corresponding to chapters 1 to 5 of "Gaussian Processes for Machine Learning" by Carl Rasmussen and Christopher Williams, The MIT Press 2006. In particular, algorithms are provides for regression and for two-class classification using either the Laplace or EP approximation.

[Skip to Readme]


Maintainer's Corner

Package maintainers

For package maintainers and hackage trustees


  • No Candidates
Versions [RSS] 0.1
Dependencies base (>=4 && <5), haskell98 (>=1 && <2), hmatrix (>=0.12 && <0.13), hmatrix-special (>=0.1 && <0.2), mtl (>=2 && <3), parsec (>=3 && <4), random (>=1 && <2) [details]
License GPL-3.0-only
Copyright Copyright (C) 2011 Sean Holden
Author Sean B. Holden
Maintainer sbh11@cl.cam.ac.uk
Category AI, Classification, Datamining, Statistics
Home page http://www.cl.cam.ac.uk/~sbh11/HasGP
Bug tracker sbh11@cl.cam.ac.uk
Uploaded by SeanHolden at 2011-10-26T15:35:53Z
Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 1386 total (5 in the last 30 days)
Rating (no votes yet) [estimated by Bayesian average]
Your Rating
  • λ
  • λ
  • λ
Status Docs uploaded by user
Build status unknown [no reports yet]

Readme for HasGP-0.1

[back to package description]
The HasGP package for Gaussian process inference in Haskell

Copyright (C) 2011 Sean Holden sbh11@cl.cam.ac.uk

For a detailed description of how to install, use and modify this code 
please download the User Manual from the project site at: