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]

Versions [RSS] [faq]	0.1
Dependencies	base (==4.), haskell98 (==1.), hmatrix (==0.12.), hmatrix-special (==0.1.), mtl (==2.), parsec (==3.), random (==1.*) [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
Distributions	NixOS:0.1
Downloads	1268 total (6 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]

Modules

[Index]

Downloads

HasGP-0.1.tar.gz [browse] (Cabal source package)
Package description (as included in the package)

Maintainer's Corner

For package maintainers and hackage trustees

edit package information

Candidates

No Candidates

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:

http://www.cl.cam.ac.uk/~sbh11/HasGP/