HasGP: A Haskell library for inference using Gaussian processes
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]
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
Candidates
- 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 |
Distributions | |
Reverse Dependencies | 1 direct, 0 indirect [details] |
Downloads | 1404 total (3 in the last 30 days) |
Rating | (no votes yet) [estimated by Bayesian average] |
Your Rating | |
Status | Docs uploaded by user Build status unknown [no reports yet] |