HasGP-0.1: 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.
Modules
- HasGP
- Classification
- Covariance
- Data
- Demos
- Likelihood
- Parsers
- Regression
- Support
- Types