Gaussian Process Library. This module contains assorted functions that support the computation of covariance, constructing covariance matrices etc.
Covariance functions store log parameters. Functions are needed to return the covariance and its derivative. Derivatives are with respect to the actual parameters, NOT their logs.
Copyright (C) 2011 Sean Holden. firstname.lastname@example.org.
- class CovarianceFunction a where
- covarianceMatrix :: CovarianceFunction c => c -> Inputs -> CovarianceMatrix
- covarianceWithPoint :: CovarianceFunction c => c -> Inputs -> Input -> DVector
- covarianceWithPoints :: CovarianceFunction c => c -> Inputs -> [Input] -> [DVector]
class CovarianceFunction a where
The actual hyperparameter values.
Derivative of covariance with respect to parameters
Construct using log parameters.
Get log parameters.
Construct a matrix of covariances from a covariance and a design matrix.
Constructs the column vector required when a new input is included. Constructed as a matrix to avoid further work elsewhere.