lvgpr: Prediction of normal-distributed random variables using GP...

In dirmeier/gpR: Gaussian processes in R

Description

Prediction of normal-distributed random variables using GP regression

Usage

lvgpr(x.train = NULL, y.train = NULL, x.new = NULL, pars = list(var = 1,
  inv.scale = 2, gamma = 2, noise = 0.1, kernel = "gamma.exp"), ...)

Arguments

x.train	vector of independent variables used for training
y.train	vector of dependent variables used for training
x.new	vector of variables for which a response should be predicted
pars	a list containing the hyper-parameters and kernel specifications
...	additional parameters (not specified)

Value

an object of class lvgpr.pred

y.predict	the predicted y* values given the lvgpr object
mean	the posterior mean values
cov	the posterior covariance/kernel
call	the function call

References

Rasmussen C.E. and Williams C.K.I. (2006), Gaussian Processes for Machine Learning, MIT Press
http://www.gaussianprocess.org/gpml/

Barber D. (2013), Bayesian Reasoning and Machine Learning, Cambridge University Press
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

Examples

 x.train <- seq(-5, 5, .1)
 y.train <- rnorm(length(x.train))
 x.new <- rnorm(100)
 pars <- list(var=1, inv.scale=2, gamma=2, noise=.1, kernel="gamma.exp")
 pred <- lvgpr(x.train, y.train, x.new, pars)

dirmeier/gpR documentation built on May 15, 2019, 8:50 a.m.