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

Description Usage Arguments Value References Examples

Description

Prediction of normal-distributed random variables using GP regression

Usage

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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

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 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.