predict.GaussianProcess: Create Predictions Using a Gaussian Process
In mattdneal/gaussianProcess: Gaussian Process Fitting and Prediction

Description Usage Arguments Value Examples

Returns the posterior mean and variance of a set of data points under a given Gaussian process

1 2	## S3 method for class 'GaussianProcess' predict(gp.obj, data)

`gp.obj`	A trained gaussianProcess object
`data`	Data to predict

a list containing named elements:

mean - the predicted mean value for each data point in data.
var - the variance about the predicted mean for each data point in data.

x <- rnorm(50)
y <- sin(1/(x^2 + 0.15))
mt <- create.model.tree.builtin()
mt <- insert.kernel.instance(mt, 1, "SE", NULL, hyper.params=c(l=1))
gp <- create.gaussian.process(x, y, mt)
gp$fit.hyperparams(NA)

x1 <- rnorm(50)
y1.predicted <- predict(gp, x1)$mean