gpr_posterior: GPR posterior

Description Usage Arguments Value

View source: R/gpr.R

Description

a simple GPR posterior distribution, no parameter learning

Usage

1
gpr_posterior(x, y, x.targets, noise, kernelfunc, derivatives = FALSE)

Arguments

x

obs timepoints

y

obs values

x.targets

target timepoints

noise

noise std, a single value or a vector

kernelfunc

a kernel function of (x1,x2) (returns a matrix)

derivatives

compute also derivatives

Value

a gpsimple-object with fields

x

timepoints

mean

GP mean

cov

covariance matrix

noisestd

vector of noise std's

mll

marginal log likelihood

x.obs

original observation times

y.obs

original observation values


nsgp documentation built on May 2, 2019, 9:19 a.m.