gaussianProcess: Create a gaussianProcess object with a given mean function...

In ebenmichael/gaussianProcess: Fits Gaussian Processes for Regression

Description

Create a gaussianProcess object with a given mean function and covariance kernel, and data

Usage

gaussianProcess(X, y, meanFunc = zeroFunction, kernel = rbf,
  hyper.params = NULL, noise.var = 1, order = 5/2, verbose = 0)

Arguments

X	An n x d matrix of covariates for observed data
y	An n dimensional vector of outputs for observed data
meanFunc	The mean function of the process, defaults to 0
kernel	The covariance kernel of the process, defaults to rbf
hyper.params	The hyper parameters for the kernel (c(amplitude, scales)), if NULL then optimize them using the log likelihood
noise.var	The variance of the noise around the function
order	The order of the kernel, defaults to 5/2
verbose	Level of information printed out, defaults to 0

Value

A gaussianProcess with these options

ebenmichael/gaussianProcess documentation built on May 15, 2019, 7:30 p.m.