construct_ppgasp: PP GaSP constructor after estimating the parameters
In RobustGaSP: Robust Gaussian Stochastic Process Emulation

construct_ppgasp

R Documentation

PP GaSP constructor after estimating the parameters

Description

This function constructs the PP GaSP model if the range and noise-variance ratio parameters are given or have been estimated.

Usage

construct_ppgasp(beta, nu, R0, X, zero_mean, output, kernel_type, alpha)

Arguments

`beta`	inverse-range parameters.
`nu`	noise-variance ratio parameter.
`R0`	A List of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
`X`	The mean basis function i.e. the trend function.
`zero_mean`	The mean basis function is zero or not.
`output`	the output matrix.
`kernel_type`	Type of kernel. `matern_3_2` and `matern_5_2` are `Matern kernel` with roughness parameter 3/2 and 5/2 respectively. `pow_exp` is power exponential kernel with roughness parameter alpha. If `pow_exp` is to be used, one needs to specify its roughness parameter alpha.
`alpha`	Roughness parameters in the kernel functions.

Value

A list. The first element is a lower triangular matrix L, a cholesky decomposition of R, i.e. LL^t=R and R is the correlation matrix. The second element is lower triangular matrix LX a cholesky decomposition of X^tR^{-1}X. The third element is a matrix of theta_hat, the mean (trend) parameters. The last element is a vector of sigma2_hat, which is the estimated variance parameter on each function.