logLikH: Generic Log-likelihood function This function can be used to...

In hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication

Generic Log-likelihood function This function can be used to compute loglikelihood for homGP/hetGP models

Description

Generic Log-likelihood function This function can be used to compute loglikelihood for homGP/hetGP models

Usage

logLikH(
  X0,
  Z0,
  Z,
  mult,
  theta,
  g,
  Delta = NULL,
  k_theta_g = NULL,
  theta_g = NULL,
  logN = FALSE,
  beta0 = NULL,
  eps = sqrt(.Machine$double.eps),
  covtype = "Gaussian"
)

Arguments

X0	unique designs
Z0	averaged observations
Z	replicated observations (sorted with respect to X0)
mult	number of replicates at each Xi
theta	scale parameter for the mean process, either one value (isotropic) or a vector (anistropic)
g	nugget of the nugget process
Delta	vector of nuggets corresponding to each X0i or pXi, that are smoothed to give Lambda
k_theta_g	constant used for linking nuggets lengthscale to mean process lengthscale, i.e., theta_g[k] = k_theta_g * theta[k], alternatively theta_g can be used
theta_g	either one value (isotropic) or a vector (anistropic), alternative to using k_theta_g
logN	should exponentiated variance be used
beta0	mean, if not provided, the MLE estimator is used
eps	minimal value of elements of Lambda
covtype	covariance kernel type

Details

For hetGP, this is not the joint log-likelihood, only the likelihood of the mean process.

hetGP documentation built on Sept. 11, 2024, 6:56 p.m.