This function computes the natural logarithm of marginal likelihood of the PP GaSP model after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
log_marginal_lik_ppgasp(param, nugget, nugget_est, R0, X, zero_mean,output,
kernel_type, alpha)
The numerical value of natural logarithm of the marginal likelihood.
a vector of natural logarithm of inverse-range parameters and natural logarithm of the nugget-variance ratio parameter.
the nugget-variance ratio parameter if this parameter is fixed.
Boolean value of whether the nugget is estimated or fixed.
a List of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
the mean basis function i.e. the trend function.
the mean basis function is zero or not.
a matrix where each row is one runs of the computer model output.
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.
roughness parameters in the kernel functions.
tools:::Rd_package_author("RobustGaSP")
Maintainer: tools:::Rd_package_maintainer("RobustGaSP")
M. Gu. and J.O. Berger (2016). Parallel partial Gaussian process emulation for computer models with massive output. Annals of Applied Statistics, 10(3), 1317-1347.
M. Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.