This function computes the natural logarithm of marginal likelihood after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
log_marginal_lik(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.
the output vector.
A vector of integer specifying the type of kernels of each coordinate of the input.
In each coordinate of the vector, 1 means the pow_exp kernel with roughness parameter specified in alpha; 2 means matern_3_2 kernel; 3 means matern_5_2 kernel; 5 means periodic_gauss kernel; 5 means periodic_exp kernel.
Roughness parameters in the kernel functions.
tools:::Rd_package_author("RobustGaSP")
Maintainer: tools:::Rd_package_maintainer("RobustGaSP")
M. Gu. (2016). Robust uncertainty quantification and scalable computation for computer models with massive output. Ph.D. thesis. Duke University.