Negative natural logarithm of marginal posterior density (with regard to a specific parameterization) with reference prior of inverse range parameter (beta parameterization) after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
neg_log_marginal_post_ref(param, nugget, nugget.est,
R0, X, zero_mean,output, prior_choice,
kernel_type, alpha)
The negative natural logarithm of marginal posterior density with reference prior with regard to a specific parameterization.
a vector of natural logarithm of inverse-range parameters and natural logarithm of the noise-variance ratio parameter.
the noise-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.
parameterization: ref_xi for log inverse range parameterization or ref_gamma for range parameterization.
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")
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.