neg_log_marginal_post_approx_ref {RobustGaSP} | R Documentation |
Natural logarithm of marginal posterior density with the approximate reference prior of inverse range parameter after marginalizing out the mean (trend) and variance parameters by the location-scale prior.
neg_log_marginal_post_approx_ref(param, nugget, nugget.est
,R0, X, zero_mean,output, CL, a, b,kernel_type, alpha)
param |
a vector of natural logarithm of inverse-range parameters and natural logarithm of the nugget-variance ratio parameter. |
nugget |
the nugget-variance ratio parameter if this parameter is fixed. |
nugget.est |
Boolean value of whether the nugget is estimated or fixed. |
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 vector. |
CL |
prior parameters in the approximate reference prior. |
a |
prior parameter in the approximate reference prior. |
b |
prior parameter in the approximate reference prior. |
kernel_type |
type of kernel. |
alpha |
roughness parameters in the kernel functions. |
The natural logarithm of the marginal posterior density with approximate reference prior of inverse range parameter (beta parameterization).
Mengyang Gu [aut, cre], Jesus Palomo [aut], James Berger [aut]
Maintainer: Mengyang Gu <mengyang@pstat.ucsb.edu>
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.