The function computes predictive mean and cholesky decomposition of the scaled covariance function.
generate_predictive_mean_cov(beta, nu, input, X,zero_mean,output,
testing_input,X_testing, L, LX, theta_hat, sigma2_hat,rr0,r0,
kernel_type,alpha,method,sample_data)
A list of 2 elements. The first is a vector for predictive mean for testing inputs. The second is a scaled covariance matrix of the predictive distribution.
inverse-range parameters.
noise-variance ratio parameter.
input matrix.
the mean basis function i.e. the trend function.
The mean basis function is zero or not.
output matrix.
testing input matrix.
mean/trend matrix of testing inputs.
a lower triangular matrix for the cholesky decomposition of R, the correlation matrix.
a lower triangular matrix for the cholesky decomposition of X^tR^{-1}X.
estimated mean/trend parameters.
estimated variance parameter.
a matrix of absolute difference between testing inputs and testing inputs.
a matrix of absolute difference between inputs and testing inputs.
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.
method of parameter estimation. post_mode means the marginal posterior mode is used for estimation. mle means the maximum likelihood estimation is used. mmle means the maximum marginal likelihood estimation is used. The post_mode is the default method.
a boolean value. If true, the data (which may contain noise) is sampled. If false, the the mean of the data is sampled.
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.