generate_predictive_mean_cov | R Documentation |
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)
beta |
inverse-range parameters. |
nu |
noise-variance ratio parameter. |
input |
input matrix. |
X |
the mean basis function i.e. the trend function. |
zero_mean |
The mean basis function is zero or not. |
output |
output matrix. |
testing_input |
testing input matrix. |
X_testing |
mean/trend matrix of testing inputs. |
L |
a lower triangular matrix for the cholesky decomposition of |
LX |
a lower triangular matrix for the cholesky decomposition of X^tR^{-1}X. |
theta_hat |
estimated mean/trend parameters. |
sigma2_hat |
estimated variance parameter. |
rr0 |
a matrix of absolute difference between testing inputs and testing inputs. |
r0 |
a matrix of absolute difference between inputs and testing inputs. |
kernel_type |
Type of kernel. |
alpha |
Roughness parameters in the kernel functions. |
method |
method of parameter estimation. |
sample_data |
a boolean value. If true, the data (which may contain noise) is sampled. If false, the the mean of the data is sampled. |
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.
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.
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