This function update the inverse of R_z multiple the outputs in the S-GaSP model for prediction.
Update_R_inv_y(R_inv_y, R0, beta_delta, kernel_type, alpha, lambda_z, num_obs)A vector of the inverse of covariance multiplied by the outputs in the S-GaSP model.
A vector of inverse of covariance multiplied by the outputs.
A List of matrix where the j-th matrix is an absolute difference matrix of the j-th input vector.
Inverse range parameters.
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. It is only useful if the power exponential correlation function is used.
A parameter controling how close the math model to the reality in squared distance.
Number of observations.
tools:::Rd_package_author("RobustCalibration")
Maintainer: tools:::Rd_package_maintainer("RobustCalibration")
A. O'Hagan and M. C. Kennedy (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology, 63, 425-464.
Mengyang Gu. (2016). Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output. Ph.D. thesis. Duke University.
M. Gu and L. Wang (2017) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. arXiv preprint arXiv:1707.08215.