| W {calibrator} | R Documentation |
Covariance matrix of beta given theta, phi, d
W(D1, D2, H1, H2, theta, det=FALSE, phi)
D1 |
Matrix whose rows are code run points |
D2 |
Matrix whose rows are observation points |
H1 |
regression function |
H2 |
regression function |
theta |
parameters |
det |
Boolean, with default FALSE meaning to return the
covariance matrix, and TRUE meaning to return its determinant. |
phi |
Hyperparameters |
This function is defined between equations 2 and 3 of the
supplement. It is used in functions betahat.fun.koh(),
p.eqn8.supp(), and p.joint().
Returns
W(theta)=inv(H(theta).inv(Vd(theta)).H(theta)).
If only the determinant is required, setting argument det to
TRUE is faster than using det(W(..., det=FALSE)), as the
former avoids an unnecessary use of solve().
Robin K. S. Hankin
M. C. Kennedy and A. O'Hagan 2001. Bayesian calibration of computer models. Journal of the Royal Statistical Society B, 63(3) pp425-464
M. C. Kennedy and A. O'Hagan 2001. Supplementary details on Bayesian calibration of computer models, Internal report, University of Sheffield. Available at http://www.shef.ac.uk/~st1ao/ps/calsup.ps
R. K. S. Hankin 2005. Introducing BACCO, an R bundle for Bayesian analysis of computer code output, Journal of Statistical Software, 14(16)
data(toys) W(D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy, theta=theta.toy, phi=phi.toy)