hbar.fun.toy {calibrator} | R Documentation |
A toy example of the expectation of h as per section 4.2
hbar.fun.toy(theta, X.dist, phi)
theta |
Parameter set |
X.dist |
Distribution of variable inputs |
phi |
Hyperparameters |
Note that if h1.toy()
or h2.toy()
change, then
hbar.fun.toy()
will have to change too; see ?h1.toy
for an
example in which nonlinearity changes the form of E.theta.toy()
Returns a vector as per section 4.2 of KOH2001S
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.tonyohagan.co.uk/academic/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)
hbar.fun.toy(theta=theta.toy, X.dist=X.dist.toy, phi=phi.toy)