A toy example of the expectation of h as per section 4.2
Usage
hbar.fun.toy(theta, X.dist, phi)
Arguments
theta
Parameter set
X.dist
Distribution of variable inputs X as per section 4.2
phi
Hyperparameters
Value
Returns a vector as per section 4.2 of KOH2001S
Details
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().
References
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)