hbar.fun.toy: Toy example of hbar (section 4.2)

In RobinHankin/calibrator: Bayesian Calibration of Complex Computer Codes

Description

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

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()

Value

Returns a vector as per section 4.2 of KOH2001S

Author(s)

Robin K. S. Hankin

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.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)

See Also

h1.toy

Examples

data(toys)
hbar.fun.toy(theta=theta.toy, X.dist=X.dist.toy, phi=phi.toy)

RobinHankin/calibrator documentation built on April 27, 2021, 2:47 p.m.