hbar.fun.toy

0th

Percentile

Toy example of hbar (section 4.2)

A toy example of the expectation of h as per section 4.2

Keywords
array
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

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

Aliases
  • hbar.fun.toy
Examples
# NOT RUN {
data(toys)
hbar.fun.toy(theta=theta.toy, X.dist=X.dist.toy, phi=phi.toy)
# }
Documentation reproduced from package calibrator, version 1.2-8, License: GPL-2

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