p.eqn4.supp {calibrator}

Apostiori probability of psi1

Description

Gives the probability of \psi_1, given observations. Equation 4 of the supplement

Usage

p.eqn4.supp(D1, y, H1, include.prior=TRUE, lognormally.distributed, return.log, phi)

Arguments

D1

Matrix of code run points

y

Vector of code outputs

H1

Regression function

include.prior

Boolean with default TRUE meaning to return the likelihood multiplied by the aprior probability and FALSE meaning to return the likelihood without the prior.

lognormally.distributed

Boolean; see ?prob.theta for details

return.log

Boolean, with default FALSE meaning to return the probability and TRUE meaning to return the logarithm of the probability

phi

hyperparameters

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

W1

Examples

data(toys)
p.eqn4.supp(D1=D1.toy, y=y.toy , H1=H1.toy, lognormally.distributed=TRUE,
phi=phi.toy)