p.eqn8.supp {calibrator} | R Documentation |
A postiori probability of hyperparameters
Description
Function to determine the a-postiori probability of hyperparameters
\rho
, \lambda
and \psi_2
,
given observations and \psi_1
.
Usage
p.eqn8.supp(theta, D1, D2, H1, H2, d, include.prior=FALSE,
lognormally.distributed=FALSE, return.log=FALSE, phi)
p.eqn8.supp.vector(theta, D1, D2, H1, H2, d, include.prior=FALSE,
lognormally.distributed=FALSE, return.log=FALSE, phi)
Arguments
theta |
Parameters |
D1 |
Matrix of code run points |
D2 |
Matrix of observation points |
H1 |
Regression function for D1 |
H2 |
Regression function for D2 |
d |
Vector of code output values and observations |
include.prior |
Boolean, with |
lognormally.distributed |
Boolean, with |
return.log |
Boolean, with default |
phi |
Hyperparameters |
Details
The user should always use p.eqn8.supp()
, which is a wrapper
for p.eqn8.supp.vector()
. The forms differ in their treatment
of \theta
. In the former, \theta
must be a
vector; in the latter, \theta
may be a matrix, in which
case p.eqn8.supp.vector()
is applied to the rows
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
Examples
data(toys)
p.eqn8.supp(theta=theta.toy, D1=D1.toy, D2=D2.toy, H1=H1.toy, H2=H2.toy,
d=d.toy, phi=phi.toy)
## Now try using the true hyperparameters, and data directly drawn from
## the appropriate multivariate distn:
phi.true <- phi.true.toy(phi=phi.toy)
jj <- create.new.toy.datasets(D1.toy , D2.toy)
d.toy <- jj$d.toy
p.eqn8.supp(theta=theta.toy, D1=D1.toy, D2=D2.toy, H1=H1.toy,
H2=H2.toy, d=d.toy, phi=phi.true)
## Now try p.eqn8.supp() with a vector of possible thetas:
p.eqn8.supp(theta=sample.theta(n=11,phi=phi.true), D1=D1.toy,
D2=D2.toy, H1=H1.toy, H2=H2.toy, d=d.toy, phi=phi.true)