# p.eqn4.supp

##### Apostiori probability of psi1

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

- Keywords
- array

##### 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

##### References

M. C. Kennedy and A. O'Hagan 2001.

*Bayesian calibration of computer models*. Journal of the Royal Statistical Society B, 63(3) pp425-464M. 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.psR. 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

```
# NOT RUN {
data(toys)
p.eqn4.supp(D1=D1.toy, y=y.toy , H1=H1.toy, lognormally.distributed=TRUE,
phi=phi.toy)
# }
```

*Documentation reproduced from package calibrator, version 1.2-8, License: GPL-2*