# hbar.fun.toy

##### 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) pp425-464M. 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)
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*