approximator (version 1.2-6)

approximator-package: Bayesian approximation of computer models when fast approximations are available

Description

Implements the ideas of Kennedy and O'Hagan 2000 (see references).

Details

Package:	approximator
Type:	Package
Version:	1.0
Date:	2006-01-10
License:	GPL

This package implements the Bayesian approximation techniques discussed in Kennedy and O'Hagan 2000.

In its simplest form, it takes input from a “slow” code and a “fast” code, each run at different points in parameter space. The approximator package then uses both sets of model runs to infer what the top level code would produce at a given, untried point in parameter space.

References

R. K. S. Hankin 2005. “Introducing BACCO, an R bundle for Bayesian analysis of computer code output”, Journal of Statistical Software, 14(16)

M. C. Kennedy and A. O'Hagan 2000. “Predicting the output from a complex computer code when fast approximations are available” Biometrika, 87(1): pp1-13

Examples

# NOT RUN {
data(toyapps)
mdash.fun(x=1:3, D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy, z=z.toy, basis=basis.toy)
# }