emulator (version 1.2-24)

prior.b: Prior linear fits

Description

Gives the fitted regression coefficients corresponding to the specified regression model.

Usage

prior.b(H, Ainv, d, b0 = NULL, B0 = NULL)
prior.B(H , Ainv , B0=NULL)

Arguments

H

Regression basis function (eg that returned by regressor.multi())

Ainv

\(A^{-1}\) where \(A\) is a correlation matrix (eg that returned by corr.matrix())

d

Vector of data points

b0

prior constant

B0

prior coefficients

Author

Robin K. S. Hankin

References

• J. Oakley 2004. Estimating percentiles of uncertain computer code outputs. Applied Statistics, 53(1), pp89-93.

• J. Oakley 1999. Bayesian uncertainty analysis for complex computer codes, PhD thesis, University of Sheffield.

Examples

# example has 10 observations on 6 dimensions.
# function is just sum( (1:6)*x) where x=c(x_1, ... , x_2)

data(toy)
val <- toy
d <- apply(val,1,function(x){sum((1:6)*x)})

#add some noise:
d <- jitter(d)

A <- corr.matrix(val,scales=rep(1,ncol(val)))
Ainv <- solve(A)
H <- regressor.multi(val)

prior.b(H,Ainv,d)
prior.B(H,Ainv)