mdash.fun: Mean of Gaussian process

In approximator: Bayesian Prediction of Complex Computer Codes

Mean of Gaussian process

Description

Returns the mean of the Gaussian process conditional on the observations and the hyperparameters

Usage

mdash.fun(x, D1, subsets, hpa, Vinv = NULL, use.Vinv = TRUE, z, basis)

Arguments

x	Point at which mean is desired
D1	Code design matrix for level 1 code
subsets	subsets object
hpa	Hyperparameter object
Vinv	Inverse of the variance matrix; if NULL, the function will calculate it
use.Vinv	Boolean, with default TRUE meaning to use the inverse of V and FALSE meaning to use a method that does not involve inverting V
z	observations
basis	Basis functions

Author(s)

Robin K. S. Hankin

References

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

data(toyapps)
mdash.fun(x=1:3,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

uu <- rbind(1:3,1,3:1,1:3)
rownames(uu) <- c("first","second","third","fourth")

mdash.fun(x=uu,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

approximator documentation built on Aug. 25, 2023, 1:07 a.m.