Multiplying a matrix by a scalar which has a prior distribution in OpenBUGS

Question

So I am having a problem specifying my model in OpenBUGS. A set of vectors in a linear regression model is given a multivariate normal prior with a constant mean vector and a constant precision matrix which is multiplied by a parameter with a hyperprior of a gamma distribution.

The code is as follows:

model{
#likelihood
for(i in 1:93){
    BegSal[i]~dnorm(mu[i],tau)
    mu[i]<-b[1]+b[2]*Sex[i]+b[3]*Educ[i]+b[4]*Exper[i]+b[5]*Time[i]
}
#priors
tau~dgamma(.00001,.00001)
covNew[1:5,1:5]<-tau*covPri[1:5,1:5]
b[1:5]~dmnorm(meanPri[1:5],covNew[1:5,1:5])
}

#data
list(meanPri= c(13625,4500,-14388.3333,55,308.3333),
     covPri = structure(.Data = c(362.5,362.5,452.5,452.5,362.5,
                                  362.5,365,452.5,452.5,362.5,
                                  452.5,452.5,565,565,452.5,
                                  452.5,452.5,565,25565,452.5,
                                  362.5,362.5,452.5,452.5,1802.5),
                                  .Dim = c(5, 5)) )

I'm receiving an error "Expected Multivariate Node," when I try and compile. If anyone has any suggestions I would greatly appreciate it. I left the rest of the data out as it is fairly long. Thanks for any help.

Answer 1

You need to construct covNew element by element

for(i in 1:5){
for(j in 1:5){
    covNew[i,j]<-tau*covPri[i,j]
}
}

BUGS acts a bit differently than R when it comes to constructing matrices/arrays. See the WINBUGS user manual for full details.