I have been using the following random intercept model with non-informative
prior:
model {
for (i in 1:n.samples) {
vomit[i] ~ dbern(p[i])
logit(p[i]) <- beta0 + alpha[siteid[i]]
}
for (j in 1:n.sites) {
alpha[j]~dnorm(0,tau)
}
beta0 ~ dnorm(0.0,1.0E-6)
tau ~ dgamma(0.01,0.01)
}
list(n.samples=3780,n.sites=63)

How could I use a beta conjugate prior for the same model so that
p(i) ~ dbeta(alpha,beta)?

Thanks for your help.


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