model
{
	Tau.noninformative <- 1.0E-4
	Sigma.max <- 1.0E+4
	for (i in 1:N.sample) {
		Y[i] ~ dbin(q[i], 8)
		logit(q[i]) <- a + r[i]
	}
	a ~ dnorm(0, Tau.noninformative)
	for (i in 1:N.sample) {
		r[i] ~ dnorm(0, tau)
	}
	tau <- 1.0 / (sigma * sigma)
	sigma ~ dunif(0, Sigma.max)
}