model
{
	Tau.noninformative <- 1.0E-2
	P.gamma <- 1.0E-2
	for (i in 1:N.sample) {
		Y[i] ~ dbin(p[i], N[i])
		logit(p[i]) <- a + b[i]
	}
	a ~ dnorm(0, Tau.noninformative)
	for (i in 1:N.sample) {
		b[i] ~ dnorm(0, tau)
	}
	tau ~ dgamma(P.gamma, P.gamma)
	sigma <- sqrt(1 / tau)
}