model
{
	Tau.noninformative <- 1.0E-4
	for (i in 1:N) {
		Y[i] ~ dpois(lambda[i])
		log(lambda[i]) <- beta1 + beta2 * (X[i] - Mean.X)
	}
	beta1 ~ dnorm(0, Tau.noninformative)
	beta2 ~ dnorm(0, Tau.noninformative)
}