			model {

			# Likelihood
				for (i in 1 : N) {
					O[i]  ~ dpois(mu[i])
					log(mu[i]) <- log(E[i]) + alpha0 + alpha1 * X[i]/10 + b[i]
					# Area-specific relative risk (for maps)
					RR[i] <- exp(alpha0 + alpha1 * X[i]/10 + b[i])  
				}

			# CAR prior distribution for random effects: 
				b[1:N] ~ car.normal(adj[], weights[], num[], tau)
				for(k in 1:sumNumNeigh) {
					weights[k] <- 1
				}

			# Other priors:
				alpha0  ~ dflat()  
				alpha1 ~ dnorm(0.0, 1.0E-5)
				tau  ~ dgamma(0.5, 0.0005) 				# prior on precision
				sigma <- sqrt(1 / tau)				# standard deviation
			   b.mean <- sum(b[])
			}
