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[])
          }