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

  # 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
}