model{
       # likelihoood
       for(i in 1:N){
              Y[i] ~ dpois(landa[i])
              log(landa[i]) <- log(E[i]) + u[i] + v[i] }

       # convolution-prior
       for(i in 1:N){u[i] <- uConstr[i] + intercept}
       intercept ~ dflat()	       
       uConstr[1:N] ~ car.normal(adj[], weights[], num[], kappaU)
       for(k in 1:sumNumNeigh) { weights[k] <- 1 }      

       for(i in 1:N){ v[i] ~ dnorm(0, kappaV) }			      		

       # prior
       kappaU ~ dgamma(1, .5); kappaV ~ dgamma(1, .01)
}
