model {
 
           #likelihood
             for(t in 1:T) {
                y[t] ~ dnorm(mu[t], tau.err)
                mu[t] <- beta + theta[t]
             }
   
             # prior for temporal effects 
          # RW prior for theta[t] - specified using car.normal with 
         #neighbours (t-1) and (t1) 
          # for theta[2],....,theta[T-1], and neighbours (t1) for 
         #theta[1] and (t-1) for theta[T]

             theta[1:T] ~ car.normal(adj[], weights[], num[], tau) 
             beta ~ dflat()
             
          # Specify weight matrix and adjacency matrix corresponding to RW(1) prior
          # (Note - this could be given in the data file instead)

             for(t in 1:1) {
                weights[t] <- 1;
               adj[t] <- t1;
               num[t] <- 1
             }
             for(t in 2:(T-1)) {
                weights[2+(t-2)*2] <- 1;
               adj[2+(t-2)*2] <- t-1
                weights[3+(t-2)*2] <- 1;
               adj[3+(t-2)*2] <- t1;
               num[t] <- 2
             }
             for(t in T:T) {
                weights[(T-2)*2 + 2] <- 1;
               adj[(T-2)*2 + 2] <- t-1;
               num[t] <- 1
             }
             
          # Alternatively, a weight matrix and adjacency matrix 
         #corresponding to RW(2) prior can
         # be specified or given in the data file 
         #(note, no need to change the prior distribution
         # on theta, just the weights/adjacencies)
          #   for(t in 1:1) {      
          #      weights[t] <- 2;      adj[t] <- t1
          #      weights[t1] <- -1;      adj[t1] <- t2;         num[t] <- 2
          #   }
          #   for(t in 2:2) {      
          #   weights[t1] <- 2;         adj[t1] <- t-1
          #   weights[t2] <- 4;         adj[t2] <- t1
          #   weights[t3] <- -1;         adj[t3] <- t2;         num[t] <- 3
          #   }
          #for(t in 3:(T-2)) {
          #      weights[6+(t-3)*4] <- -1;      adj[6+(t-3)*4] <- t-2
          #      weights[7+(t-3)*4] <- 4;      adj[7+(t-3)*4] <- t-1
          #      weights[8+(t-3)*4] <- 4;      adj[8+(t-3)*4] <- t1
          #      weights[9+(t-3)*4] <- -1;      adj[9+(t-3)*4] <- t2;         num[t] <- 4
          #   }
          #   for(t in (T-1):(T-1)) {      
          #      weights[(T-4)*4 + 6] <- 2;      adj[(T-4)*4 + 6] <- t1
          #      weights[(T-4)*4 + 7] <- 4;      adj[(T-4)*4 + 7] <- t-1
          #      weights[(T-4)*4 + 8] <- -1;      adj[(T-4)*4 + 8] <- t-2;         num[t] <- 3
          #   }
          #   for(t in T:T) {      
          #      weights[(T-4)*4 + 9] <- 2;      adj[(T-4)*4 + 9] <- t-1
          #      weights[(T-4)*4 + 10] <- -1;      adj[(T-4)*4 + 10] <- t-2;         num[t] <- 2
          #   }

          # other priors
             tau.err ~ dgamma(0.01, 0.01)      # measurement error precision
             sigma.err <- 1 / sqrt(tau.err)
             sigma2.err <- 1/tau.err

             tau ~ dgamma(0.01, 0.01)            # random walk precision
             sigma <- 1 / sqrt(tau)
             sigma2 <- 1/tau
         # include this variable to use in time series (model fit) plot
             for(t in 1:T) { day[t] <- t }            
 
          }