model
   {
      for (i in 1 : nChild) {
         theta[i] ~ dnorm(0.0, 0.001)
         for (j in 1 : nInd) { 
   # Cumulative probability of > grade k given theta
            for (k in 1: ncat[j] - 1) {
               logit(Q[i, j, k]) <- delta[j] * (theta[i] - gamma[j, k])
            }
         }

   # Probability of observing grade k given theta
         for (j in 1 : nInd) {
            p[i, j, 1] <- 1 - Q[i, j, 1]
            for (k in 2 : ncat[j] - 1) {
               p[i, j, k] <- Q[i, j, k - 1] - Q[i, j, k]
            }
            p[i, j, ncat[j]] <- Q[i, j, ncat[j] - 1]
            grade[i, j] ~ dcat(p[i, j, 1 : ncat[j]])
         }
      }
   }