	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]])
			}
		}
	}   
