model {
    for (i in 1:n) {
        for (j in 1:n.raters) {
            rater[i, j] ~ dcat(p[j, 1:n.categories])
        }
        equal[i] ~ dbern(equal.p)
    }
    # priors
    for (l in 1:n.raters) {
        p[l, 1:n.categories] ~ ddirch(alpha)
    }
    equal.p ~ dbeta(1, 1)
    # Compute chance agreement and Cohen's Kappa
    equal.c <- sum(p[1, 1:n.categories] * p[2, 1:n.categories])
    Kappa <- (equal.p - equal.c) / (1 - equal.c)
}