# Ragged array of simplexes

Thanks for the reply. That \mathbb{R}^k \to \mathbb{R}^k mapping makes sense, and it’s invertible because one element of the simplex \pi is the reciprocal of the sum \Gamma := \sum_{i=1}^K \gamma_{i}. I’m still not sure I understand the Jacobian; for example,

J_{11} = \frac{\partial \pi_{1}}{\partial \gamma_{1}} = \frac{\partial}{\partial \gamma_{1}} \left[\frac{\gamma_{1}}{\Gamma}\right] = \frac{1 \cdot \Gamma - \gamma_{1} \cdot 1}{\Gamma^2} = \frac{1}{\Gamma} - \frac{\gamma_{1}}{\Gamma^2}.

Maybe I just need remedial calculus?

I’ve been using a different approach based on simplex amalgamation. If K_{0} is the dimension of the largest simplex you’ll need, you can declare an array of length-K_{0} simplexes and form a smaller one, say of length K, by amalgamating the last (or first) K_{0} - K + 1 elements. An appropriate Dirichlet prior ensures that the amalgamation is simplex-uniform, which is what I want. Here’s a toy example:

data {
int<lower=2> K0;          // length of original simplex
int<lower=1,upper=K0> K;  // length of amalgamated simplex
int<lower=0> x[K];        // multinomial counts
}
parameters {
simplex[K0] gamma;
}
transformed parameters {
vector<lower=0>[K] pi = append_row(head(gamma, K - 1), sum(tail(gamma, K0 - K + 1)));
}
model {
// prior on gamma that implies pi ~ Dir(rep_vector(1,K))
gamma ~ dirichlet(append_row(rep_vector(1, K - 1), rep_vector(1.0/(K0 - K + 1), K0 - K + 1)));

// likelihood
x ~ multinomial(pi);
}

The amalgamated K_{0} - K + 1 elements of \gamma are nonidentified, but bounded because they’re just sampled from the simplex prior. Seems to work fine in practice, and I find it easier to wrap my head around (YMMV).