I have a Turing model that I’m using to estimate how evenly distributed some count variables of interest are. I’m using the concentration parameter of a symmetric Dirichlet distribution as a measure of evenness. A simplified version of the model (removing hierarchical structure, treatment groups, repeated measures) is as follows:
@model function Mod(data) alpha ~ truncated(Normal (1,20),0,Inf) probs ~ Dirichlet (5, alpha) data ~ Multinomial(sum(data),probs) end
Is there a way to avoid sampling “probs” at each iteration, given that Dirichlet is conjugate to multinomial. But still sample alpha and the rest of the model (not shown, for simplicity)?
I am happy to try non-Turing solutions.