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.