Ragged array of simplexes

Modeling
22

Thanks again Ben! So I think I’ve managed to incorporate your feedback into a model (that compiles). I’m pasting it below for anybody else who has the same question.

My remaining question is how to integrate the shape parameter like you’ve mentioned. As you point out, this example code is using a uniform distribution, but I really need to modulate the shape. Do I need an additional variable in transformed parameters or am I supposed to do some kind of initialization of the zeta?

data {
   int<lower=1> M;               // num observations
   int<lower=1> N;                // number of sub-observations for all observations
   real<lower=0> y[N];               //Observations where each row is a subobservation
   int<lower=1,upper=M> observation[N];    //observation id for subobservation n
   int<lower=1> row_to_subobservation_map[N];      //subobservation index for each row
   int<lower=1> count_of_subobservations_per_observation[M];
}

parameters {
   vector<lower=0,upper=1.0>[N] zeta;      // subobservation dist over observation
} 

model {
   
   for (n in 1:N) {
      
      int zeta_mark = n-row_to_subobservation_map[n]+1;
      int nc = count_of_subobservations_per_observation[observation[n]];
      
      vector[nc] pi = segment(zeta,zeta_mark,nc);  // gamma(zeta | shape, 1)
      pi = pi / sum(pi);                           // thus dirichlet(pi | shape)

      target += log(y[n]*pi[row_to_subobservation_map[n]]); // likelihood

  }
}
1 Like