Efficient way to fill up matrix of log-likelihoods

Modeling
1

Filling up matrix M with log-likelihoods with two loops as follows, where Y and X are both vectors, and r is a real.

matrix[N,J] M;
for(i in 1:N){
  for(j in 1:J){
    M[i,j] = neg_binomial_2_lpmf(Y[i] | X[j], r);
   }
}

How could this be made more efficient by vectorising?

2 
transformed data{
  array[N*J] y_indices ;
  array[N*J] x_indices ;
  int k = 0 ;
  for(i in 1:N){
    for(j in 1:J){
      k += 1 ;
      y_indices[k] = i ;
      x_indices[k] = j ;
     }
  }
}
...
model{
  matrix[N,J] M = matrix( neg_binomial_2_lpmf( Y[y_indices], X[x_indices], r ), N , J) ;
}
1 Like
3

Would that work? Wouldn’t:

neg_binomial_2_lpmf( Y[y_indices], X[x_indices], r )

Just return a single scalar?

4

Bah, yes it would. 🤦‍♂️ Forgot the lpmfs do that.

5

Can I clarify the way _lmpf functions work as I can’t find any documentation on this. When the observations are given as a vector, does the function return the sum of the log probabilities?

6

Yep, the lpmf and lupmf functions will always return a single scalar, summing if any of the inputs are vectorised