 # Vectorizing sampling of arrays of vectors

I have an array of vectors as parameters, and I’m wondering how to vectorize setting the priors of the parameters. Below is example code which illustrates the problem I’m facing.


data {
int<lower=0> N;
vector y[N];

cov_matrix SD[N];
}

parameters{
vector example_mu[N];

}

model{
vector filler;

vector m[N];
vector s[N];

filler = 1;
filler = 2;
filler = 3;

m = rep_array(filler,N);

filler = 2;
filler = 3;
filler = 4;

s = rep_array(filler,N);

// this isn't allowed but I want to vectorize my vector operations
example_mu ~ normal(m,s);

// otherwise I'd have to loop through like this:
for(i in 1:N){
example_mu[i] ~ normal(m[i], s[i]);
}

// what other options do I have??

y ~ multi_normal(example,SD);

}



I don’t think there is a lot you can do differently because you need an array for the multi_normal function. Your approach is vectorizing each vector in the array. I think the most important benefit from vectorizing comes from reusing values. So even if example_mu ~ normal(m, s); you would not get the full benefits of vectorizing because m[i] <> m[j].

I think a big part of the problem is that the for loops in sampling add a lot of time. I think if I was able to make all the sampling into one vectorized operation then it only takes the gradient once of that vector vs. in the for loop it is taking N gradients which is a lot more computationally expensive. I tried out my actual model out with the flor loop and it took excessively long. I think that it won’t be possible to actually use that model unless I find a way to get rid of the for loop.

If you declare the parameters as a matrix like

parameters {
matrix[N, 3] example_mu;
}


then you can use the to_vector function in the model block like

model {
to_vector(example_mu) ~ normal(...);
}


but the arguments to normal have to either be scalars or vectors that are N \times 3.

Ok that makes sense, but can you have the standard deviation argument be a N x 3 matrix and will stan know that (for example) the (4,2) nd entry of that is the covariance between example_mu and example_mu ?

No, if you are using normal (as opposed to one of the multi_normal variants), it assumes the covariances are zero.

1 Like

Probably good to clarify. The original model has two log likelihood statements

to_vector(example_mu) ~ normal(m, s);
y ~ multi_normal(example_mu, SD);


The first line has no correlations, the second line has the corrrelations implied by SD.

Unfortunately, this is not going to work because example_mu is a matrix and multi_normal needs an array of vectors. I think you will need to make a new example_mu_array and copy the vectors from the matrix in the array in a for loop.