Continuing the discussion from Using _lupmf for multivariate likelihood in reduce_sum:

Hello Stanimals! When attempting to use `reduce_sum`

with multivariate likelihood, it appears that if I pass in a `dummy`

variable for the first argument, nothing happens in the update?

I will post a full example (in the `model`

section, I have commented out what the model *should* do and what the `reduce_sum`

is attempting to parallelize).

The full run with some data take a few minutes, but when I compile the `reduce_sum`

version and run it, it runs almost instantly (but as I said, no update to `weights`

seems to happen).

Can anyone see anything egregiously wrong with what I am doing?

Function:

```
functions {
real partial_sum_likelihood_lpmf(
data int [] dummy_slice,
int start,
int end,
vector omega,
data int [,] selected_indicies,
data int [] num_selected_events_this_block,
data int [,] available_sku_indicies_this_block,
data int [] number_available_skus_this_block
)
{
real ret_val = 0.;
for (n in start:end) {
ret_val += categorical_logit_lupmf(
selected_indicies[n,1:num_selected_events_this_block[n]] |
omega[available_sku_indicies_this_block[n,1:number_available_skus_this_block[n]]]
);
}
return ret_val;
}
}
data {
int<lower=1> num_blocks;
int<lower=1> num_skus;
int<lower=1> max_number_selections_per_block;
int<lower=0, upper=num_skus> available_sku_indicies_this_block[num_blocks, num_skus]; // padded with zeros
int<lower=1> number_available_skus_this_block[num_blocks];
int<lower=1> total_selections_this_block[num_blocks];
int<lower=0,upper=num_skus> selected_indicies[num_blocks, max_number_selections_per_block]; //padded with zeros
}
transformed data {
int<lower=1> dummy_slice[0];
int grainsize=50;
}
parameters {
vector[num_skus] log_weights;
}
transformed parameters {
simplex[num_skus] weights = softmax(log_weights);
}
model {
log_weights ~ std_normal();
target += reduce_sum(
partial_sum_likelihood_lpmf,
dummy_slice,
grainsize,
weights,
selected_indicies,
total_selections_this_block,
available_sku_indicies_this_block,
number_available_skus_this_block
);
/*for (n in 1:num_blocks) {
target += categorical_logit_lupmf(
selected_indicies[n,1:total_selections_this_block[n]] |
weights[available_sku_indicies_this_block[n,1:number_available_skus_this_block[n]]]
);
}*/
}
```