Hi all,

I have a quick question about assigning priors to a vector of values. Say I have a parameterization:

```
parameters {
real mu
vector[N] mu_staff
...
}
model {
...
mu ~ normal(0, 1)
mu_staff ~ normal(mu, ...)
}
```

I understand the case above as a multilevel model, where elements of `mu_staff`

are different values that depend the same `normal(mu)`

distribution.

What if I parameterized my model this way?

```
parameters {
vector[N] mu_staff
...
}
model {
...
mu_staff ~ normal(10, ...)
}
```

Does the assignment here vectorize? More specifically, does each value of `mu_staff`

come from its own `normal(10)`

prior, i.e. N separate priors for each element of `mu_staff`

? Or does it denote a multilevel model i.e. all elements of `mu_staff`

come from the same `normal(10)`

distribution like the above?

I dug through the manual but couldnâ€™t find a good answer. Hope what Iâ€™m asking makes sense!