Hi All,

I am working on a multi-level model with a vector of parameters that are random slopes, and I would like to assign a prior to the distribution of these parameters. Whenever I run the model, the distribution of the mean estimates for these random effect parameters ends up being normal, but I have an *a priori* reason to expect that they would be non-negative with a positive skew. So, is it possible to treat this expectation as a prior on the distribution of parameters (or at least their estimated means)?

When I specify the parameter, I use:

```
parameters {
vector[Num_levels] theta;
}
```

However, specifying a distribution in the model block, like `theta ~ normal(0, 1);`

or `theta ~ chi_square(1);`

only affects the distribution of the estimates for each individual parameter in the vector, not the distribution of parameters across the vector. The best I can do is to specify the lower bound of the vector, ie

```
parameters{
vector<lower=0>[Num_levels] theta;
}
```

But this is unsatisfying, as the distribution is still normal, and it is more of a constraint than a prior. Is there a way to specify the distribution of parameter means in a vector of parameters as a hyperparameter?