I want to fit a model where the prior for the parameter mu is bounded from below by 0. This can be done like this in rstan:

```
"data {
int N;
real y[N]; // data
}
parameters {
real<lower=0> mu;
real<lower=0> sigma;
}
model {
// priors
mu ~ normal(0,2000);
sigma ~ normal(0,500);
// likelihood
y ~ normal(mu,sigma);
}
generated quantities {
vector[N] y_sim;
// posterior predictive
for(i in 1:N) {
y_sim[i] = normal_rng(mu,sigma);
}
}
```

I generated fake data to fit this model in brms:

```
set.seed(123)
N <- 500
true_mu <- 400
true_sigma <- 125
y <- rnorm(N, true_mu, true_sigma)
y <- round(y)
fake_data <- data.frame(y=y)
```

Then I tried to bound the intercept from below in brms:

```
priors <- c(set_prior("normal(0, 2000)",
class = "Intercept",lb=0),
set_prior("normal(0, 500)",
class = "sigma"))
```

I get the error:

Error: Currently boundaries are only allowed for population-level and autocorrelation parameters.

The intercept is surely a population level parameter? Is this a bug?