When I try to get prior predictive samples for a simple gaussian intercept model with brms, it appears to ignore priors on `sigma`

. Is this intended? Is there a way to somehow override this behavior? Any insight is appreciated!

This is what I expect from a manual simulation:

```
library(tidyverse)
tibble(
mu = rnorm(1e3, 0, 10),
sigma1 = rexp(1e3, 1),
sigma5 = rexp(1e3, 5)
) |>
mutate(
`exp(1)` = rnorm(mu, sigma1),
`exp(5)` = rnorm(mu, sigma5)
) |>
pivot_longer(starts_with("exp"), names_to = "prior", values_to = "y") |>
ggplot(aes(y, color = prior)) + geom_density()
```

But here’s what I get from brms:

```
library(brms)
options(brms.backend = 'cmdstanr')
m1 <- brm(
y ~ 0 + Intercept,
data = tibble(y = 1),
family = gaussian(),
prior = c(
prior(normal(0, 10), class = "b"),
prior(exponential(1), class = "sigma")
),
sample_prior = "only",
chains = 4,
cores = 4
)
m2 <- brm(
y ~ 0 + Intercept,
data = tibble(y = 1),
family = gaussian(),
prior = c(
prior(normal(0, 10), class = "b"),
prior(exponential(5), class = "sigma")
),
sample_prior = "only",
chains = 4,
cores = 4
)
bind_rows(
posterior_predict(m1, newdata = tibble(y = 1)) |>
as.vector() |>
enframe(value = "y") |>
mutate(prior = "exp(1)"),
posterior_predict(m2, newdata = tibble(y = 1)) |>
as.vector() |>
enframe(value = "y") |>
mutate(prior = "exp(5)")
) |>
ggplot(aes(y, color = prior)) + geom_density()
```

which appears to just be sampling from the `normal(0, 10)`

prior.

I’m running brms 2.18.0, using the cmdstanr 0.5.3 backend on R 4.2.1