This isn’t a problem with my code, but a question of how brms specifies default priors… most of the documentation I’ve read states brms uses flat priors when priors are not specified via set_prior(). But what is not clear to me, is what is meant by a “flat prior”? Surely there’s some distribution being implemented, even if it is a very wide interval (e.g. normal(-10, 10)).

So, say I’ve constructed a model:

```
odhe.m1 <- brm(noct ~ HOMO.n.day ,
family=gaussian,
save_pars = save_pars(all = TRUE),
iter=10000,
warmup=5000,
cores=detectCores(),
data=ODHE)
```

Where “noct” and “HOMO.n.day” are both continuous numeric variables, normally distributed, etc. When I run the following, since I’m not specifying any prior distributions for the variable “HOMO.n.day”, I see the distribution is “flat” :

```
get_prior(noct ~ HOMO.n.day, data=ODHE)
prior class coef group resp dpar nlpar bound source
(flat) b default
(flat) b HOMO.n.day (vectorized)
student_t(3, 20527, 11672.5) Intercept default
student_t(3, 0, 11672.5) sigma default
```

I’d just like to know if there’s a way to identify the prior which is actually being used in constructing this model. What is “flat” here? Is there a way to explain this numerically?

Any feedback on this would be much appreciated.

Cheers!