I’m trying to understand how is the best way to set priors for group effects taking into account the variation between them, because I’m a bit confused about the class = “sd”. I have the following case where I want to estimate Units sold using Price which varies by Region (North, South, East, West), using the following :

```
"Units ~ 1 + Price + (0 + Price | Region)"
```

Let’s assume that I have knowledge and want the estimates for each region to be around those values :

- North : 0.6
- South : 1.1
- East : 2.1
- West : 0.05

What would be the best way to define the model priors in order to define the variation among groups since in I can’t specify seperate priors for each beta coefficient of every Region? Would it best captured by something like :

```
set_prior(normal(Region_avg, Region_sd), class = "b", coef = "Price")
```

or also I need to add a prior for the sd class where I also account for the variation?

```
set_prior(student_t(3, 0, Region_sd)), class = "sd", coef = "Price", group = "Region")
```