As far as I can tell there’s very little difference between the following two prior structures on the non centred parametrisation for a hierarchical effect:

```
parameters {
vector[NumGrp] grp_raw;
real<lower=0> grp_sigma;
}
model {
grp_eff[grpID] = grp_raw[grpID] * grp_sigma;
#Possible prior structure 1
grp_raw ~ normal(0,1);
grp_sigma ~ normal(0,0.5);
#Possible prior structure 2
grp_raw ~ normal(0,0.5);
grp_sigma ~ normal(0,1);
}
```

I appreciate NCP can help with convergence issues but I’m not sure in the value or difference in attributing more prior variance to the `sigma`

component of the NCP versus the `raw`

component.

That said, sometimes when I run my model the `raw`

component will exhibit results that suggest that my prior variance was smaller than the likely value and at the same time the `sigma`

values will suggest my prior value was likely too big.

This seems counter to my original interpretation that it doesn’t really matter in which parameter the group variance is expressed.

Is my original interpretation correct? If not, any guidance on this would be much appreciated.