Hi all,

I am fitting a logistic regression model with some parameters modelled as random effects with what I hope is a non-centered parameterization. I don’t get any divergences and I get ok ESS values. However, the posterior distributions of the parameters look odd to me.

Here are the relevant lines for one of the hierarchical parameters in the model:

```
.
.
.
family_sigma = pm.HalfNormal("family_sigma", 1.5)
family_raw = pm.Normal("family_raw", 0.0, 1.0, shape=df.family.nunique())
a_family = pm.Deterministic("family", family_raw * family_sigma)
family = pm.intX(df.family)
mu = intercept + a_family[family] + ...
```

Here are some trace plots:

Firstly, I’m wondering about the posterior distribution of the sigma parameter. It doesn’t look like a half-normal distribution anymore but I guess that could be fine since the posterior is a mix of the prior and the likelihood. Secondly, some of the distributions of the family parameter are really skewed, e.g. the blue and green ones.

My questions are if there is anything wrong with how I’m implementing the non-centered parameterization and if not what could be the reason for seeing these weird distribution shapes. Is this something that can be expected when multiplying the `family_raw`

and the `family_sigma`

parameters or should I try to respecify the model?

Thanks in advance!