Default priors are the same for the centered and non-centered parametrization

In Version: 0.2.4 changing noncentered from True to False does not change priors on random effects:

```python
# Non-centered
cav_data = hssm.load_data("cavanagh_theta")
model = hssm.HSSM(
data=cav_data,
model = 'ddm',
loglik_kind="approx_differentiable",
noncentered = True,
include=[
{
"name": "v",
"formula": "v ~ 1 + theta + (1 + theta | participant_id) ",
"link": "identity",
},
],
)
model
```

```python
# Ccentered
cav_data = hssm.load_data("cavanagh_theta")
model = hssm.HSSM(
data=cav_data,
model = 'ddm',
loglik_kind="approx_differentiable",
noncentered = False,
include=[
{
"name": "v",
"formula": "v ~ 1 + theta + (1 + theta | participant_id) ",
"link": "identity",
},
],
)
model
```