Minimum working Example:

```
import pandas as pd
import pymc3 as pm
foxes = pd.read_csv('https://github.com/rmcelreath/rethinking/raw/master/data/foxes.csv', sep=';')
with pm.Model() as mdl:
a = pm.Normal('a', mu=0, sd=0.25)
b = pm.Normal('b', mu=0, sd=0.4)
mu = pm.Deterministic('mu', a + b * foxes.area.values.reshape(-1, 1))
sigma = pm.Exponential('sigma', lam=1)
weight = pm.Normal('weight', mu=mu, sd=sigma, observed=foxes.weight)
prior = pm.sample_prior_predictive()
{k: prior[k].shape for k in prior.keys()}
```

As output I get:

```
{'a': (500,),
'weight': (500, 116, 116),
'mu': (500, 116, 1),
'sigma': (500,),
'b': (500,),
'sigma_log__': (500,)}
```

For â€śweightâ€ť, I would have expected a shape of (500, 116), but instead get (500, 116, 116). Am I misunderstanding something with what sample_prior_predictive should be doing?