Hello,

I am currently working with the following spline model:

```
with pm.Model() as model:
b = patsy.dmatrix('cc(ALPHA, knots=knots)', {'ALPHA': df['ALPHA'], 'knots': np.arange(30,360,30)})
model.add_coord('spline', length=b.shape[1], mutable=True)
model.add_coord('angle', df['ALPHA'], mutable=True)
data_b = pm.MutableData('data_b', np.asarray(b), dims=('angle', 'spline'))
tau = pm.HalfCauchy('tau', 20)
beta = pm.Normal('beta', mu=0, sigma=tau, dims='spline')
mu = pm.Deterministic('mu', pm.math.dot(data_b, beta), dims='angle')
sigma = pm.HalfNormal('sigma', 0.01)
pm.Normal('force_obs', mu=mu, sigma=sigma, observed=df['Force'], dims='angle')
```

Up to the step of `sample()`

everything is fine. I plotted the `mu`

of the posterior after averaging over the chains and draws which yields very reasonable results with respect to the observed variable.

Meanwhile the results yielded by plotting the posterior_predictive after using `sample_posterior_predictive()`

appear very poor fluctuating around 0. That surprises me as actually only a tiny `sigma`

should be added to the otherwise good result of `mu`

.

Does someone have an idea where this problem stems from? Weird enough, when executing `sample_posterior_predictive()`

it shows me that not only the observed variable is sampled but also `beta`

(although `beta`

is already present in the posterior). I suppose this might be related to the issue.