In my data set there are D number of observations. Each observation is an N dimensional binary vector.

I want to represent this data as a K mixture of N-D Binomial distributions.

```
model = pm.Model()
K = 4
N = 8
with model:
w = pm.Dirichlet('w', a = np.ones(K), shape = K)
p_ = pm.Dirichlet('p_', a = np.ones((K, N)), shape = (K, N))
components = []
for k in range(K):
components.append(pm.Bernoulli.dist(p = p_[k]))
like = pm.Mixture('like', w=w, comp_dists = components, observed = data)
```

I get the following error.

`ValueError: Input dimension mis-match. (input[0].shape[2] = 4, input[1].shape[2] = 8)`

How can I fix this?

Also any way I could avoid the for loop please?

thanks