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.shape = 4, input.shape = 8)
How can I fix this?
Also any way I could avoid the for loop please?