Properly sampling mixture models

Questions
8

Seems to run fine for me:

NumberOfFeatures = 5
NumberOfClusters = 3
y = np.random.randn(1000, NumberOfFeatures)

with pm.Model() as Model:
    # Create a covariance matrix for each potential cluster which relates all features of our data
    Lower = tt.stack([pm.LKJCholeskyCov('Sigma_{}'.format(k), n=NumberOfFeatures, eta=2.,
                                        sd_dist=pm.HalfNormal.dist(sd=1.)) for k in range(NumberOfClusters)])
    Chol = tt.stack([pm.expand_packed_triangular(
        NumberOfFeatures, Lower[k]) for k in range(NumberOfClusters)])

    # The center of each cluster
    Mus = tt.stack([pm.Uniform('Mu_{}'.format(k), lower=0., upper=1.,
                               shape=NumberOfFeatures) for k in range(NumberOfClusters)])

    # Create the multivariate normal distribution for each cluster
    MultivariateNormals = [pm.MvNormal.dist(
        Mus[k], chol=Chol[k], shape=NumberOfFeatures) for k in range(NumberOfClusters)]

    # Create the weights for each cluster which measures how much impact they have
    Weights = pm.Dirichlet('w', np.ones(NumberOfClusters) / NumberOfClusters)

    Prob = pm.Mixture("prob", w=Weights, comp_dists=MultivariateNormals,
                      observed=y)
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