I am very new to PYMC (and probabilistic programming) and I’m trying to follow a tutorial made for Edward:

https://www.ritchievink.com/blog/2018/06/05/clustering-data-with-dirichlet-mixtures-in-edward-and-pymc3/

```
k = 3 # number of clusters
d = df.shape[1]
n = df.shape[0]
pi = ed.models.Dirichlet(tf.ones(k))
mu = ed.models.Normal(tf.zeros(d), tf.ones(d), sample_shape=k) # shape (3, 4) 3 gaussians, 4 variates
sigmasq = ed.models.InverseGamma(tf.ones(d), tf.ones(d), sample_shape=k)
x = ed.models.ParamMixture(pi, {'loc': mu, 'scale_diag': tf.sqrt(sigmasq)},
ed.models.MultivariateNormalDiag,
sample_shape=n)
z = x.cat
```

I especially do not understand how to create those 2D shaped Distributions. (As far as I understand those are not multivariate, or am I wrong?)

```
t = 500 # number of samples
qpi = ed.models.Empirical(tf.get_variable('qpi', shape=[t, k], initializer=tf.constant_initializer(1 / k)))
qmu = ed.models.Empirical(tf.get_variable('qmu', shape=[t, k, d], initializer=tf.zeros_initializer()))
qsigmasq = ed.models.Empirical(tf.get_variable('qsigmasq', shape=[t, k, d], initializer=tf.ones_initializer()))
qz = ed.models.Empirical(tf.get_variable('qz', shape=[t, n], initializer=tf.zeros_initializer(), dtype=tf.int32))
inference = ed.Gibbs({pi: qpi, mu: qmu, sigmasq: qsigmasq, z: qz},
data={x: y})
```

How would the inference in pymc look like?

I hope that’s not too much to ask. It would help my learning process a lot.