I’m trying to build a simple DPMM with binomial distributions as the component dists.

However, even Metropolis’ sampling is extremely slow (only like 10draws/s max) with N=200

NUTS, ADVI are also extremely slow.

Is there any reason such a simple model should take so long to sample?

Here is the model code:

```
def stick_breaking(beta):
portion_remaining = tt.concatenate([[1], tt.extra_ops.cumprod(1 - beta)[:-1]])
return beta * portion_remaining
d0=np.concatenate([np.random.binomial(15, .1, size=(100, 1)), np.random.binomial(15, .5, size=(100, 1))])
d1=np.ones(200)*15
with pm.Model() as model:
alpha = pm.Gamma('alpha', 1., 1.)
beta = pm.Beta('beta', 1, alpha, shape=30)
w = pm.Deterministic('w', stick_breaking(beta))
dpmm_comp_mu=pm.Normal('dpmm_comp_mu', 0., 100., shape=30)
visit_rate_like=pm.Mixture(
'visit_rate_like',
w,
pm.Binomial.dist(
p=pm.math.invlogit(dpmm_comp_mu),
n=d1.astype('int32')[:, None]
),
observed=d0.astype('int32')[:, None]
)
with model:
trace=pm.sample(step=pm.Metropolis())
```