I’m running HMC to learn a normal distribution, and conditioning on a categorical distribution. A very simplified example of my model is as follows:

```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import numpy as np
import tensorflow as tf
from edward.models import Empirical, Normal, Categorical
from edward.inferences import HMC
mu = Normal(mu=0.0, sigma=1.0)
x = Normal(mu=tf.ones(50) * mu, sigma=1.0)
cat = Categorical(p=x)
observed = 3
qmu = Empirical(params=tf.Variable(tf.zeros([1000])))
inference = ed.HMC({mu: qmu},data={cat: observed})
inference.run()
sess = ed.get_session()
print(sess.run(qmu.params))
```

The acceptance rate is 0, and as a result, I end up with an empirical distribution of 0s for qmu, after inference. I assume this is a conceptual problem, not a coding one, but I’m not quite sure what I’m doing wrong.

Thanks!

Reuben