Hi,

I’m trying to get a basic latent variable model working so that I can develop it into something more complicated. Here’s what I’ve got:

```
# Data
data = tf.random_normal(shape=[n], mean=90, stddev=3).eval()
# Model
mu = Normal(0., 1.)
sigma_2 = Normal(0., 1.)
y = Normal(tf.ones(n) * mu, tf.ones(n) * sigma_2)
# Inference
q_mu = Empirical(tf.Variable(tf.zeros(500)))
q_sigma_2 = Empirical(tf.Variable(tf.zeros(500)))
inference = ed.HMC({mu: q_mu, sigma_2: q_sigma_2}, data={y: data})
inference.run(step_size=0.1)
```

I’ve tried step sizes ranging from 100 to 0.0000000001 and no luck — the acceptance rate is never sensible (always 0 or 1) and trying to sample from `q_mu`

or `q_sigma_2`

returns `nan`

.

Does anyone know what I’m doing wrong here?

Thanks!