Hey all–my apologies for the newbie question, but I’m having some trouble setting up even a pretty basic model using HMC. Here’s the code I’m having trouble with:

```
import numpy as np
import tensorflow as tf
from edward.models import Normal, Empirical
import edward as ed
X_train = np.array([[ 0.87024415, 0.65901989],
[ 0.09892046, 0.92157853],
[ 0.34324086, 0.7804544 ],
[ 0.19976632, 0.90515983],
[ 0.75492591, 0.50158632],
[ 0.41315702, 0.63009453],
[ 0.40734398, 0.74817169],
[ 0.85471708, 0.69424045],
[ 0.14578448, 0.95430565],
[ 0.51036876, 0.63925266]], dtype=np.float32)
y_train = np.array([[-0.1065 ],
[-0.005 ],
[ 0.0488 ],
[ 0.0714 ],
[-0.0192 ],
[ 0.0944 ],
[ 0. ],
[-0.0888 ],
[ 0.0392 ],
[-0.73610002]], dtype=np.float32)
layer_1_size = 10
W_0 = Normal(loc = tf.zeros([2,layer_1_size]), scale = tf.ones([2,layer_1_size]))
W_1 = Normal(loc = tf.zeros([layer_1_size,1]), scale = tf.ones([layer_1_size,1]))
b_0 = Normal(loc = tf.zeros(layer_1_size), scale = tf.ones(layer_1_size))
b_1 = Normal(loc = tf.zeros(1), scale = tf.ones(1))
x = tf.placeholder(dtype=np.float32)
y = Normal(loc = tf.matmul(tf.tanh(tf.matmul(x, W_0) + b_0), W_1) + b_1,
scale = 0.1)
T = 10000
ydata = tf.constant(np.repeat([y_train],T,axis=0))
qy = Empirical(params = ydata)
inference = ed.HMC({y: qy}, data = {x: X_train})
inference.run(n_iter=10000)
```

When I run that, I get:

```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-122-8793699b40bc> in <module>()
----> 1 inference.run(n_iter=10000)
/home/matthew/anaconda3/envs/edward/lib/python3.6/site-packages/edward/inferences/inference.py in run(self, variables, use_coordinator, *args, **kwargs)
114 Passed into ``initialize``.
115 """
--> 116 self.initialize(*args, **kwargs)
117
118 if variables is None:
/home/matthew/anaconda3/envs/edward/lib/python3.6/site-packages/edward/inferences/hmc.py in initialize(self, step_size, n_steps, *args, **kwargs)
62 self.n_steps = n_steps
63 self.scope_iter = 0 # a convenient counter for log joint calculations
---> 64 return super(HMC, self).initialize(*args, **kwargs)
65
66 def build_update(self):
/home/matthew/anaconda3/envs/edward/lib/python3.6/site-packages/edward/inferences/monte_carlo.py in initialize(self, *args, **kwargs)
96 self.n_accept = tf.Variable(0, trainable=False, name="n_accept")
97 self.n_accept_over_t = self.n_accept / self.t
---> 98 self.train = self.build_update()
99
100 def update(self, feed_dict=None):
/home/matthew/anaconda3/envs/edward/lib/python3.6/site-packages/edward/inferences/hmc.py in build_update(self)
113 assign_ops = []
114 for z, qz in six.iteritems(self.latent_vars):
--> 115 variable = qz.get_variables()[0]
116 assign_ops.append(tf.scatter_update(variable, self.t, sample[z]))
117
IndexError: list index out of range
```

I have no idea what list is even being accessed here. All the dimensions seem right but maybe I’ve got something wrong:

```
ydata
<tf.Tensor 'Const_112:0' shape=(10000, 10, 1) dtype=float32>
qy
<ed.RandomVariable 'Empirical_74/' shape=(10, 1) dtype=float32>
```

Incidentally, is my understanding of `Empirical`

correct that I should be setting up a `tf.constant`

tensor with the number of desired samples as the first dimension, with the target data repeated for each one?

Thanks very much for any assistance you can provide!

Matt