I just started looking at Edward. It looks very nice. To those of you who had a hand in creating it, thank you very much!

Unfortunately I am having a problem with making the inferencing work, even in a very simple case. I tried running the following code:

```
import tensorflow as tf
import edward as ed
from edward.models import Normal
print ed.__version__
print tf.__version__
# Generative model
A = Normal(0., 1., name='A')
B = Normal(A, 1., name='B')
C = Normal(A, 1., name='C')
# Variational model
mu = tf.Variable(0., name='mu')
sigma = tf.Variable(1., name='sigma')
qB = Normal(mu, sigma, name='qB')
with tf.Session() as sess:
inference = ed.KLqp({B: qB}, {C: 100.})
inference.run()
print mu.eval(), sigma.eval()
```

The output is

```
1.3.3
1.3.0
1000/1000 [100%] ██████████████████████████████ Elapsed: 1s | Loss: 5024.725
0.0159369 1.0
```

Given that C is observed to be 100, the posterior of B should be centered at 50, isn’t it? Edward does not seem to get anywhere close to figuring this out. (I just posted this particular example but I also tried less extreme ones interactively and the inference result always seemed random.)

I am assuming that I am doing something basic wrong. Please advise. Thank you!