Hello All! I’m new to Edward (which is awesome, thanks!) and so apologies if this is a simple question.

I am trying to convert some existing models from MCMC (I have been using `pymc`

) to variationaI Bayes, and I’m struggling to figure out the way that prior distributions are used in `KLqp`

. A simple example is the inference of the input to a model from an observation of the output, shown below.

```
import edward as ed
import tensorflow as tf
sess = ed.get_session()
# a simple model function
def mfunc(x):
return x**2.
# data to be fit
xd = 0.25
yd = mfunc(xd)
# input point to be inferred is known to be positive
X = ed.models.Uniform(low=0.,high=1.,name="X")
y = tf.identity(mfunc(X),name="y")
# the likelihood
yobs = ed.models.Normal( loc=y, scale=0.01, name="yobs" )
```

Clearly there are two solutions and my intention is that the prior on `X`

removes the negative one. In the real application, the model function is a surrogate for a complex simulation which has been trained on a limited domain; I need to make sure that the model is not evaluated outside of that. The above works as expected with `MetropolisHastings`

but with `KLqp`

I get the negative answer;

```
# the variational model
xmuq = tf.Variable(0.5, name="xmu_post")
xscq = tf.nn.softplus(tf.Variable(1.,name="xsc_post_nt"), name="xsc_post")
xq = ed.models.Normal( loc=xmuq, scale=0.01*xscq, name="x_post" )
inference = ed.KLqp( {X:xq}, data={yobs: yd})
inference.run(n_samples=10, n_iter=20000,logdir='log')
print sess.run([xmuq,xscq])
20000/20000 [100%] ██████████████████████████████ Elapsed: 123s | Loss: inf
[-0.24771848, 2.0027606]
```

I’m guessing that the loss function being `inf`

reflects the fact that `X`

is outside of it’s domain, so the question is; **how do I restrict the KLqp inference to respect the prior I have defined?**

Thanks, in advance,

Jim