Hi,

I am getting NaN errors while trying to fit a very simple model given below using SVGD with 1 particle. Any idea why?

*Model:*

```
x_obs = np.random.normal(loc=10, scale=1.0, size=1000)
with pm.Model() as mdl:
mu = pm.Normal('mu', mu=0.0, sd=1.0)
x = pm.Normal('x', mu=mu, sd=1.0, observed=x_obs)
optmzr = pm.SVGD(n_particles=1)
approx = optmzr.fit()
trace = approx.sample(100)
```

*Error trace:*

```
0%| | 0/10000 [00:00<?, ?it/s]
---------------------------------------------------------------------------
FloatingPointError Traceback (most recent call last)
<ipython-input-17-102169e6999f> in <module>()
4 x = pm.Normal('x', mu=mu, sd=1.0, observed=x_obs)
5 optmzr = pm.SVGD(n_particles=1)
----> 6 approx = optmzr.fit()
7 trace = approx.sample(100)
~/anaconda3/envs/python3.6/lib/python3.6/site-packages/pymc3/variational/inference.py in fit(self, n, score, callbacks, progressbar, **kwargs)
136 state = self._iterate_with_loss(0, n, step_func, progress, callbacks)
137 else:
--> 138 state = self._iterate_without_loss(0, n, step_func, progress, callbacks)
139
140 # hack to allow pm.fit() access to loss hist
~/anaconda3/envs/python3.6/lib/python3.6/site-packages/pymc3/variational/inference.py in _iterate_without_loss(self, s, _, step_func, progress, callbacks)
169 except IndexError:
170 pass
--> 171 raise FloatingPointError('\n'.join(errmsg))
172 for callback in callbacks:
173 callback(self.approx, None, i+s+1)
FloatingPointError: NaN occurred in optimization.
The current approximation of RV `mu`.ravel()[0] is NaN.
Try tracking this parameter: http://docs.pymc.io/notebooks/variational_api_quickstart.html#Tracking-parameters
```

Thanks!