I have a very simple weather model. Dataset is the 250 temperature samples from `N(55.,1.)`

Latent variable is the probability of Cloudy.

After training the Guide distribution is always similar to prior: `Beta(10, 20)`

. Anytime I change Prior it will always converge to these values. It does not take into account conditional distribution (observations).

It is clear that if I have 250 obs of temperature 55 it should be Cloudy. I have provided an example of when I change params I get much better ELBO.

Can you please suggest where is a bug?

```
def weather():
# Prior
alpha0 = torch.tensor(10.0)
beta0 = torch.tensor(20.0)
prob_cloudy = pyro.sample("prob_cloudy", dist.Beta(alpha0, beta0))
cloudy = pyro.distributions.Bernoulli(prob_cloudy).sample()
cloudy = 'cloudy' if cloudy.item() == 1.0 else 'sunny'
mean_temp = {'cloudy': 55.0, 'sunny': 75.0}[cloudy]
scale_temp = {'cloudy': 10.0, 'sunny': 15.0}[cloudy]
with pyro.plate('observe_data'):
pyro.sample('temp', pyro.distributions.Normal(mean_temp, scale_temp))
def weather_guide():
alpha0 = pyro.param("alpha0", torch.tensor(10.0), constraint=constraints.positive)
beta0 = pyro.param("beta0", torch.tensor(1.0), constraint=constraints.positive)
prob_cloudy = pyro.sample("prob_cloudy", dist.Beta(alpha0, beta0))
pyro.clear_param_store()
# prepare data
obs = pyro.distributions.Normal(55., 1.).sample([250])
conditioned_weather = pyro.condition(weather, data={"temp": obs})
adam = Adam({"lr": 0.0005, "betas": (0.90, 0.999)})
svi = SVI(conditioned_weather, weather_guide, adam, loss=Trace_ELBO())
for _ in range(5000):
svi.step()
```

It is easy to verify that ELBO is less with different Guide parameters:

```
pyro.get_param_store()['alpha0'] = torch.tensor(10.)
pyro.get_param_store()['beta0'] = torch.tensor(30.)
loss = []
elbo = Trace_ELBO()
for i in range(1000):
loss.append(elbo.loss(conditioned_weather, weather_guide_2))
print(np.mean(loss)) # 1041
pyro.get_param_store()['alpha0'] = torch.tensor(10.)
pyro.get_param_store()['beta0'] = torch.tensor(1.)
loss = []
elbo = Trace_ELBO()
for i in range(1000):
loss.append(elbo.loss(conditioned_weather, weather_guide_2))
print(np.mean(loss)) # 875
```