Hi everyone,

Looking at the tutorial on VAEs (Variational Autoencoders β Pyro Tutorials 1.8.4 documentation) it appears to me strange why the data is not binarized, even though the calculation of the likelihood of the image is supposed to be with respect to generation by a bernoulli distribution.

```
# define the model p(x|z)p(z)
def model(self, x):
# register PyTorch module `decoder` with Pyro
pyro.module("decoder", self.decoder)
with pyro.iarange("data", x.size(0)):
# setup hyperparameters for prior p(z)
z_loc = x.new_zeros(torch.Size((x.size(0), self.z_dim)))
z_scale = x.new_ones(torch.Size((x.size(0), self.z_dim)))
# sample from prior (value will be sampled by guide when computing the ELBO)
z = pyro.sample("latent", dist.Normal(z_loc, z_scale).independent(1))
# decode the latent code z
loc_img = self.decoder.forward(z)
# score against actual images
pyro.sample("obs", dist.Bernoulli(loc_img).independent(1), obs=x.reshape(-1, 784))
```

How is the likelihood calculated when the observation is not 0 or 1?, what is it actually calculating?.

I also noticed that results are better at digit generation when binarization is not done, even though the elbo is better when doing binarization.