In the VAE tutorial, the observed images in the mini-batch `x`

are scored against the Bernoulli likelihood parametrized by `loc_img`

:

```
pyro.sample("obs", dist.Bernoulli(loc_img).to_event(1), obs=x.reshape(-1, 784))
```

However, X is not either 0 or 1. X is not discrete; it is continuous. It could have any value in [0, 1] interval. Isn’t it an error to make this assumption? Shouldn’t the sample happen from a continuous distribution in the [0, 1] interval centered in `loc_img`

?

From D. Kingma, M. Welling (2014):

“We let pθ(x|z) be a multivariate Gaussian (in case of real-valued data) or Bernoulli (in case of binary data)…”. … however, in this case the data is not binary. Why then the tutorial uses Bernoulli sample?

Thanks!