Hi, thank you for the work of this great package! I have a question regarding inference API.

According to the inference tutorial, inference API is as simple as running one method:

`inference = ed.Inference({z: qz, beta: qbeta}, {x: x_train})`

To my knowledge, to optimize ELBO, variational inference requires defining the family of variational distribution **q(z; lambda)** and joint likelihood **p(x, z; theta)**. Does Edward assumes implicitly that both are normal distribution? If so, can we override the assumption and pass for example a non-Gaussian joint likelihood function **p(x, z; theta)** instead?

Thanks,

Shan