  Variational inference methods often focus on the problem of efficient model
optimization, with little emphasis on the choice of the approximating
posterior. In this paper, we review and implement the various methods that
enable us to develop a rich family of approximating posteriors. We show that
one particular method employing transformations on distributions results in
developing very rich and complex posterior approximation. We analyze its
performance on the MNIST dataset by implementing with a Variational Autoencoder
and demonstrate its effectiveness in learning better posterior distributions.
