In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions. Explicitly defining the structure of the approximation allows to make the variational lower bound tighter, resulting in more accurate inference.
Utilizing the structure of distributions improves semi-implicit variational inference