  This note compares two recently published machine learning methods for
constructing flexible, but tractable families of variational hidden-variable
posteriors. The first method, called "hierarchical variational models" enriches
the inference model with an extra variable, while the other, called "auxiliary
deep generative models", enriches the generative model instead. We conclude
that the two methods are mathematically equivalent.
