From Deep Mixtures to Deep Quantiles - Part 2 - 2019-02-24

In which we (attempt to) speed up sampling from a mixture density model

In Part 1 of this series of posts, we trained a Mixture Density Network to capture a heteroscedastic conditional probability distribution.

After predicting the parameters $(w_{ij}, \mu_{ij}, \sigma_{ij})$ of the $m$ mixture components, we would like to generate some - and in some cases many - samples from the learned distribution.

A fully vectorized implementation for the problem at hand required some thought, so I decided to write it up for future reference.