This is a follow-up post where we will see how to apply a normalizing flow model to learn the density of observed data. If you are not familiar with these models I recommend checking out the first part which explains how normalizing flows work and the math behind them. The promise of normalizing flows is that we can learn probability densities over our observations without having to model our entire domain by hand. Read more

Probabilistic models give a rich representation of observed data and allow us to quantify uncertainty, detect outliers, and perform simulations. Classic probabilistic modeling require us to model our domain with conditional probabilities, which is not always feasible. This is particularly true for high-dimensional data such as images or audio. In these scenarios, we would like to learn the data distribution without all the modeling assumptions. normaizing flows is a powerful class of models that allow us to do just that, without resorting to approximations. Read more