Normalizing Flows Tutorial, Part 1: Distributions and Determinants

Generate new data "for free" by sampling from the learned distribution in silico; no need to run the true generative process for the data. This is a useful tool if the data is expensive to generate, i.e. a real-world experiment that takes a long time to run [1]. Sampling is also used to construct estimators of high-dimensional integrals over spaces. Evaluate the likelihood of data observed at test time (this can be used for rejection sampling or to score how good our model is). Find the conditional relationship between variables. For example, learning the distribution $p(x_2 x_1)$ allows us to build discriminative classification or regression models.