  Density estimation is an interdisciplinary topic at the intersection of
statistics, theoretical computer science and machine learning. We review some
old and new techniques for bounding the sample complexity of estimating
densities of continuous distributions, focusing on the class of mixtures of
Gaussians and its subclasses. In particular, we review the main techniques used
to prove the new sample complexity bounds for mixtures of Gaussians by
Ashtiani, Ben-David, Harvey, Liaw, Mehrabian, and Plan arXiv:1710.05209.
