This video gives a brief, graphical introduction to kernel density estimation. Many plots are shown, all created using Python and the KDEpy library (https://github.com/tommyod/KDEpy). A .pdf of the presentation may be found here: https://github.com/tommyod/KDEpy/blob...

Contents
00:22 What is kernel density estimation?
01:27 Kernel functions
03:27 Bandwidth
04:30 Silverman's rule of thumb
05:19 Improved Sheather Jones
06:10 Weighting the data
07:30 Bounded domains and reflections
09:18 Kernel density estimation in higher dimensions
10:02 The choice of norm
11:11 Example of 2D kernel density estimation
12:36 A fast algorithm using linear binning and convolution
15:30 2D linear binning
16:18 KDEpy software for kernel density estimation in Python
16:51 References