Understanding Kernel Density Estimation

Matthew Conlen explains KDE well with a dynamic visualization , worth referring to. Origin KDE is a very important concept. It&rsquo;s particularly useful when the true underlying distribution of data is uncertain and likely doesn&rsquo;t follow a standard distribution (like a normal distribution). Let&rsquo;s use the data from Gaussian Mixture Distribution as an example: import numpy as np import…