Kernel Density Estimation (KDE) - cpshooter/geoML GitHub Wiki

In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample.

This is called a Parzen window density estimator, or kernel density estimator (KDE), and is a simple non-parametric density model. The advantage over a parametric model is that no model fitting is required (except for tuning the bandwidth, usually done by cross-validation). and there is no need to pick K. The disadvantage is that the model takes a lot of memory to store, and a lot of time to evaluate. It is also of no use for clustering tasks.