# Monthly Archives: July 2013

## A short tutorial on Kernel Density Estimation (KDE)

The aim of Kernel Density Estimation(KDE) is: Given a set of $N$ samples from a random variable, $\mathbf{X}$, possibly multivariate and continuous, estimate the random variables probability density function(pdf) The univariate case To get a rough idea how one can think about the problem, we start out with a set of samples, $X=[x_1,x_2,...,x_N]$, of a […]