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 […]