Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

We propose a method for reconstructing a probability density function (pdf) from a sample of an n-dimensional probability distribution. The method works by iteratively applying some simple ...

Two estimates of the density function f(x, y) of points in a plane are defined from a sample of n points by (1) counting how many points lie within a square of side 2h and center at (x, y), and (2) by ...

The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...

This is a preview. Log in through your library . Abstract The paper deals with upper and lower bounds for the quality of (probability) density estimation. Connections are established between these ...