Editing Density estimation (section)

{{Short description|Estimate of an unobservable underlying probability density function}}
{{for|the signal processing concept|spectral density estimation}}
{{more citations needed|date=August 2012}}

[[File:KernelDensityGaussianAnimated.gif|thumb|350px|Demonstration of density estimation using [[Kernel density estimation]]: The true density is a mixture of two Gaussians centered around 0 and 3, shown with a solid blue curve. In each frame, 100 samples are generated from the distribution, shown in red. Centered on each sample, a Gaussian kernel is drawn in gray. Averaging the Gaussians yields the density estimate shown in the dashed black curve.]]

In [[statistics]], '''probability density estimation''' or simply '''density estimation''' is the construction of an [[estimate]], based on observed [[data]], of an unobservable underlying [[probability density function]].  The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.<ref>Alberto Bernacchia, Simone Pigolotti, Self-Consistent Method for Density Estimation, Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 73, Issue 3, June 2011, Pages 407–422, https://doi.org/10.1111/j.1467-9868.2011.00772.x</ref>

A variety of approaches to density estimation are used, including [[Parzen window]]s and a range of [[data clustering]] techniques, including [[vector quantization]]. The most basic form of density estimation is a rescaled [[histogram]].