Multivariate kernel density estimation: Difference between revisions

Revision as of 17:55, 3 December 2014

Revision as of 02:10, 24 December 2014

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[[File:Synthetic data 2D KDE.png|thumb|center|500px|alt=Left. Individual kernels. Right. Kernel density estimate.|Construction of 2D kernel density estimate. Left. Individual kernels. Right. Kernel density estimate.]]

The goal of density estimation is to take a finite sample of data and to make inferences about the underylingunderlying probability density function everywhere, including where no data are observed. In kernel density estimation, the contribution of each data point is smoothed out from a single point into a region of space surrounding it. Aggregating the individually smoothed contributions gives an overall picture of the structure of the data and its density function. In the details to follow, we show that this approach leads to a reasonable estimate of the underlying density function.

==Definition==