Kernel Density Estimation

Kernel Density Estimation

Frederic P. Miller, Agnes F. Vandome, John McBrewster

     

бумажная книга



Издательство: Книга по требованию
Дата выхода: июль 2011
ISBN: 978-6-1318-1368-9
Объём: 72 страниц
Масса: 129 г
Размеры(В x Ш x Т), см: 23 x 16 x 1

High Quality Content by WIKIPEDIA articles! In statistics, kernel density estimation (or Parzen window method, named after Emanuel Parzen) is a non- parametric way of estimating the probability density function of a random variable. As an illustration, given some data about a sample of a population, kernel density estimation makes it possible to extrapolate the data to the entire population. The Parzen window is also used in signal processing as a lag window, in such procedures as the Blackman-Tukey procedure. A histogram can be thought of as a collection of point samples from a kernel density estimate for which the kernel is a uniform box the width of the histogram bin.

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