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Probability Densities

  • Igor Grabec
  • Wolfgang Sachse
Chapter
Part of the Springer Series in Synergetics book series (SSSYN, volume 68)

Abstract

It was mentioned in Chap. 2 that a proper analytical description of empirical data is one of the primary tasks of empirical science. A basic related problem is the estimation of the probability density function (PDF) f(x) of a continuous, random variable X from a set of N independent, identically distributed sample values {x 1,…, x N }. Hereafter we assume that only continuity of the random variable and no other a priori information about its properties is known, therefore a non-parametric estimation is considered. Among various non-parametric techniques the presentation of the PDF by a kernel estimator is most common [4, 3, 6, 5] and this is also the focus of our treatment.

Keywords

Probability Density Probability Density Function Probability Density Function Window Width Window Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Igor Grabec
    • 1
  • Wolfgang Sachse
    • 2
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Theoretical and Applied MechanicsCornell UniversityIthacaUSA

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