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The Gaussian distribution revisited

Part of: Classical measure theory Turbulence Stochastic processes Limit theorems

Published online by Cambridge University Press:  01 July 2016

Carlos E. Puente ,
Miguel M. López ,
Jorge E. Pinzón  and
José M. Angulo
Carlos E. Puente*
Affiliation:
University of California, Davis
Miguel M. López*
Affiliation:
University of British Columbia
Jorge E. Pinzón*
Affiliation:
University of California, Davis
José M. Angulo*
Affiliation:
Universidad de Granada
*
∗ Postal address: Hydrologic Science, Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA. Also at: Institute of Theoretical Dynamics, University of California, Davis, CA 95616, USA.
∗∗ Postal address: Department of Mathematics, University of British Columbia, Vancouver, B.C. V6T 1Z2, Canada.
∗∗∗ Postal address: Hydrologic Science, Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA.
∗∗∗∗ Postal address: Departamento de Estadística, Universidad de Granada, E-18071 Granada, Spain.
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Abstract

A new construction of the Gaussian distribution is introduced and proven. The procedure consists of using fractal interpolating functions, with graphs having increasing fractal dimensions, to transform arbitrary continuous probability measures defined over a closed interval. Specifically, let X be any probability measure on the closed interval I with a continuous cumulative distribution. And let f Θ,D :I → R be a deterministic continuous fractal interpolating function, as introduced by Barnsley (1986), with parameters Θ and fractal dimension for its graph D. Then, the derived measure Y = f Θ,D (X) tends to a Gaussian for all parameters Θ such that D → 2, for all X. This result illustrates that plane-filling fractal interpolating functions are ‘intrinsically Gaussian'. It explains that close approximations to the Gaussian may be obtained transforming any continuous probability measure via a single nearly-plane filling fractal interpolator.

Keywords

DETERMINISTIC FRACTAL GEOMETRYMULTIFRACTAL MEASURESFRACTAL INTERPOLATING FUNCTIONSGAUSSIAN DISTRIBUTION

MSC classification

Primary: 28A20: Measurable and nonmeasurable functions, sequences of measurable functions, modes of convergence 28A33: Spaces of measures, convergence of measures 28A80: Fractals
Secondary: 60F05: Central limit and other weak theorems 60G30: Continuity and singularity of induced measures 76F20: Dynamical systems approach to turbulence

Information

Type
General Applied Probability
Information
Advances in Applied Probability , Volume 28 , Issue 2 , June 1996 , pp. 500 - 524
Copyright
Copyright © Applied Probability Trust 1996 

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