Kaniadakis Gaussian distribution
Continuous probability distribution From Wikipedia, the free encyclopedia
The Kaniadakis Gaussian distribution (also known as κ-Gaussian distribution) is a probability distribution which arises as a generalization of the Gaussian distribution from the maximization of the Kaniadakis entropy under appropriated constraints. It is one example of a Kaniadakis κ-distribution. The κ-Gaussian distribution has been applied successfully for describing several complex systems in economy,[1] geophysics,[2] astrophysics, among many others.
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The κ-Gaussian distribution is a particular case of the κ-Generalized Gamma distribution.[3]
Definitions
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Perspective
Probability density function
The general form of the centered Kaniadakis κ-Gaussian probability density function is:[3]
where is the entropic index associated with the Kaniadakis entropy, is the scale parameter, and
is the normalization constant.
The standard Normal distribution is recovered in the limit
Cumulative distribution function
The cumulative distribution function of κ-Gaussian distribution is given by
where
is the Kaniadakis κ-Error function, which is a generalization of the ordinary Error function as .
Properties
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Perspective
Moments, mean and variance
The centered κ-Gaussian distribution has a moment of odd order equal to zero, including the mean.
The variance is finite for and is given by:
Kurtosis
The kurtosis of the centered κ-Gaussian distribution may be computed thought:
which can be written as
Thus, the kurtosis of the centered κ-Gaussian distribution is given by:
or
κ-Error function
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Perspective
κ-Error function | |
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![]() Plot of the κ-error function for typical κ-values. The case κ=0 corresponds to the ordinary error function. | |
General information | |
General definition | |
Fields of application | Probability, thermodynamics |
Domain, codomain and image | |
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Image | |
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Derivative |
The Kaniadakis κ-Error function (or κ-Error function) is a one-parameter generalization of the ordinary error function defined as:[3]
Although the error function cannot be expressed in terms of elementary functions, numerical approximations are commonly employed.
For a random variable X distributed according to a κ-Gaussian distribution with mean 0 and standard deviation , κ-Error function means the probability that X falls in the interval .
Applications
The κ-Gaussian distribution has been applied in several areas, such as:
- In economy, the κ-Gaussian distribution has been applied in the analysis of financial models, accurately representing the dynamics of the processes of extreme changes in stock prices.[4]
- In inverse problems, Error laws in extreme statistics are robustly represented by κ-Gaussian distributions.[2][5][6]
- In astrophysics, stellar-residual-radial-velocity data have a Gaussian-type statistical distribution, in which the K index presents a strong relationship with the stellar-cluster ages.[7][8]
- In nuclear physics, the study of Doppler broadening function in nuclear reactors is well described by a κ-Gaussian distribution for analyzing the neutron-nuclei interaction.[9][10]
- In cosmology, for interpreting the dynamical evolution of the Friedmann–Robertson–Walker Universe.
- In plasmas physics, for analyzing the electron distribution in electron-acoustic double-layers[11] and the dispersion of Langmuir waves.[12]
See also
References
External links
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