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The Cauchy distribution is often used in statistics as the canonical example of a "pathological" distribution since both its expected value and its variance are undefined (but see § Moments below). The Cauchy distribution does not have finite moments of order greater than or equal to one; only fractional absolute moments exist. [1]
In probability theory, a Cauchy process is a type of stochastic process. There are symmetric and asymmetric forms of the Cauchy process. [1] The unspecified term "Cauchy process" is often used to refer to the symmetric Cauchy process. [2] The Cauchy process has a number of properties: It is a Lévy process [3] [4] [5] It is a stable process [1] [2]
Generally, a Cauchy distribution is any probability distribution belonging to the same location-scale family as this one. Thus, if X has a standard Cauchy distribution and μ is any real number and σ > 0, then Y = μ + σX has a Cauchy distribution whose median is μ and whose first and third quartiles are respectively μ − σ and μ + σ .
The motivation for doing this is that pressure is typically a variable of interest, and also this simplifies application to specific fluid families later on since the rightmost tensor τ in the equation above must be zero for a fluid at rest. Note that τ is traceless. The Cauchy equation may now be written in another more explicit form:
This is the characteristic function of the standard Cauchy distribution: thus, the sample mean has the same distribution as the population itself. As a further example, suppose X follows a Gaussian distribution i.e. X ∼ N ( μ , σ 2 ) {\displaystyle X\sim {\mathcal {N}}(\mu ,\sigma ^{2})} .
The Voigt distribution, or Voigt profile, is the convolution of a normal distribution and a Cauchy distribution. It is found in spectroscopy when spectral line profiles are broadened by a mixture of Lorentzian and Doppler broadening mechanisms. The Chen distribution.
Well-known families in which the functional form of the distribution is consistent throughout the family include the following: Normal distribution; Elliptical distributions; Cauchy distribution; Uniform distribution (continuous) Uniform distribution (discrete) Logistic distribution; Laplace distribution; Student's t-distribution
The Lorentzian profile has no moments (other than the zeroth), and so the moment-generating function for the Cauchy distribution is not defined. It follows that the Voigt profile will not have a moment-generating function either, but the characteristic function for the Cauchy distribution is well defined, as is the characteristic function for ...