Search results
Results from the WOW.Com Content Network
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is [ 2 ] [ 3 ] f ( x ) = 1 2 π σ 2 e − ( x − μ ) 2 2 σ 2 . {\displaystyle f(x)={\frac {1}{\sqrt {2\pi \sigma ^{2 ...
In mathematical physics and probability and statistics, the Gaussian q-distribution is a family of probability distributions that includes, as limiting cases, the uniform distribution and the normal (Gaussian) distribution. It was introduced by Diaz and Teruel. [clarification needed] It is a q-analog of the Gaussian or normal distribution.
The product of two Gaussian probability density functions (PDFs), though, is not in general a Gaussian PDF. Taking the Fourier transform (unitary, angular-frequency convention) of a Gaussian function with parameters a = 1 , b = 0 and c yields another Gaussian function, with parameters c {\displaystyle c} , b = 0 and 1 / c {\displaystyle 1/c ...
The q-Gaussian is a probability distribution arising from the maximization of the Tsallis entropy under appropriate constraints. It is one example of a Tsallis distribution . The q -Gaussian is a generalization of the Gaussian in the same way that Tsallis entropy is a generalization of standard Boltzmann–Gibbs entropy or Shannon entropy . [ 1 ]
Gaussian measures with mean = are known as centered Gaussian measures. The Dirac measure δ μ {\displaystyle \delta _{\mu }} is the weak limit of γ μ , σ 2 n {\displaystyle \gamma _{\mu ,\sigma ^{2}}^{n}} as σ → 0 {\displaystyle \sigma \to 0} , and is considered to be a degenerate Gaussian measure ; in contrast, Gaussian measures with ...
q-Gaussian processes are deformations of the usual Gaussian distribution. There are several different versions of this; here we treat a multivariate deformation, also addressed as q-Gaussian process, arising from free probability theory and corresponding to deformations of the canonical commutation relations .
Another way is to define the cdf () as the probability that a sample lies inside the ellipsoid determined by its Mahalanobis distance from the Gaussian, a direct generalization of the standard deviation. [13] In order to compute the values of this function, closed analytic formula exist, [13] as follows.
The Gaussian quadrature chooses more suitable points instead, so even a linear function approximates the function better (the black dashed line). As the integrand is the third-degree polynomial y(x) = 7x 3 – 8x 2 – 3x + 3, the 2-point Gaussian quadrature rule even returns an exact result.