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  2. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed. There are several versions of the CLT, each applying in the ...

  3. Characteristic function (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Characteristic_function...

    The characteristic function approach is particularly useful in analysis of linear combinations of independent random variables: a classical proof of the Central Limit Theorem uses characteristic functions and Lévy's continuity theorem. Another important application is to the theory of the decomposability of random variables.

  4. Berry–Esseen theorem - Wikipedia

    en.wikipedia.org/wiki/Berry–Esseen_theorem

    As with the multidimensional central limit theorem, there is a multidimensional version of the Berry–Esseen theorem. [5] [6]Let , …, be independent -valued random vectors each having mean zero.

  5. Illustration of the central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Illustration_of_the...

    This section illustrates the central limit theorem via an example for which the computation can be done quickly by hand on paper, unlike the more computing-intensive example of the previous section. Sum of all permutations of length 1 selected from the set of integers 1, 2, 3

  6. Category:Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Category:Central_limit_theorem

    This page was last edited on 1 December 2024, at 08:30 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  7. q-Gaussian distribution - Wikipedia

    en.wikipedia.org/wiki/Q-Gaussian_distribution

    A generalized q-analog of the classical central limit theorem [3] was proposed in 2008, in which the independence constraint for the i.i.d. variables is relaxed to an extent defined by the q parameter, with independence being recovered as q → 1. However, a proof of such a theorem is still lacking. [4]

  8. Asymptotic distribution - Wikipedia

    en.wikipedia.org/wiki/Asymptotic_distribution

    The central limit theorem gives only an asymptotic distribution. As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails.

  9. Stable distribution - Wikipedia

    en.wikipedia.org/wiki/Stable_distribution

    The Generalized Central Limit Theorem (GCLT) was an effort of multiple mathematicians (Berstein, Lindeberg, Lévy, Feller, Kolmogorov, and others) over the period from 1920 to 1937. [14] The first published complete proof (in French) of the GCLT was in 1937 by Paul Lévy. [15]