Related results [to Central Limit Theorem]

Gaussian distributions arise when large numbers of random variables get added together. If instead such variables (say probabilities) get multiplied together what arises is the lognormal distribution

Exp[-(Log[x] - μ)^{2}/(2 σ^{2})]/(Sqrt[2 π] x σ)

For a wide range of underlying distributions the extreme values in large collections of random variables follow the Fisher–Tippett distribution

Exp[(x - μ)/β] Exp[-Exp[(x - μ)/β]]/β

related to the Weibull distribution used in reliability analysis.

For large symmetric matrices with random entries following a distribution with mean 0 and bounded variance the density of normalized eigenvalues tends to Wigner's semicircle law

2Sqrt[1 - x^{2}] UnitStep[1 - x^{2}]/π

while the distribution of spacings between tends to

1/2(π x)Exp[1/4(-π)x^{2}]

The distribution of largest eigenvalues can often be expressed in terms of Painlevé functions.

(See also *1/f* noise on page 969.)