Gauss–Kuzmin distribution

Probability distribution in number theory From Wikipedia, the free encyclopedia

In mathematics, the Gauss–Kuzmin distribution is a discrete probability distribution that arises as the limit probability distribution of the coefficients in the continued fraction expansion of a random variable uniformly distributed in (0, 1).[4] The distribution is named after Carl Friedrich Gauss, who derived it around 1800,[5] and Rodion Kuzmin, who gave a bound on the rate of convergence in 1929.[6][7] It is given by the probability mass function

Parameters (none)
Support
PMF
CDF
Quick facts Parameters, Support ...
Gauss–Kuzmin
Probability mass function
PDF of the Gauss Kuzmin Distribution
Cumulative distribution function
CDF of the Gauss Kuzmin Distribution
Parameters (none)
Support
PMF
CDF
Mean
Median
Mode
Variance
Skewness (not defined)
Excess kurtosis (not defined)
Entropy 3.432527514776...[1][2][3]
Close

Gauss–Kuzmin theorem

Let

be the continued fraction expansion of a number x uniformly distributed in (0, 1). Then

Equivalently, let

then

tends to zero as n tends to infinity.

Rate of convergence

In 1928, Kuzmin gave the bound

In 1929, Paul Lévy[8] improved it to

Later, Eduard Wirsing showed[9] that, for λ = 0.30366... (the Gauss–Kuzmin–Wirsing constant), the limit

exists for every s in [0, 1], and the function Ψ(s) is analytic and satisfies Ψ(0) = Ψ(1) = 0. Further bounds were proved by K. I. Babenko.[10]

See also

References

Related Articles

Wikiwand AI