Gauss–Kuzmin distribution

Gauss–Kuzmin
Probability mass function
PDF of the Gauss Kuzmin Distribution
Cumulative distribution function
CDF of the Gauss Kuzmin Distribution
Parameters (none)
Support k { 1 , 2 , } {\displaystyle k\in \{1,2,\ldots \}}
PMF log 2 [ 1 1 ( k + 1 ) 2 ] {\displaystyle -\log _{2}\left[1-{\frac {1}{(k+1)^{2}}}\right]}
CDF 1 log 2 ( k + 2 k + 1 ) {\displaystyle 1-\log _{2}\left({\frac {k+2}{k+1}}\right)}
Mean + {\displaystyle +\infty }
Median 2 {\displaystyle 2\,}
Mode 1 {\displaystyle 1\,}
Variance + {\displaystyle +\infty }
Skewness (not defined)
Excess kurtosis (not defined)
Entropy 3.432527514776...[1][2][3]

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

p ( k ) = log 2 ( 1 1 ( 1 + k ) 2 )   . {\displaystyle p(k)=-\log _{2}\left(1-{\frac {1}{(1+k)^{2}}}\right)~.}

Gauss–Kuzmin theorem

Let

x = 1 k 1 + 1 k 2 + {\displaystyle x={\cfrac {1}{k_{1}+{\cfrac {1}{k_{2}+\cdots }}}}}

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

lim n P { k n = k } = log 2 ( 1 1 ( k + 1 ) 2 )   . {\displaystyle \lim _{n\to \infty }\mathbb {P} \left\{k_{n}=k\right\}=-\log _{2}\left(1-{\frac {1}{(k+1)^{2}}}\right)~.}

Equivalently, let

x n = 1 k n + 1 + 1 k n + 2 +   ; {\displaystyle x_{n}={\cfrac {1}{k_{n+1}+{\cfrac {1}{k_{n+2}+\cdots }}}}~;}

then

Δ n ( s ) = P { x n s } log 2 ( 1 + s ) {\displaystyle \Delta _{n}(s)=\mathbb {P} \left\{x_{n}\leq s\right\}-\log _{2}(1+s)}

tends to zero as n tends to infinity.

Rate of convergence

In 1928, Kuzmin gave the bound

| Δ n ( s ) | C exp ( α n )   . {\displaystyle |\Delta _{n}(s)|\leq C\exp(-\alpha {\sqrt {n}})~.}

In 1929, Paul Lévy[8] improved it to

| Δ n ( s ) | C 0.7 n   . {\displaystyle |\Delta _{n}(s)|\leq C\,0.7^{n}~.}

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

Ψ ( s ) = lim n Δ n ( s ) ( λ ) n {\displaystyle \Psi (s)=\lim _{n\to \infty }{\frac {\Delta _{n}(s)}{(-\lambda )^{n}}}}

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

  1. ^ Blachman, N. (1984). "The continued fraction as an information source (Corresp.)". IEEE Transactions on Information Theory. 30 (4): 671–674. doi:10.1109/TIT.1984.1056924.
  2. ^ Kornerup, Peter; Matula, David W. (July 1995). "LCF: A Lexicographic Binary Representation of the Rationals". J.UCS the Journal of Universal Computer Science. Vol. 1. pp. 484–503. CiteSeerX 10.1.1.108.5117. doi:10.1007/978-3-642-80350-5_41. ISBN 978-3-642-80352-9. {{cite book}}: |journal= ignored (help)
  3. ^ Vepstas, L. (2008), Entropy of Continued Fractions (Gauss-Kuzmin Entropy) (PDF)
  4. ^ Weisstein, Eric W. "Gauss–Kuzmin Distribution". MathWorld.
  5. ^ Gauss, Johann Carl Friedrich. Werke Sammlung. Vol. 10/1. pp. 552–556.
  6. ^ Kuzmin, R. O. (1928). "On a problem of Gauss". Dokl. Akad. Nauk SSSR: 375–380.
  7. ^ Kuzmin, R. O. (1932). "On a problem of Gauss". Atti del Congresso Internazionale dei Matematici, Bologna. 6: 83–89.
  8. ^ Lévy, P. (1929). "Sur les lois de probabilité dont dépendant les quotients complets et incomplets d'une fraction continue". Bulletin de la Société Mathématique de France. 57: 178–194. doi:10.24033/bsmf.1150. JFM 55.0916.02.
  9. ^ Wirsing, E. (1974). "On the theorem of Gauss–Kusmin–Lévy and a Frobenius-type theorem for function spaces". Acta Arithmetica. 24 (5): 507–528. doi:10.4064/aa-24-5-507-528.
  10. ^ Babenko, K. I. (1978). "On a problem of Gauss". Soviet Math. Dokl. 19: 136–140.
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