// Workers AI · dad joke modeWhy was Wrapped Lévy distribution sad? It was feeling wrapped up.

In probability theory and directional statistics, a wrapped Lévy distribution is a wrapped probability distribution that results from the "wrapping" of the Lévy distribution around the unit circle.

Description

The pdf of the wrapped Lévy distribution is

${\displaystyle f_{WL}(\theta$ ;\mu ,c)=\sum _{n=-\infty }^{\infty }{\sqrt {\frac {c}{2\pi }}}\,{\frac {e^{-c/2(\theta +2\pi n-\mu )}}{(\theta +2\pi n-\mu )^{3/2}}}}

where the value of the summand is taken to be zero when $\theta +2\pi n-\mu \leq 0$, $c$ is the scale factor and $\mu$ is the location parameter. Expressing the above pdf in terms of the characteristic function of the Lévy distribution yields:

${\displaystyle f_{WL}(\theta$ ;\mu ,c)={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }e^{-in(\theta -\mu )-{\sqrt {c|n|}}\,(1-i\operatorname {sgn} {n})}={\frac {1}{2\pi }}\left(1+2\sum _{n=1}^{\infty }e^{-{\sqrt {cn}}}\cos \left(n(\theta -\mu )-{\sqrt {cn}}\,\right)\right)}

In terms of the circular variable $z=e^{i\theta }$ the circular moments of the wrapped Lévy distribution are the characteristic function of the Lévy distribution evaluated at integer arguments:

${\displaystyle \langle z^{n}\rangle =\int _{\Gamma }e^{in\theta }\,f_{WL}(\theta$ ;\mu ,c)\,d\theta =e^{in\mu -{\sqrt {c|n|}}\,(1-i\operatorname {sgn}(n))}.}

where $\Gamma \,$ is some interval of length $2\pi$. The first moment is then the expectation value of z, also known as the mean resultant, or mean resultant vector:

$\langle z\rangle =e^{i\mu -{\sqrt {c}}(1-i)}$

The mean angle is

$\theta _{\mu }=\mathrm {Arg} \langle z\rangle =\mu +{\sqrt {c}}$

and the length of the mean resultant is

$R=|\langle z\rangle |=e^{-{\sqrt {c}}}$

See also

• Wrapped distribution
• Directional statistics

References

• Fisher, N. I. (1996). Statistical Analysis of Circular Data. Cambridge University Press. ISBN 978-0-521-56890-6. Retrieved 2010-02-09.