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Milds # Wigner semicircle distribution

Parameters Probability density function Cumulative distribution function $R>0\!$ radius (real) $x\in [-R;+R]\!$ ${\frac {2}{\pi R^{2}}}\,{\sqrt {R^{2}-x^{2}}}\!$ ${\frac {1}{2}}+{\frac {x{\sqrt {R^{2}-x^{2}}}}{\pi R^{2}}}+{\frac {\arcsin \!\left({\frac {x}{R}}\right)}{\pi }}\!$ for $-R\leq x\leq R$ $0\,$ $0\,$ $0\,$ ${\frac {R^{2}}{4}}\!$ $0\,$ $-1\,$ $\ln(\pi R)-{\frac {1}{2}}\,$ $2\,{\frac {I_{1}(R\,t)}{R\,t}}$ $2\,{\frac {J_{1}(R\,t)}{R\,t}}$ The Wigner semicircle distribution, named after the physicist Eugene Wigner, is the probability distribution supported on the interval [−R, R] the graph of whose probability density function f is a semicircle of radius R centered at (0, 0) and then suitably normalized (so that it is really a semi-ellipse):

$f(x)={2 \over \pi R^{2}}{\sqrt {R^{2}-x^{2}\,}}\,$ for −RxR, and f(x) = 0 if |x| > R.

This distribution arises as the limiting distribution of eigenvalues of many random symmetric matrices as the size of the matrix approaches infinity.

It is a scaled beta distribution, more precisely, if Y is beta distributed with parameters α = β = 3/2, then X = 2RYR has the above Wigner semicircle distribution.

A higher-dimensional generalization is a parabolic distribution in three dimensional space, namely the marginal distribution function of a spherical (parametric) distribution 

$f_{X,Y,Z}(x,y,z)={\frac {3}{4\pi }},\qquad \qquad x^{2}+y^{2}+z^{2}\leq 1,$ $f_{X}(x)=\int _{-{\sqrt {1-y^{2}-x^{2}}}}^{+{\sqrt {1-y^{2}-x^{2}}}}\int _{-{\sqrt {1-x^{2}}}}^{+{\sqrt {1-x^{2}}}}{\frac {3\mathrm {d} y}{4\pi }}=3(1-x^{2})/4.$ Note that R=1.

While Wigner's semicircle distribution pertains to the distribution of eigenvalues, Wigner surmise deals with the probability density of the differences between consecutive eigenvalues.

## General properties

The Chebyshev polynomials of the second kind are orthogonal polynomials with respect to the Wigner semicircle distribution.

For positive integers n, the 2n-th moment of this distribution is

$E(X^{2n})=\left({R \over 2}\right)^{2n}C_{n}\,$ where X is any random variable with this distribution and Cn is the nth Catalan number

$C_{n}={1 \over n+1}{2n \choose n},\,$ so that the moments are the Catalan numbers if R = 2. (Because of symmetry, all of the odd-order moments are zero.)

Making the substitution $x=R\cos(\theta )$ into the defining equation for the moment generating function it can be seen that:

$M(t)={\frac {2}{\pi }}\int _{0}^{\pi }e^{Rt\cos(\theta )}\sin ^{2}(\theta )\,d\theta$ which can be solved (see Abramowitz and Stegun §9.6.18) to yield:

$M(t)=2\,{\frac {I_{1}(Rt)}{Rt}}$ where $I_{1}(z)$ is the modified Bessel function. Similarly, the characteristic function is given by:

$\varphi (t)=2\,{\frac {J_{1}(Rt)}{Rt}}$ where $J_{1}(z)$ is the Bessel function. (See Abramowitz and Stegun §9.1.20), noting that the corresponding integral involving $\sin(Rt\cos(\theta ))$ is zero.)

In the limit of $R$ approaching zero, the Wigner semicircle distribution becomes a Dirac delta function.

## Relation to free probability

In free probability theory, the role of Wigner's semicircle distribution is analogous to that of the normal distribution in classical probability theory. Namely, in free probability theory, the role of cumulants is occupied by "free cumulants", whose relation to ordinary cumulants is simply that the role of the set of all partitions of a finite set in the theory of ordinary cumulants is replaced by the set of all noncrossing partitions of a finite set. Just as the cumulants of degree more than 2 of a probability distribution are all zero if and only if the distribution is normal, so also, the free cumulants of degree more than 2 of a probability distribution are all zero if and only if the distribution is Wigner's semicircle distribution.

PDF spherical distribution, (X, Y, Z)

## Related distributions

### Wigner (spherical) parabolic distribution

Parameters $R>0\!$ radius (real) $x\in [-R;+R]\!$ ${\frac {3}{4R^{3}}}\,(R^{2}-x^{2})$ ${\frac {1}{4R^{3}}}\,(2R-x)\,(R+x)^{2}$ $3\,{\frac {i_{1}(R\,t)}{R\,t}}$ $3\,{\frac {j_{1}(R\,t)}{R\,t}}$ The parabolic probability distribution[citation needed] supported on the interval [−R, R] of radius R centered at (0, 0):

$f(x)={3 \over \ 4R^{3}}{(R^{2}-x^{2})}\,$ for −RxR, and f(x) = 0 if |x| > R.

Example. The joint distribution is

$\int _{0}^{\pi }\int _{0}^{+2\pi }\int _{0}^{R}f_{X,Y,Z}(x,y,z)R^{2}\,dr\sin(\theta )\,d\theta \,d\phi =1;$ $f_{X,Y,Z}(x,y,z)={\frac {3}{4\pi }}$ Hence, the marginal PDF of the spherical (parametric) distribution is 

$f_{X}(x)=\int _{-{\sqrt {1-y^{2}-x^{2}}}}^{+{\sqrt {1-y^{2}-x^{2}}}}\int _{-{\sqrt {1-x^{2}}}}^{+{\sqrt {1-x^{2}}}}f_{X,Y,Z}(x,y,z)\,dy\,dz;$ $f_{X}(x)=\int _{-{\sqrt {1-x^{2}}}}^{+{\sqrt {1-x^{2}}}}2{\sqrt {1-y^{2}-x^{2}}}\,dy\,;$ $f_{X}(x)={3 \over \ 4}{(1-x^{2})}\,;$ such that R=1

The characteristic function of a spherical distribution becomes the pattern multiplication of the expected values of the distributions in X, Y and Z.

The parabolic Wigner distribution is also considered the monopole moment of the hydrogen like atomic orbitals.

## Wigner n-sphere distribution

The normalized N-sphere probability density function supported on the interval [−1, 1] of radius 1 centered at (0, 0):

$f_{n}(x;n)={(1-x^{2})^{(n-1)/2}\Gamma (1+n/2) \over {\sqrt {\pi }}\Gamma ((n+1)/2)}\,(n>=-1)$ ,

for −1 ≤ x ≤ 1, and f(x) = 0 if |x| > 1.

Example. The joint distribution is

$\int _{-{\sqrt {1-y^{2}-x^{2}}}}^{+{\sqrt {1-y^{2}-x^{2}}}}\int _{-{\sqrt {1-x^{2}}}}^{+{\sqrt {1-x^{2}}}}\int _{0}^{1}f_{X,Y,Z}(x,y,z){{\sqrt {1-x^{2}-y^{2}-z^{2}}}^{(n)}}dxdydz=1;$ $f_{X,Y,Z}(x,y,z)={\frac {3}{4\pi }}$ Hence, the marginal PDF distribution is 

$f_{X}(x;n)={(1-x^{2})^{(n-1)/2)}\Gamma (1+n/2) \over \ {\sqrt {\pi }}\Gamma ((n+1)/2)}\,;$ such that R=1

The cumulative distribution function (CDF) is

$F_{X}(x)={2x\Gamma (1+n/2)_{2}F_{1}(1/2,(1-n)/2;3/2;x^{2}) \over \ {\sqrt {\pi }}\Gamma ((n+1)/2)}\,;$ such that R=1 and n >= -1

The characteristic function (CF) of the PDF is related to the beta distribution as shown below

$CF(t;n)={_{1}F_{1}(n/2,;n;jt/2)}\,\urcorner (\alpha =\beta =n/2);$ In terms of Bessel functions this is

$CF(t;n)={\Gamma (n/2+1)J_{n/2}(t)/(t/2)^{(n/2)}}\,\urcorner (n>=-1);$ Raw moments of the PDF are

$\mu '_{N}(n)=\int _{-1}^{+1}x^{N}f_{X}(x;n)dx={(1+(-1)^{N})\Gamma (1+n/2) \over \ {2{\sqrt {\pi }}}\Gamma ((2+n+N)/2)};$ Central moments are

$\mu _{0}(x)=1$ $\mu _{1}(n)=\mu _{1}'(n)$ $\mu _{2}(n)=\mu _{2}'(n)-\mu _{1}'^{2}(n)$ $\mu _{3}(n)=2\mu _{1}'^{3}(n)-3\mu _{1}'(n)\mu _{2}'(n)+\mu _{3}'(n)$ $\mu _{4}(n)=-3\mu _{1}'^{4}(n)+6\mu _{1}'^{2}(n)\mu _{2}'(n)-4\mu '_{1}(n)\mu '_{3}(n)+\mu '_{4}(n)$ The corresponding probability moments (mean, variance, skew, kurtosis and excess-kurtosis) are:

$\mu (x)=\mu _{1}'(x)=0$ $\sigma ^{2}(n)=\mu _{2}'(n)-\mu ^{2}(n)=1/(2+n)$ $\gamma _{1}(n)=\mu _{3}/\mu _{2}^{3/2}=0$ $\beta _{2}(n)=\mu _{4}/\mu _{2}^{2}=3(2+n)/(4+n)$ $\gamma _{2}(n)=\mu _{4}/\mu _{2}^{2}-3=-6/(4+n)$ Raw moments of the characteristic function are:

$\mu '_{N}(n)=\mu '_{N;E}(n)+\mu '_{N;O}(n)=\int _{-1}^{+1}cos^{N}(xt)f_{X}(x;n)dx+\int _{-1}^{+1}sin^{N}(xt)f_{X}(x;n)dx;$ For an even distribution the moments are

$\mu _{1}'(t;n:E)=CF(t;n)$ $\mu _{1}'(t;n:O)=0$ $\mu _{1}'(t;n)=CF(t;n)$ $\mu _{2}'(t;n:E)=1/2(1+CF(2t;n))$ $\mu _{2}'(t;n:O)=1/2(1-CF(2t;n))$ $\mu '_{2}(t;n)=1$ $\mu _{3}'(t;n:E)=(CF(3t)+3CF(t;n))/4$ $\mu _{3}'(t;n:O)=0$ $\mu _{3}'(t;n)=(CF(3t;n)+3CF(t;n))/4$ $\mu _{4}'(t;n:E)=(3+4CF(2t;n)+CF(4t;n))/8$ $\mu _{4}'(t;n:O)=(3-4CF(2t;n)+CF(4t;n))/8$ $\mu _{4}'(t;n)=(3+CF(4t;n))/4$ Hence, the moments of the CF (provided N=1) are

$\mu (t;n)=\mu _{1}'(t)=CF(t;n)=_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})$ $\sigma ^{2}(t;n)=1-|CF(t;n)|^{2}=1-|_{0}F_{1}({2+n \over 2},-t^{2}/4)|^{2}$ $\gamma _{1}(n)={\mu _{3} \over \mu _{2}^{3/2}}={_{0}F_{1}({2+n \over 2},-9{t^{2} \over 4})-_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})+8|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})|^{3} \over 4(1-|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4}))^{2}|^{(3/2)}}$ $\beta _{2}(n)={\mu _{4} \over \mu _{2}^{2}}={3+_{0}F_{1}({2+n \over 2},-4t^{2})-(4_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})(_{0}F_{1}({2+n \over 2},-9{t^{2} \over 4}))+3_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})(-1+|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4}|^{2})) \over 4(-1+|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4}))^{2}|^{2}}$ $\gamma _{2}(n)=\mu _{4}/\mu _{2}^{2}-3={-9+_{0}F_{1}({2+n \over 2},-4t^{2})-(4_{0}F_{1}({2+n \over 2},-t^{2}/4)(_{0}F_{1}({2+n \over 2},-9{t^{2} \over 4}))-9_{0}F_{1}({2+n \over 2},-{t^{2} \over 4})+6|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4}|^{3}) \over 4(-1+|_{0}F_{1}({2+n \over 2},-{t^{2} \over 4}))^{2}|^{2}}$ Skew and Kurtosis can also be simplified in terms of Bessel functions.

The entropy is calculated as

$H_{N}(n)=\int _{-1}^{+1}f_{X}(x;n)\ln(f_{X}(x;n))dx$ The first 5 moments (n=-1 to 3), such that R=1 are

$\ -\ln(2/\pi );n=-1$ $\ -\ln(2);n=0$ $\ -1/2+\ln(\pi );n=1$ $\ 5/3-\ln(3);n=2$ $\ -7/4-\ln(1/3\pi );n=3$ ## N-sphere Wigner distribution with odd symmetry applied

The marginal PDF distribution with odd symmetry is 

$f{_{X}}(x;n)={(1-x^{2})^{(n-1)/2)}\Gamma (1+n/2) \over \ {\sqrt {\pi }}\Gamma ((n+1)/2)}\operatorname {sgn}(x)\,;$ such that R=1

Hence, the CF is expressed in terms of Struve functions

$CF(t;n)={\Gamma (n/2+1)H_{n/2}(t)/(t/2)^{(n/2)}}\,\urcorner (n>=-1);$ "The Struve function arises in the problem of the rigid-piston radiator mounted in an infinite baffle, which has radiation impedance given by" 

$Z={\rho c\pi a^{2}[R_{1}(2ka)-iX_{1}(2ka)],}$ $R_{1}={1-{2J_{1}(x) \over 2x},}$ $X_{1}={{2H_{1}(x) \over x},}$ The normalized received signal strength is defined as

$|R|={{1 \over N}|}\sum _{k=1}^{N}\exp[ix_{n}t]|$ $x={1 \over N}\sum _{k=1}^{N}\cos(x_{n}t)$ $y={1 \over N}\sum _{k=1}^{N}\sin(x_{n}t)$ Hence, for an even distribution we expand the NRSS, such that x = 1 and y = 0, obtaining

${\sqrt {x^{2}+y^{2}}}=x+{3 \over 2}y^{2}-{3 \over 2}xy^{2}+{1 \over 2}x^{2}y^{2}+O(y^{3})+O(y^{3})(x-1)+O(y^{3})(x-1)^{2}+O(x-1)^{3}$ The expanded form of the Characteristic function of the received signal strength becomes 

$E[x]={1 \over N}CF(t;n)$ $E[y^{2}]={1 \over 2N}(1-CF(2t;n))$ $E[x^{2}]={1 \over 2N}(1+CF(2t;n))$ $E[xy^{2}]={t^{2} \over 3N^{2}}CF(t;n)^{3}+({N-1 \over 2N^{2}})(1-tCF(2t;n))CF(t;n)$ $E[x^{2}y^{2}]={1 \over 8N^{3}}(1-CF(4t;n))+({N-1 \over 4N^{3}})(1-CF(2t;n)^{2})+({N-1 \over 3N^{3}})t^{2}CF(t;n)^{4}+({(N-1)(N-2) \over N^{3}})CF(t;n)^{2}(1-CF(2t;n))$ 