Wrapped distribution

In probability theory and directional statistics, a wrapped probability distribution is a continuous probability distribution that describes data points that lie on a unit n-sphere. In one dimension, a wrapped distribution will consist of points on the unit circle. If φ is a random variate in the interval (-∞,∞;) with probability density function p(φ), then z = e^{i φ} will be a circular variable distributed according to the wrapped distribution p_zw(z) and θ=arg(z) will be an angular variable in the interval (-π,π ] distributed according to the wrapped distribution p_w(θ).

Any probability density function (pdf) $p(\phi )$ on the line can be "wrapped" around the circumference of a circle of unit radius.^[1] That is, the pdf of the wrapped variable

\theta =\phi \mod 2\pi

in some interval of length

2\pi

p_{w}(\theta )=\sum _{{k=-\infty }}^{\infty }{p(\theta +2\pi k)}.

which is a periodic sum of period $2\pi$ . The preferred interval is generally $(-\pi <\theta \leq \pi )$ for which $\ln(e^{{i\theta }})=\arg(e^{{i\theta }})=\theta$

Theory

In most situations, a process involving circular statistics produces angles ( $\phi$ ) which lie in the interval from negative infinity to positive infinity, and are described by an "unwrapped" probability density function $p(\phi )$ . However, a measurement will yield a "measured" angle $\theta$ which lies in some interval of length $2\pi$ (for example $[0,2\pi)$ ). In other words, a measurement cannot tell if the "true" angle $\phi$ has been measured or whether a "wrapped" angle $\phi +2\pi a$ has been measured where a is some unknown integer. That is:

\theta =\phi +2\pi a.

If we wish to calculate the expected value of some function of the measured angle it will be:

\langle f(\theta )\rangle =\int _{{-\infty }}^{\infty }p(\phi )f(\phi +2\pi a)d\phi .

We can express the integral as a sum of integrals over periods of $2\pi$ (e.g. 0 to $2\pi$ ):

\langle f(\theta )\rangle =\sum _{{k=-\infty }}^{\infty }\int _{{2\pi k}}^{{2\pi (k+1)}}p(\phi )f(\phi +2\pi a)d\phi .

Changing the variable of integration to $\theta '=\phi -2\pi k$ and exchanging the order of integration and summation, we have

\langle f(\theta )\rangle =\int _{0}^{{2\pi }}p_{w}(\theta ')f(\theta '+2\pi a')d\theta '

where $p_{w}(\theta ')$ is the pdf of the "wrapped" distribution and a' is another unknown integer (a'=a+k). It can be seen that the unknown integer a' introduces an ambiguity into the expectation value of $f(\theta )$ . A particular instance of this problem is encountered when attempting to take the mean of a set of measured angles. If, instead of the measured angles, we introduce the parameter $z=e^{i\theta }$ it is seen that z has an unambiguous relationship to the "true" angle $\phi$ since:

z=e^{{i\theta }}=e^{{i\phi }}.

Calculating the expectation value of a function of z will yield unambiguous answers:

\langle f(z)\rangle =\int _{0}^{{2\pi }}p_{w}(\theta ')f(e^{{i\theta '}})d\theta '

and it is for this reason that the z parameter is the preferred statistical variable to use in circular statistical analysis rather than the measured angles $\theta$ . This suggests, and it is shown below, that the wrapped distribution function may itself be expressed as a function of z so that:

\langle f(z)\rangle =\oint p_{wz}(z)f(z)\,dz

where $p_{w}(z)$ is defined such that $p_{w}(\theta )\,|d\theta |=p_{wz}(z)\,|dz|$ . This concept can be extended to the multivariate context by an extension of the simple sum to a number of $F$ sums that cover all dimensions in the feature space:

p_{w}({\vec \theta })=\sum _{{k_{1},...,k_{F}=-\infty }}^{{\infty }}{p({\vec \theta }+2\pi k_{1}{\mathbf {e}}_{1}+\dots +2\pi k_{F}{\mathbf {e}}_{F})}

where $\mathbf {e} _{k}=(0,\dots ,0,1,0,\dots ,0)^{\mathsf {T}}$ is the $k$ th Euclidean basis vector.

Expression in terms of characteristic functions

A fundamental wrapped distribution is the Dirac comb which is a wrapped Dirac delta function:

\Delta _{{2\pi }}(\theta )=\sum _{{k=-\infty }}^{{\infty }}{\delta (\theta +2\pi k)}.

Using the delta function, a general wrapped distribution can be written

p_{w}(\theta )=\sum _{{k=-\infty }}^{{\infty }}\int _{{-\infty }}^{\infty }p(\theta ')\delta (\theta -\theta '+2\pi k)\,d\theta '.

Exchanging the order of summation and integration, any wrapped distribution can be written as the convolution of the "unwrapped" distribution and a Dirac comb:

p_{w}(\theta )=\int _{{-\infty }}^{\infty }p(\theta ')\Delta _{{2\pi }}(\theta -\theta ')\,d\theta '.

The Dirac comb may also be expressed as a sum of exponentials, so we may write:

p_{w}(\theta )={\frac {1}{2\pi }}\,\int _{{-\infty }}^{\infty }p(\theta ')\sum _{{n=-\infty }}^{{\infty }}e^{{in(\theta -\theta ')}}\,d\theta '

again exchanging the order of summation and integration,

p_{w}(\theta )={\frac {1}{2\pi }}\,\sum _{n=-\infty }^{\infty }\int _{-\infty }^{\infty }p(\theta ')e^{in(\theta -\theta ')}\,d\theta '

using the definition of $\phi (s)$ , the characteristic function of $p(\theta )$ yields a Laurent series about zero for the wrapped distribution in terms of the characteristic function of the unwrapped distribution:^[2]

p_{w}(\theta )={\frac {1}{2\pi }}\,\sum _{n=-\infty }^{\infty }\phi (n)\,e^{-in\theta }

p_{wz}(z)={\frac {1}{2\pi }}\,\sum _{n=-\infty }^{\infty }\phi (n)\,z^{-n}.

By analogy with linear distributions, the $\phi (m)$ are referred to as the characteristic function of the wrapped distribution^[2] (or perhaps more accurately, the characteristic sequence). This is an instance of the Poisson summation formula and it can be seen that the Fourier coefficients of the Fourier series for the wrapped distribution are just the Fourier coefficients of the Fourier transform of the unwrapped distribution at integer values.

Moments

The moments of the wrapped distribution $p_{w}(z)$ are defined as:

\langle z^{m}\rangle =\oint p_{wz}(z)z^{m}\,dz.

Expressing $p_{w}(z)$ in terms of the characteristic function and exchanging the order of integration and summation yields:

\langle z^{m}\rangle ={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }\phi (n)\oint z^{m-n}\,dz.

From the theory of residues we have

\oint z^{m-n}\,dz=2\pi \delta _{m-n}

where $\delta _{k}$ is the Kronecker delta function. It follows that the moments are simply equal to the characteristic function of the unwrapped distribution for integer arguments:

\langle z^{m}\rangle =\phi (m).

Generation of random variates

If X is a random variate drawn from a linear probability distribution P, then $Z=e^{iX}$ will be a circular variate distributed according to the wrapped P distribution, and $\theta =\arg(Z)$ will be the angular variate distributed according to the wrapped P distribution, with $-\pi <\theta \leq \pi$ .

Entropy

The information entropy of a circular distribution with probability density $p_{w}(\theta )$ is defined as:^[1]

H=-\int _{\Gamma }p_{w}(\theta )\,\ln(p_{w}(\theta ))\,d\theta

where $\Gamma$ is any interval of length $2\pi$ . If both the probability density and its logarithm can be expressed as a Fourier series (or more generally, any integral transform on the circle) then the orthogonality property may be used to obtain a series representation for the entropy which may reduce to a closed form.

The moments of the distribution $\phi (n)$ are the Fourier coefficients for the Fourier series expansion of the probability density:

p_{w}(\theta )={\frac {1}{2\pi }}\sum _{n=-\infty }^{\infty }\phi _{n}e^{-in\theta }

If the logarithm of the probability density can also be expressed as a Fourier series:

\ln(p_{w}(\theta ))=\sum _{m=-\infty }^{\infty }c_{m}e^{im\theta }

where

c_{m}={\frac {1}{2\pi }}\int _{\Gamma }\ln(p_{w}(\theta ))e^{-im\theta }\,d\theta

Then, exchanging the order of integration and summation, the entropy may be written as:

H=-{\frac {1}{2\pi }}\sum _{m=-\infty }^{\infty }\sum _{n=-\infty }^{\infty }c_{m}\phi _{n}\int _{\Gamma }e^{i(m-n)\theta }\,d\theta

Using the orthogonality of the Fourier basis, the integral may be reduced to:

H=-\sum _{n=-\infty }^{\infty }c_{n}\phi _{n}

For the particular case when the probability density is symmetric about the mean, $c_{-m}=c_{m}$ and the logarithm may be written:

\ln(p_{w}(\theta ))=c_{0}+2\sum _{m=1}^{\infty }c_{m}\cos(m\theta )

and

c_{m}={\frac {1}{2\pi }}\int _{\Gamma }\ln(p_{w}(\theta ))\cos(m\theta )\,d\theta

and, since normalization requires that $\phi _{0}=1$ , the entropy may be written:

H=-c_{0}-2\sum _{n=1}^{\infty }c_{n}\phi _{n}

References

1 2 Mardia, Kantilal; Jupp, Peter E. (1999). Directional Statistics. Wiley. ISBN 978-0-471-95333-3. Retrieved 2011-07-19.
1 2 Mardia, K. (1972). Statistics of Directional Data. New York: Academic press.

Borradaile, Graham (2003). Statistics of Earth Science Data. Springer. ISBN 978-3-540-43603-4. Retrieved 31 December 2009.
Fisher, N. I. (1996). Statistical Analysis of Circular Data. Cambridge University Press. ISBN 978-0-521-56890-6. Retrieved 2010-02-09.

External links

Circular Values Math and Statistics with C++11, A C++11 infrastructure for circular values (angles, time-of-day, etc.) mathematics and statistics

Probability distributions
List
Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot
Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta
Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda
Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt
Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Marchenko–Pastur q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda
Mixed continuous-discrete univariate	rectified Gaussian
Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate Laplace multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart
Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham
Degenerate and singular	Degenerate Dirac delta function Singular Cantor
Families	Circular compound Poisson elliptical exponential natural exponential location–scale maximum entropy mixture Pearson Tweedie wrapped

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