von Mises distribution

Probability density function The support is chosen to be [− $π$ , $π$ ] with μ = 0
Cumulative distribution function The support is chosen to be [− $π$ , $π$ ] with μ = 0
Parameters	$\mu$ real $\kappa >0$
Support	$x\in$ any interval of length 2π
PDF	${\frac {e^{\kappa \cos(x-\mu )}}{2\pi I_{0}(\kappa )}}$
CDF	(not analytic – see text)
Mean	$\mu$
Median	$\mu$
Mode	$\mu$
Variance	${\textrm {var}}(x)=1-I_{1}(\kappa )/I_{0}(\kappa )$ (circular)
Entropy	$-\kappa {\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}+\ln[2\pi I_{0}(\kappa )]$ (differential)
CF	${\frac {I_{\|n\|}(\kappa )}{I_{0}(\kappa )}}e^{in\mu }$

In probability theory and directional statistics, the von Mises distribution (also known as the circular normal distribution or Tikhonov distribution) is a continuous probability distribution on the circle. It is a close approximation to the wrapped normal distribution, which is the circular analogue of the normal distribution. A freely diffusing angle $\theta$ on a circle is a wrapped normally distributed random variable with an unwrapped variance that grows linearly in time. On the other hand, the von Mises distribution is the stationary distribution of a drift and diffusion process on the circle in a harmonic potential, i.e. with a preferred orientation.^[1] The von Mises distribution is the maximum entropy distribution for circular data when the real and imaginary parts of the first circular moment are specified. The von Mises distribution is a special case of the von Mises–Fisher distribution on the N-dimensional sphere.

Definition

The von Mises probability density function for the angle x is given by:^[2]

f(x\mid \mu ,\kappa )={\frac {e^{\kappa \cos(x-\mu )}}{2\pi I_{0}(\kappa )}}

where I₀(k) is the modified Bessel function of order 0.

The parameters μ and 1/κ are analogous to μ and σ² (the mean and variance) in the normal distribution:

μ is a measure of location (the distribution is clustered around μ), and
κ is a measure of concentration (a reciprocal measure of dispersion, so 1/κ is analogous to σ²).
- If κ is zero, the distribution is uniform, and for small κ, it is close to uniform.
- If κ is large, the distribution becomes very concentrated about the angle μ with κ being a measure of the concentration. In fact, as κ increases, the distribution approaches a normal distribution in x with mean μ and variance 1/κ.

The probability density can be expressed as a series of Bessel functions (see Abramowitz and Stegun §9.6.34)

f(x\mid \mu ,\kappa )={\frac {1}{2\pi }}\left(1+{\frac {2}{I_{0}(\kappa )}}\sum _{j=1}^{\infty }I_{j}(\kappa )\cos[j(x-\mu )]\right)

where I_j(x) is the modified Bessel function of order j.

The cumulative distribution function is not analytic and is best found by integrating the above series. The indefinite integral of the probability density is:

\Phi (x\mid \mu ,\kappa )=\int f(t\mid \mu ,\kappa )\,dt={\frac {1}{2\pi }}\left(x+{\frac {2}{I_{0}(\kappa )}}\sum _{j=1}^{\infty }I_{j}(\kappa ){\frac {\sin[j(x-\mu )]}{j}}\right).

The cumulative distribution function will be a function of the lower limit of integration x₀:

F(x\mid \mu ,\kappa )=\Phi (x\mid \mu ,\kappa )-\Phi (x_{0}\mid \mu ,\kappa ).\,

Moments

Further information: Circular mean

The moments of the von Mises distribution are usually calculated as the moments of the complex exponential z = e^ix rather than the angle x itself. These moments are referred to as circular moments. The variance calculated from these moments is referred to as the circular variance. The one exception to this is that the "mean" usually refers to the argument of the complex mean.

The nth raw moment of z is:

m_{n}=\langle z^{n}\rangle =\int _{\Gamma }z^{n}\,f(x|\mu ,\kappa )\,dx

={\frac {I_{|n|}(\kappa )}{I_{0}(\kappa )}}e^{in\mu }

where the integral is over any interval $\Gamma$ of length 2π. In calculating the above integral, we use the fact that zⁿ = cos(nx) + i sin(nx) and the Bessel function identity (See Abramowitz and Stegun §9.6.19):

I_{n}(\kappa )={\frac {1}{\pi }}\int _{0}^{\pi }e^{\kappa \cos(x)}\cos(nx)\,dx.

The mean of the complex exponential z is then just

m_{1}={\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}e^{i\mu }

and the circular mean value of the angle x is then taken to be the argument μ. This is the expected or preferred direction of the angular random variables. The variance of z, or the circular variance of x is:

{\textrm {var}}(x)=1-E[\cos(x-\mu )]=1-{\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}.

Limiting behavior

In the limit of large κ the distribution becomes a normal distribution

\lim _{\kappa \rightarrow \infty }f(x\mid \mu ,\kappa )={\frac {1}{\sigma {\sqrt {2\pi }}}}\exp \left[{\dfrac {-(x-\mu )^{2}}{2\sigma ^{2}}}\right]

where σ² = 1/κ. In the limit of small κ it becomes a uniform distribution:

\lim _{\kappa \rightarrow 0}f(x\mid \mu ,\kappa )=\mathrm {U} (x)

where the interval for the uniform distribution U(x) is the chosen interval of length 2π.

Estimation of parameters

A series of N measurements $z_{n}=e^{i\theta _{n}}$ drawn from a von Mises distribution may be used to estimate certain parameters of the distribution. (Borradaile, 2003) The average of the series ${\overline {z}}$ is defined as

{\overline {z}}={\frac {1}{N}}\sum _{n=1}^{N}z_{n}

and its expectation value will be just the first moment:

\langle {\overline {z}}\rangle ={\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}e^{i\mu }.

In other words, ${\overline {z}}$ is an unbiased estimator of the first moment. If we assume that the mean $\mu$ lies in the interval $[-\pi ,\pi )$ , then Arg $({\overline {z}})$ will be a (biased) estimator of the mean $\mu$ .

Viewing the $z_{n}$ as a set of vectors in the complex plane, the ${\bar {R}}^{2}$ statistic is the square of the length of the averaged vector:

{\bar {R}}^{2}={\overline {z}}\,{\overline {z^{*}}}=\left({\frac {1}{N}}\sum _{n=1}^{N}\cos \theta _{n}\right)^{2}+\left({\frac {1}{N}}\sum _{n=1}^{N}\sin \theta _{n}\right)^{2}

and its expectation value is:

\langle {\bar {R}}^{2}\rangle ={\frac {1}{N}}+{\frac {N-1}{N}}\,{\frac {I_{1}(\kappa )^{2}}{I_{0}(\kappa )^{2}}}.

In other words, the statistic

R_{e}^{2}={\frac {N}{N-1}}\left({\bar {R}}^{2}-{\frac {1}{N}}\right)

will be an unbiased estimator of ${\frac {I_{1}(\kappa )^{2}}{I_{0}(\kappa )^{2}}}\,$ and solving the equation $R_{e}={\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}\,$ for $\kappa \,$ will yield a (biased) estimator of $\kappa \,$ . In analogy to the linear case, the solution to the equation ${\bar {R}}={\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}\,$ will yield the maximum likelihood estimate of $\kappa \,$ and both will be equal in the limit of large N. For approximate solution to $\kappa \,$ refer to von Mises–Fisher distribution.

Distribution of the mean

The distribution of the sample mean ${\overline {z}}={\bar {R}}e^{i{\overline {\theta }}}$ for the von Mises distribution is given by:^[3]

P({\bar {R}},{\bar {\theta }})\,d{\bar {R}}\,d{\bar {\theta }}={\frac {1}{(2\pi I_{0}(\kappa ))^{N}}}\int _{\Gamma }\prod _{n=1}^{N}\left(e^{\kappa \cos(\theta _{n}-\mu )}d\theta _{n}\right)={\frac {e^{\kappa N{\bar {R}}\cos({\bar {\theta }}-\mu )}}{I_{0}(\kappa )^{N}}}\left({\frac {1}{(2\pi )^{N}}}\int _{\Gamma }\prod _{n=1}^{N}d\theta _{n}\right)

where N is the number of measurements and $\Gamma \,$ consists of intervals of $2\pi$ in the variables, subject to the constraint that ${\bar {R}}$ and ${\bar {\theta }}$ are constant, where ${\bar {R}}$ is the mean resultant:

{\bar {R}}^{2}=|{\bar {z}}|^{2}=\left({\frac {1}{N}}\sum _{n=1}^{N}\cos(\theta _{n})\right)^{2}+\left({\frac {1}{N}}\sum _{n=1}^{N}\sin(\theta _{n})\right)^{2}

and ${\overline {\theta }}$ is the mean angle:

{\overline {\theta }}=\mathrm {Arg} ({\overline {z}}).\,

Note that product term in parentheses is just the distribution of the mean for a circular uniform distribution.^[3]

This means that the distribution of the mean direction $\mu$ of a von Mises distribution $VM(\mu ,\kappa )$ is a von Mises distribution $VM(\mu ,{\bar {R}}N\kappa )$ , or, equivalently, $VM(\mu ,R\kappa )$ .

Entropy

The information entropy of the Von Mises distribution is defined as:^[2]

H=-\int _{\Gamma }f(\theta ;\mu ,\kappa )\,\ln(f(\theta ;\mu ,\kappa ))\,d\theta \,

where $\Gamma$ is any interval of length $2\pi$ . The logarithm of the density of the Von Mises distribution is straightforward:

\ln(f(\theta ;\mu ,\kappa ))=-\ln(2\pi I_{0}(\kappa ))+\kappa \cos(\theta )\,

The characteristic function representation for the Von Mises distribution is:

f(\theta ;\mu ,\kappa )={\frac {1}{2\pi }}\left(1+2\sum _{n=1}^{\infty }\phi _{n}\cos(n\theta )\right)

where $\phi _{n}=I_{|n|}(\kappa )/I_{0}(\kappa )$ . Substituting these expressions into the entropy integral, exchanging the order of integration and summation, and using the orthogonality of the cosines, the entropy may be written:

H=\ln(2\pi I_{0}(\kappa ))-\kappa \phi _{1}=\ln(2\pi I_{0}(\kappa ))-\kappa {\frac {I_{1}(\kappa )}{I_{0}(\kappa )}}

For $\kappa =0$ , the von Mises distribution becomes the circular uniform distribution and the entropy attains its maximum value of $\ln(2\pi )$ .

Notice that the Von Mises distribution maximizes the entropy when the real and imaginary parts of the first circular moment are specified^[4] or, equivalently, the circular mean and circular variance are specified.

References

↑ Risken, H. (1989). The Fokker–Planck Equation. Springer. ISBN 978-3-540-61530-9.
1 2 Mardia, Kantilal; Jupp, Peter E. (1999). Directional Statistics. Wiley. ISBN 978-0-471-95333-3. Retrieved 2011-07-19.
1 2 Jammalamadaka, S. Rao; Sengupta, A. (2001). Topics in Circular Statistics. World Scientific Publishing Company. ISBN 978-981-02-3778-3. Retrieved 2010-03-03.
↑ Jammalamadaka, S. Rao; SenGupta, A. (2001). Topics in circular statistics. New Jersey: World Scientific. ISBN 981-02-3778-2. Retrieved 2011-05-15.

Abramowitz, M. and Stegun, I. A. (ed.), Handbook of Mathematical Functions, National Bureau of Standards, 1964; reprinted Dover Publications, 1965. ISBN 0-486-61272-4
"Algorithm AS 86: The von Mises Distribution Function", Mardia, Applied Statistics, 24, 1975 (pp. 268–272).
"Algorithm 518, Incomplete Bessel Function I0: The von Mises Distribution", Hill, ACM Transactions on Mathematical Software, Vol. 3, No. 3, September 1977, Pages 279–284.
Best, D. and Fisher, N. (1979). Efficient simulation of the von Mises distribution. Applied Statistics, 28, 152–157.
Evans, M., Hastings, N., and Peacock, B., "von Mises Distribution". Ch. 41 in Statistical Distributions, 3rd ed. New York. Wiley 2000.
Fisher, Nicholas I., Statistical Analysis of Circular Data. New York. Cambridge 1993.
"Statistical Distributions", 2nd. Edition, Evans, Hastings, and Peacock, John Wiley and Sons, 1993, (chapter 39). ISBN 0-471-55951-2
Borradaile, Graham (2003). Statistics of Earth Science Data. Springer. ISBN 978-3-540-43603-4. Retrieved 31 Dec 2009.

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 Tukey lambda q-Gaussian q-exponential q-Weibull shifted log-logistic

Mixed continuous-discrete univariate	rectified Gaussian

Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet 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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