Seymour Ginsburg

File:Von mises fisher.png

Points sampled from three von Mises-Fisher distributions on the sphere (blue: $\kappa =1$, green: $\kappa =10$, red: $\kappa =100$). The mean directions $\mu$ are shown with arrows.

In directional statistics, the von Mises–Fisher distribution is a probability distribution on the $(p-1)$-dimensional sphere in ${\mathbb{ℝ}}^p$. If $p=2$ the distribution reduces to the von Mises distribution on the circle.

The probability density function of the von Mises-Fisher distribution for the random p-dimensional unit vector $\mathbf{x}$ is given by:

$f_p(\mathbf{x};\mu ,\kappa )=C_p(\kappa )\exp \left(\kappa {\mu }^T\mathbf{x}\right)$

where $\kappa \ge 0,\Vert \mu \Vert =1$ and the normalization constant $C_p(\kappa )$ is equal to

$C_p(\kappa )=\frac{{\kappa }^{p/2-1}}{(2\pi {)}^{p/2}{I}_{p/2-1}(\kappa )}.$

where $I_v$ denotes the modified Bessel function of the first kind and order $v$. If $p=3$, the normalization constant reduces to

$C_3(\kappa )=\frac{\kappa }{4\pi \sinh \kappa }=\frac{\kappa }{2\pi (e^{\kappa }-e^{-\kappa })}.$

Note that the equations above apply for polar coordinates only.

The parameters $\mu$ and $\kappa$ are called the mean direction and concentration parameter, respectively. The greater the value of $\kappa$, the higher the concentration of the distribution around the mean direction $\mu$. The distribution is unimodal for $\kappa >0$, and is uniform on the sphere for $\kappa =0$.

The von Mises-Fisher distribution for $p=3$, also called the Fisher distribution, was first used to model the interaction of dipoles in an electric field (Mardia, 2000). Other applications are found in geology, bioinformatics, and text mining.

Estimation of parameters

A series of $N$ independent measurements $x_i$ are drawn from a von Mises-Fisher distribution. Define

$A_p(\kappa )=\frac{I_{p/2}(\kappa )}{I_{p/2-1}(\kappa )}.$

Then (Sra, 2011) the maximum likelihood estimates of $\mu$ and $\kappa$ are given by

$\mu =\frac{\sum _i^N{x}_i}{\left|\right|\sum _i^N{x}_i\left|\right|},$

$\kappa =A_p^{-1}(\overline{R}).$

Thus $\kappa$ is the solution to

$A_p(\kappa )=\frac{\left|\right|\sum _i^N{x}_i\left|\right|}{N}=\overline{R}.$

A simple approximation to $\kappa$ is

$\hat{\kappa }=\frac{\overline{R}(p-{\overline{R}}^2)}{1-{\overline{R}}^2},$

but a more accurate measure can be obtained by iterating the Newton method a few times

${\hat{\kappa }}_1=\hat{\kappa }-\frac{A_p(\hat{\kappa })-\overline{R}}{1-A_p(\hat{\kappa }{)}^2-\frac{p-1}{\hat{\kappa }}{A}_p(\hat{\kappa })},$

${\hat{\kappa }}_2={\hat{\kappa }}_1-\frac{A_p({\hat{\kappa }}_1)-\overline{R}}{1-A_p({\hat{\kappa }}_1{)}^2-\frac{p-1}{{\hat{\kappa }}_1}{A}_p({\hat{\kappa }}_1)}.$

See also

• Kent distribution, a related distribution on the two-dimensional unit sphere
• von Mises distribution, von Mises–Fisher distribution where p=2, the one-dimensional unit circle
• Bivariate von Mises distribution
• Directional statistics

References

• Dhillon, I., Sra, S. (2003) "Modeling Data using Directional Distributions". Tech. rep., University of Texas, Austin.
• Fisher, RA, "Dispersion on a sphere'". (1953) Proc. Roy. Soc. London Ser. A., 217: 295-305
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