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Reasons Why the Modified Bessel Function of the First Kind Appears in Directional Statistics 📂Probability Distribution

Reasons Why the Modified Bessel Function of the First Kind Appears in Directional Statistics

Buildup

Modified Bessel Functions

Jν(x)=n=0(1)nΓ(n+1)Γ(n+ν+1)(x2)2n+ν J_{\nu}(x) = \sum \limits_{n=0}^{\infty} \frac{(-1)^{n} }{\Gamma (n+1) \Gamma (n+\nu+1)} \left(\frac{x}{2} \right)^{2n+\nu}

The IνI_{\nu} defined as follows for the Bessel function of the first kind JνJ_{\nu} is called the modified Bessel function of the first kind1.

Iν(z):=iνJν(iz)=(z2)νk=0z22kk!Γ(ν+k+1)=(z2)νπΓ(ν+12)11ezt(1t2)ν12dt \begin{align*} I_{\nu} (z) :=& i^{-\nu} J_{\nu} \left( iz \right) \\ =& \left( {{ z } \over { 2 }} \right)^{\nu} \sum_{k=0}^{\infty} {{ {{ z } \over { 2 }}^{2k} } \over { k! \Gamma \left( \nu + k + 1 \right) }} \\ =& {{ \left( {{ z } \over { 2 }} \right)^{\nu} } \over { \sqrt{\pi} \Gamma \left( \nu + {{ 1 } \over { 2 }} \right) }} \int_{-1}^{1} e^{zt} \left( 1 - t^{2} \right)^{\nu - {{ 1 } \over { 2 }}} dt \end{align*}

Directional Statistics

On the other hand, Directional Statistics is a field that studies probability distributions and statistical inferences in manifolds, rather than in the usual Euclidean space. For example, it deals with data placed on spheres like the Earth, represented by Spheres, and on tori reflected by 2π2 \pi modulus, like Torus, and can be applied to spatial statistics (on spheres) such as the distance between points, as well as to angles between molecules (on tori), showing a bright future for this subdivision. However, the distributions that appear here all have strange probability density functions as follows. fp(x;μ,κ):=(κ2)p/211Γ(p/2)Ip/21(κ)exp(κμTx),xSp1 f_{p} \left( \mathbf{x} ; \mu , \kappa \right) := \left( {{ \kappa } \over { 2 }} \right)^{p/2 - 1} {{ 1 } \over { \Gamma \left( p/2 \right) I_{p/2 - 1} \left( \kappa \right) }} \exp \left( \kappa \mu^{T} \mathbf{x} \right) \qquad , \mathbf{x} \in S^{p-1} The complex factor in front is a constant that normalizes Sp1fdx=1\int_{S^{p-1}} f d \mathbf{x} = 1, including the modified Bessel function of the first kind IνI_{\nu}. This complex function is used for a simple reason.

Solutions

Derivation of the Bessel Function of the First Kind: For νR\nu \in \mathbb{R}, the differential equation of the following form is called the ν\nu order Bessel equation. x2y+xy+(x2ν2)y=0ory+1xy+(1ν2x2)y=0 \begin{align*} && x^{2} y^{\prime \prime} +xy^{\prime}+(x^{2}-\nu^{2})y &= 0 \\ \text{or} && y^{\prime \prime}+\frac{1}{x} y^{\prime} + \left( 1-\frac{\nu^{2}}{x^{2}} \right)y &= 0 \end{align*} Bessel’s equation is a differential equation that appears when solving the wave equation in spherical coordinates. The coefficient is not constant and depends on the independent variable xx. Solutions can be found using the Frobenius Method, and the series solution looks as follows. Jν(x)=n=0(1)nΓ(n+1)Γ(n+ν+1)(x2)2n+νJν(x)=n=0(1)nΓ(n+1)Γ(nν+1)(x2)2nν \begin{align*} J_{\nu}(x) &= \sum \limits_{n=0}^{\infty} \frac{(-1)^{n} }{\Gamma (n+1) \Gamma (n+\nu+1)} \left(\frac{x}{2} \right)^{2n+\nu} \\ J_{-\nu}(x) & =\sum \limits_{n=0}^{\infty}\frac{(-1)^{n}}{\Gamma (n+1)\Gamma (n-\nu+1)} \left( \frac{x}{2} \right)^{2n-\nu} \end{align*}

Bessel functions and their derivation involve complex expressions, like Bessel equation being one of the differential equations and its solutions. However, in the provided quote about directional statistics, only one sentence is of importance.

“Bessel’s equation is a differential equation that appears when solving the wave equation in spherical coordinates.”

In mathematical physics, there might be discussions about wave equations and such, but what we really need is only Sp1fdx=1\int_{S^{p-1}} f d \mathbf{x} = 1. The issue is, unlike in ordinary Euclidean space, the probability density function values on the sphere do not experience the phenomenon of ‘getting farther from the center and closer to 00,’ making integration itself not an easy task. Imagine the shape covered by the probability density function of the normal distribution wrapping around a circle S1S^{1}.

1.png

Looking at when τ=1\tau = 1 in the above figure, the infinitely long tail of the normal distribution is adding infinitely thin layers while circling around S1S^{1} infinitely, not being 00. 2 This repeats with 2π2 \pi, twice the pi, as its period, and this is exactly why the Bessel function can be used, as it creates a ‘wave on the sphere’ like shape.


  1. Sungkyu Jung. “Geodesic projection of the von Mises–Fisher distribution for projection pursuit of directional data.” Electron. J. Statist. 15 (1) 984 - 1033, 2021. https://doi.org/10.1214/21-EJS1807 ↩︎

  2. Straub, J. (2017). Bayesian Inference with the von-Mises-Fisher Distribution in 3D. https://www.semanticscholar.org/paper/Bayesian-Inference-with-the-von-Mises-Fisher-in-3-D-Straub/26d5bb31153df418388b6eb242b2d8842c039c2d#extracted ↩︎