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Kent Distribution 📂Probability Distribution

Kent Distribution

Definition 1

Concentration κ>0\kappa > 0 and βR\beta \in \mathbb{R}, Mean γ1Sp1\gamma_{1} \in S^{p-1}, Major Axis γ2Sp1\gamma_{2} \in S^{p-1}, Minor Axis γ3Sp1\gamma_{3} \in S^{p-1} are characterized by the following probability density function for the multivariate distribution FB5((γ1,γ2,γ3),κ,β)\text{FB}_{5} \left( \left( \gamma_{1} , \gamma_{2} , \gamma_{3} \right) , \kappa , \beta \right), known as the Kent Distribution. f(x)=1c(κ,ν)exp(κγTx+β[(γ2Tx)2(γ3Tx)2]),xSp1 f \left( \mathbf{x} \right) = {{ 1 } \over { c \left( \kappa , \nu \right) }} \exp \left( \kappa \gamma^{T} \mathbf{x} + \beta \left[ \left( \gamma_{2}^{T} \mathbf{x} \right)^{2} - \left( \gamma_{3}^{T} \mathbf{x} \right)^{2} \right] \right) \qquad , \mathbf{x} \in S^{p-1}

Especially when 0β<κ/20 \le \beta < \kappa / 2, this distribution is oval on the sphere, and c(κ,ν)>0c \left( \kappa , \nu \right) > 0 is the normalizing constant such that Sp1f(x)dx=1\int_{S^{p-1}} f(\mathbf{x}) d \mathbf{x} = 1. c(κ,β)=2πj=0Γ(j+12)Γ(j+1)β2j(2κ)2j+12I2j+12(κ) c \left( \kappa , \beta \right) = 2 \pi \sum_{j=0}^{\infty} {{ \Gamma \left( j + {{ 1 } \over { 2 }} \right) } \over { \Gamma \left( j+1 \right) }} \beta^{2j} \left( {{ 2 } \over { \kappa }} \right)^{2j + {{ 1 } \over { 2 }}} I_{2j + {{ 1 } \over { 2 }}} \left( \kappa \right)


Description

The Kent distribution draws oval-shaped contours on the sphere similar to the geometric meaning given by the non-trivial covariance matrix of the multivariate normal distribution, i.e., it seems like one could just draw an ellipse on the plane and project it onto a sphere, but as introduced in the definition, modeling with complex formulas is necessary to avoid distortion on the sphere.

Eccentricity 2β/κ2 \beta / \kappa defined by the distribution’s parameters indicates how different the contours are from a circle.


  1. Kasarapu. (2015). Modelling of directional data using Kent distributions. https://doi.org/10.48550/arXiv.1506.08105 ↩︎