A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions - Archive ouverte HAL Access content directly
Journal Articles Entropy Year : 2022

A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions

, (1)
1
Nizar Bouhlel

Abstract

This paper introduces a closed-form expression for the Kullback–Leibler divergence (KLD) between two central multivariate Cauchy distributions (MCDs) which have been recently used in different signal and image processing applications where non-Gaussian models are needed. In this overview, the MCDs are surveyed and some new results and properties are derived and discussed for the KLD. In addition, the KLD for MCDs is showed to be written as a function of Lauricella D-hypergeometric series FD(p). Finally, a comparison is made between the Monte Carlo sampling method to approximate the KLD and the numerical value of the closed-form expression of the latter. The approximation of the KLD by Monte Carlo sampling method are shown to converge to its theoretical value when the number of samples goes to the infinity.
Fichier principal
Vignette du fichier
2022_Bouhlel_Entropy.pdf (404.37 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03720272 , version 1 (11-07-2022)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Nizar Bouhlel, David Rousseau. A Generic Formula and Some Special Cases for the Kullback–Leibler Divergence between Central Multivariate Cauchy Distributions. Entropy, 2022, 24 (6), pp.838. ⟨10.3390/e24060838⟩. ⟨hal-03720272⟩
26 View
7 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More