Bayesian parameter estimation for asymmetric power distributions

Alexandre Baussard (1, 2) , Jean-Yves Tourneret (3, 4)

1 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192)
2 IRIT-SC - Signal et Communications
3 IRIT-MINDS - CoMputational imagINg anD viSion
4 Toulouse INP - Institut National Polytechnique (Toulouse)

This paper proposes a hierarchical Bayesian model for estimating the parameters of asymmetric power distributions (APDs). These distributions are defined by shape, scale and asymmetry parameters which make them very flexible for approximating empirical distributions. A hybrid Markov chain Monte Carlo method is then studied to sample the unknown parameters of APDs. The generated samples can be used to compute the Bayesian estimators of the unknown APD parameters. Numerical experiments show the good performance of the proposed estimation method. An application to an image segmentation problem is finally investigated.

Mots clés: MCMC; Hierarchical Bayesian model; Asymmetric power distributions; Gibbs sampler; Image segmentation

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Alexandre Baussard, Jean-Yves Tourneret. Bayesian parameter estimation for asymmetric power distributions. 23rd European Signal Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. pp. 2206-2210. ⟨hal-01485027⟩