Modelling with mixture of symmetric stable distributions using Gibbs sampling

Authors:
Diego Salas-Gonzalez;Ercan E. Kuruoglu;Diego P. Ruiz
Affiliations:
Department of Signal Processing, Networking and Communications, 18071, University of Granada, Spain;ISTI-CNR. Via G. Moruzzi 1, 56124 Pisa, Italy;Department of Applied Physics, 18071, University of Granada, Spain
Venue:
Signal Processing
Year:
2010

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Abstract

The stable distribution is a very useful tool to model impulsive data. In this work, a fully Bayesian mixture of symmetric stable distribution model is presented. Despite the non-existence of closed form for @a-stable distributions, the use of the product property make it possible to infer on parameters using a straightforward Gibbs sampling. This model is compared to the mixture of Gaussians model. Our proposed methodology is proved to be more robust to outliers than the mixture of Gaussians. Therefore, it is suitable to model mixture of impulsive data. Moreover, as Gaussian is a particular case of the @a-stable distribution, the proposed model is a generalization of mixture of Gaussians. Mixture of symmetric @a-stable is intensively tested in both, simulated and real data.