Estimation of mixtures of symmetric alpha stable processes with unknown number of components

Salas D., Kuruoglu E. E., Ruiz D. P.

Bayesian analysis  Reversible jump Markov chain Monte Carlo  Mixture distributions 

In this work, we study the estimation of mixtures of symmetric á-stable distributions using Bayesian inference. We utilise numerical Bayesian sampling techniques such as Markov chain Monte Carlo (MCMC). Our estimation technique is capable of estimating also the number of á-stable components in the mixture in addition to the component parameters and mixing coefficients which is accomplished by the use of the Reversible Jump MCMC (RJMCMC) algorithm.

Source: Speech and Signal Processing, Toulouse, France, 14-19/05/2006