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

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.