Estimation of Mixtures of Symmetric Alpha Stable Distributions With an Unknown Number of Components

In this work, we study the estimation of mixtures of symmetric alpha-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 alpha-stable components in the mixture in addition to the component parameters and mixing coefficients which is accomplished by using the reversible jump MCMC (RJMCMC) algorithm