Likelihood-free Bayesian inference for alpha-stable models
alpha-stable distributions are utilized as models for heavy-tailed noise in many areas of statistics, finance and signal processing engineering. However, in general, neither univariate nor multivariate alpha-stable models admit closed form densities which can be evaluated pointwise. This complicates the inferential procedure. As a result, alpha-stable models are practically limited to the univariate setting under the Bayesian paradigm, and to bivariate models under the classical framework. A novel Bayesian approach to modelling univariate and multivariate alpha-stable distributions is introduced, based on recent advances in "likelihood-free" inference. The performance of this procedure is evaluated in 1, 2 and 3 dimensions, and through an analysis of real daily currency exchange rate data. The proposed approach provides a feasible inferential methodology at a moderate computational cost. (C) 2010 Elsevier B.V. All rights reserved.
bayesian inference alpha-stable distributions approximate bayesian computation likelihood-free inference multivariate models