  Parameter estimation for model-based clustering using a finite mixture of
normal inverse Gaussian (NIG) distributions is achieved through variational
Bayes approximations. Univariate NIG mixtures and multivariate NIG mixtures are
considered. The use of variational Bayes approximations here is a substantial
departure from the traditional EM approach and alleviates some of the
associated computational complexities and uncertainties. Our variational
algorithm is applied to simulated and real data. The paper concludes with
discussion and suggestions for future work.
