Multivariate scale mixture of gaussians modeling

Authors:
Torbjørn Eltoft;Taesu Kim;Te-Won Lee
Affiliations:
Department of Physics, University of Tromsø & Norut IT, Tromsø, Norway;Institute of Neural Computation, UCSD, CA;Institute of Neural Computation, UCSD, CA
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

In this paper, we present an approach to generate a class of multivariate probability models, which are referred to as scale mixture of Gaussians models. They are constructed as normal variance mixture models, in which the covariance matrix involves a stochastic scale factor with a given prior distribution. We limit the presentation here to the multivariate K (MK) model, which results if we apply a Γ distribution for the scale factor. We then discuss how the parameter of the model can be estimated in an iterative procedure, and include a 2-D case study, where we compare the ability of the MK model to represent real data to corresponding abilities of the multivariate Laplace and the multivariate NIG models.