Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11679363_99 | ICA |
Keywords | Field | DocType |
scale factor,multivariate k,gaussians modeling,multivariate probability model,multivariate nig model,mk model,stochastic scale factor,multivariate scale mixture,gaussians model,scale mixture,normal variance mixture model,multivariate laplace,covariance matrix,prior distribution,mixture of gaussians,mixture model | Multivariate t-distribution,Applied mathematics,Discrete mathematics,Multivariate stable distribution,Multivariate statistics,Gaussian process,Statistics,Normal-Wishart distribution,Scatter matrix,Mixture model,Mathematics,Matrix t-distribution | Conference |
Volume | ISSN | ISBN |
3889 | 0302-9743 | 3-540-32630-8 |
Citations | PageRank | References |
13 | 1.10 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Torbjørn Eltoft | 1 | 583 | 48.56 |
| Taesu Kim | 2 | 464 | 34.57 |
| Te-Won Lee | 3 | 2233 | 260.51 |