2010/01/08 by Nicolas Brunel, Jérôme Lapuyade‐Lahorgue, Wojciech Pieczynski · 2 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Advanced Statistical Methods and Models #Blind Source Separation Techniques
paper · doi:10.1109/tac.2009.2034929
openalex publication_date 2010/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Parametric modeling and estimation of non-Gaussian multidimensional probability density function is a difficult problem whose solution is required by many applications in signal and image processing. A lot of efforts have been devoted to escape the usual Gaussian assumption by developing perturbed Gaussian models such as spherically invariant random vectors (SIRVs). In this work, we introduce an alternative solution based on copulas that enables theoretically to represent any multivariate distribution. Estimation procedures are proposed for some mixtures of copula-based densities and are compared in the hidden Markov chain setting, in order to perform statistical unsupervised classification of signals or images. Useful copulas and SIRV for multivariate signal classification are particularly studied through experiments.