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Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus

Published by Dashlink | National Aeronautics and Space Administration | Catalog Last Checked: October 06, 2025 at 04:25 AM | Dataset Last Updated: March 31, 2025
In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatio-temporally dependent and the mixing system may be non-stationary. Here, we propose a DCA algorithm, that combines concepts of particle filters and Markov Chain Monte Carlo (MCMC) methods in order to separate non-stationary mixtures of spatially dependent Gaussian sources.

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