{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Deniz Gencaga", "hasEmail": "mailto:dgencaga@gmail.com"}, "description": "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.", "distribution": [{"@type": "dcat:Distribution", "description": "Bayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/MAXENT_2005.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "MAXENT_2005.pdf"}], "identifier": "DASHLINK_215", "issued": "2010-09-22", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/215/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus"}