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In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.<\/jats:p>","DOI":"10.1142\/s012906570400208x","type":"journal-article","created":{"date-parts":[[2004,11,24]],"date-time":"2004-11-24T03:29:42Z","timestamp":1101266982000},"page":"267-292","source":"Crossref","is-referenced-by-count":106,"title":["ADVANCES IN BLIND SOURCE SEPARATION (BSS) AND INDEPENDENT COMPONENT ANALYSIS (ICA) FOR NONLINEAR MIXTURES"],"prefix":"10.1142","volume":"14","author":[{"given":"CHRISTIAN","family":"JUTTEN","sequence":"first","affiliation":[{"name":"Laboratory of Images and Signals (UMR CNRS 5083, INPG, UJF), 46 avenue F\u00e9lix Viallet, 38031 Grenoble Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JUHA","family":"KARHUNEN","sequence":"additional","affiliation":[{"name":"Neural Networks Research Centre, Helsinki University of Technology, P.O. 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