El Halabi, MarwaBaldassarre, LucaCevher, Volkan2014-09-232014-09-232014-09-23201410.1109/MLSP.2014.6958846https://infoscience.epfl.ch/handle/20.500.14299/107023We propose a Bayesian approach where the signal structure can be represented by a mixture model with a submodular prior. We consider an observation model that leads to Lipschitz functions. Due to its combinatorial nature, computing the maximum a posteriori estimate for this model is NP-Hard, nonetheless our converging majorization-minimization scheme yields approximate estimates that, in practice, outperform state-of-the-art methods.text::conference output::conference paper not in proceedings