Infoscience

Conference paper

MAP Estimation for Bayesian Mixture Models with Submodular Priors

We 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.

    Reference

    • EPFL-CONF-201800

    Record created on 2014-09-23, modified on 2016-08-09

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