Generalized Marginal Likelihood for Gaussian mixtures
نویسنده
چکیده
The dominant approach in Bernoulli-Gaussian myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. The aim of this correspondence is to assess the theoretical properties of a related Generalized Marginal Likelihood criterion in a simpliied framework where the lter is reduced to identity. Then the output is a mixture of Gaussian populations. Under a single reasonable assumption we prove that the maximum generalized marginal likelihood estimator always converge asymptotically. Then numerical experiments show that this estimator can perform better than Maximum Likelihood (ML) in the nite sample case, moreover asymptotic estimates are signiicant although biased.
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