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We present an algorithm for a class of statistical inference problems. Themain idea is to reformulate the inference problem as an optimization procedure,based on the generation of surrogate (auxiliary) functions. This approach ismotivated by the MM algorithm, combined with the systematic and iterativestructure of the Expectation-Maximization algorithm. The resulting algorithmcan deal with hidden variables in Maximum Likelihood and Maximum a Posterioriestimation problems, Instrumental Variables, Regularized Optimization andConstrained Optimization problems.  The advantage of the proposed algorithm is to provide a systematic procedureto build surrogate functions for a class of problems where hidden variables areusually involved. Numerical examples show the benefits of the proposedapproach.

A systematic optimization approach for a class of statistical inference problems utilizing data augmentation