  We present an algorithm for a class of statistical inference problems. The
main idea is to reformulate the inference problem as an optimization procedure,
based on the generation of surrogate (auxiliary) functions. This approach is
motivated by the MM algorithm, combined with the systematic and iterative
structure of the Expectation-Maximization algorithm. The resulting algorithm
can deal with hidden variables in Maximum Likelihood and Maximum a Posteriori
estimation problems, Instrumental Variables, Regularized Optimization and
Constrained Optimization problems.
  The advantage of the proposed algorithm is to provide a systematic procedure
to build surrogate functions for a class of problems where hidden variables are
usually involved. Numerical examples show the benefits of the proposed
approach.
