Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by
solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum
a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the
minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for
these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by
solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum
a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the
minimum mean squared error (MMSE) estimators. Unfortunately, analytical expressions for
these estimators cannot be found in most real-world applications, and the Monte Carlo (MC) …