The Fisher information matrix summarizes the amount of information in a set
of data relative to the quantities of interest. There are many applications of
the information matrix in statistical modeling, system identification and
parameter estimation. This short paper reviews a feedback-based method and an
independent perturbation approach for computing the information matrix for
complex problems, where a closed form of the information matrix is not
achievable. We show through numerical examples how these methods improve the
accuracy of the estimate of the information matrix compared to the basic
resampling-based approach. Some relevant theory is summarized