  We discuss methods for {\em a priori} selection of parameters to be estimated
in inverse problem formulations (such as Maximum Likelihood, Ordinary and
Generalized Least Squares) for dynamical systems with numerous state variables
and an even larger number of parameters. We illustrate the ideas with an
in-host model for HIV dynamics which has been successfully validated with
clinical data and used for prediction.
