Parameter Selection Methods in Inverse Problem Formulation
NORTH CAROLINA STATE UNIV AT RALEIGH CENTER FOR RESEARCH IN SCIENTIFIC COMPUTATION
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We discuss methods for 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 and a model for the reaction of the cardiovascular system to an ergometric workload.
- Theoretical Mathematics