Parameter Selection Methods in Inverse Problem Formulation

TL;DR

This paper reviews methods for selecting parameters in inverse problems for complex dynamical systems, demonstrating their application with an HIV in-host model validated by clinical data.

Contribution

It introduces approaches for a priori parameter selection in inverse problems and applies them to a validated HIV model, highlighting practical utility.

Findings

Effective parameter selection improves model accuracy.

Application to HIV model demonstrates real-world relevance.

Validated approach with clinical data.

Abstract

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.