Quantifying the relative importance of experimental data points in parameter estimation.

ABSTRACT:

Background

Ordinary differential equations (ODEs) are often used to understand biological processes. Since ODE-based models usually contain many unknown parameters, parameter estimation is an important step toward deeper understanding of the process. Parameter estimation is often formulated as a least squares optimization problem, where all experimental data points are considered as equally important. However, this equal-weight formulation ignores the possibility of existence of relative importance among different data points, and may lead to misleading parameter estimation results. Therefore, we propose to introduce weights to account for the relative importance of different data points when formulating the least squares optimization problem. Each weight is defined by the uncertai

SUBMITTER: Jeong JE 

PROVIDER: S-EPMC6249737 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature