Optimal Experiment Design, Ill-Posed Problems

Synonyms

Design of experiments; Inverse problems

Definition

Optimal experiment design refers to model-based methods to select experimental degrees of freedom such that the information gained from the experiment is maximized. For well-posed problems, optimal experiment design methods can focus on minimizing the variance of unknown parameters since suitable estimation methods ensure that the parameter estimates are unbiased. Ill-posed problems require the incorporation of a priori knowledge to regularize the solution which leads to stable but biased estimates. Optimal experimental design methods for ill-posed problems therefore have to incorporate the bias-variance trade-off in their objective function.

Characteristics

Ill-Posed Problems

A major goal of systems biology is the construction of predictive models from the combination of experimental techniques and computational methods. Typical inverse problems that arise in model building of biological systems are, e.g., the construction...