Abstract
Designing an experiment for parameter estimation involves two steps. The first one is qualitative, and consists in selecting a suitable configuration of the input/output ports so as to make, if possible, all the parameters of interest identifiable. The second step is quantitative, and based on the optimization of a suitable criterion (with respect to the input shapes, sampling schedule,…) so as to get the maximum information from the data to be collected. When the model is nonlinear in the parameters, both steps present specific difficulties which are discussed in this paper. The practical importance of qualitative experiment design is illustrated by a very simple biological model. Various policies presented in the literature for quantitative experiment design are reviewed. Special emphasis is given to methods allowing uncertainty on the prior information to be taken into account.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
