Abstract
The purpose of model-based experimental design is to maximise the information gathered for quantitative model identification. Instead of the commonly used optimal experimental design, robust experimental design aims to address parametric uncertainties in the design process. In this paper, the Bayesian robust experimental design is investigated, where both a Monte Carlo sampling strategy and local sensitivity evaluation at each sampling point are employed to achieve the robust solution. The link between global sensitivity analysis (GSA) and the Bayesian robust experimental design is established. It is revealed that a lattice sampling based GSA strategy, the Morris method, can be explicitly interpreted as the Bayesian A-optimal design for the uniform hypercube type uncertainties.
| Original language | English |
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| Title of host publication | DYCOPS 2010 - 9th International Symposium on Dynamics and Control of Process Systems, Book of Abstracts |
| Pages | 577-582 |
| Number of pages | 6 |
| Volume | 9 |
| Edition | PART 1 |
| Publication status | Published - 1 Dec 2010 |
| Externally published | Yes |
| Event | 9th International Symposium on Dynamics and Control of Process Systems, DYCOPS 2010 - Leuven, Belgium Duration: 5 Jul 2010 → 7 Jul 2010 |
Conference
| Conference | 9th International Symposium on Dynamics and Control of Process Systems, DYCOPS 2010 |
|---|---|
| Country/Territory | Belgium |
| City | Leuven |
| Period | 5/07/10 → 7/07/10 |
Keywords
- Bayesian method
- Global sensitivity analysis
- Lattice sampling
- Parameter estimation
- Robust experimental design
- Systems biology
ASJC Scopus subject areas
- Control and Systems Engineering