Investigating Bayesian robust experimental design with principles of global sensitivity analysis

Fei He, Hong Yue, Martin Brown

Research output: Contribution to conferencePaper

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 languageEnglish
Pages563-568
Number of pages6
DOIs
Publication statusPublished - 2010
Event9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010) - Lueven, Belgium
Duration: 5 Jul 20107 Jul 2010

Conference

Conference9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010)
CountryBelgium
CityLueven
Period5/07/107/07/10

Keywords

  • bayesian analysis
  • global sensitivity analysis
  • modeling

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