Parameter estimation via conditional expectation: a Bayesian inversion
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Center for Uncertainty Quantification
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AbstractWhen a mathematical or computational model is used to analyse some system, it is usual that some parameters resp. functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observations of the response of the real system. In a probabilistic setting, Bayes’s theory is the proper mathematical background for this identification process. The possibility of being able to compute a conditional expectation turns out to be crucial for this purpose. We show how this theoretical background can be used in an actual numerical procedure, and shortly discuss various numerical approximations.
CitationMatthies HG, Zander E, Rosić BV, Litvinenko A (2016) Parameter estimation via conditional expectation: a Bayesian inversion. Advanced Modeling and Simulation in Engineering Sciences 3. Available: http://dx.doi.org/10.1186/s40323-016-0075-7.
SponsorsPartly supported by the Deutsche Forschungsgemeinschaft (DFG) through SFB 880.