Parameter estimation via conditional expectation: a Bayesian inversion

Hermann G. Matthies, Elmar Zander, Bojana V. Rosić, Alexander Litvinenko

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

When 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.
Original languageEnglish (US)
JournalAdvanced Modeling and Simulation in Engineering Sciences
Volume3
Issue number1
DOIs
StatePublished - Aug 11 2016

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