Parameter estimation via conditional expectation: a Bayesian inversion

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

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


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
Issue number1
StatePublished - Aug 11 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Partly supported by the Deutsche Forschungsgemeinschaft (DFG) through SFB 880.


Dive into the research topics of 'Parameter estimation via conditional expectation: a Bayesian inversion'. Together they form a unique fingerprint.

Cite this