Parameter estimation via conditional expectation: a Bayesian inversion
H.G. Matthies, E. Zander, B.V. Rosić, A. Litvinenko
Advanced Modeling and Simulation in Engineering
Sciences, 3(1), 24, (2016)
Keywords
Inverse identification,
Uncertainty quantification,
Bayesian update,
Parameter identification,
Conditional expectation,
Filters,
Functional and spectral approximation
Abstract
Page Content
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.
Code
DOI: 10.1186/s40323-016-0075-7
See all publications 2016
No
