Parameter estimation via conditional expectation: a Bayesian inversion

被引：0
|
作者：
Matthies H.G. [1 ]
Zander E. [1 ]
Rosić B.V. [1 ]
Litvinenko A. [2 ]
机构：
[1] Institute of Scientific Computing, Technische Universität Braunschweig, Braunschweig
[2] Center for Uncertainty Quantification, King Abdullah University of Science and Technology, Thuwal
来源：
Advanced Modeling and Simulation in Engineering Sciences | / 3卷 / 1期
关键词：
Bayesian update; Conditional expectation; Filters; Functional and spectral approximation; Inverse identification; Parameter identification; Uncertainty quantification;
D O I：
10.1186/s40323-016-0075-7
中图分类号：
学科分类号：
摘要：
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. © 2016, The Author(s).
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