Bayesian inverse problems and Kalman Filters

被引：0

作者：

Ernst, Oliver G ^{[1
]}

Sprungk, Björn ^{[1
]}

Starkloff, Hans-Jörg ^{[2
]}

机构：

[1] Technical University of Chemnitz, Reichenhainer Str. 41, Chemnitz,09126, Germany

[2] University of Applied Sciences Zwickau, Postfach 201037, Zwickau,08012, Germany

来源：

Lecture Notes in Computational Science and Engineering | 2014年 / 102卷

关键词：

Bandpass filters - Differential equations - Polynomials - Kalman filters;

D O I：

10.1007/978-3-319-08159-5__7

中图分类号：

学科分类号：

摘要：

We provide a brief introduction to Bayesian inverse problems and Bayesian estimators emphasizing their similarities and differences to the classical regularized least-squares approach to inverse problems. We then analyze Kalman filtering techniques for nonlinear systems, specifically the well-known Ensemble Kalman Filter (EnKF) and the recently proposed Polynomial Chaos Expansion Kalman Filter (PCE-KF), in this Bayesian framework and show how they relate to the solution of Bayesian inverse problems. © Springer International Publishing Switzerland 2014.

引用

页码：133 / 159

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