Schillings, C. and Stuart, A. M. (2017) Convergence Analysis of the Ensemble Kalman Filter for Inverse Problems: the Noisy Case. . (Submitted) https://resolver.caltech.edu/CaltechAUTHORS:20170612-123329512
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Abstract
We present an analysis of the ensemble Kalman filter for inverse problems based on the continuous time limit of the algorithm. The analysis of the dynamical behaviour of the ensemble allows to establish well-posedness and convergence results for a fixed ensemble size. We will build on the results presented in [Schillings, Stuart 2017] and generalise them to the case of noisy observational data, in particular the influence of the noise on the convergence will be investigated, both theoretically and numerically.
Item Type: | Report or Paper (Discussion Paper) | ||||||||
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Additional Information: | Both authors are grateful to the EPSRC Programme Grant EQUIP for funding of this research. AMS is also grateful to DARPA and to ONR for funding parts of this research. | ||||||||
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Subject Keywords: | Bayesian Inverse Problems, Ensemble Kalman Filter, Parameter Identi�cation | ||||||||
Classification Code: | AMS Subject Classifications: 65N21, 62F15, 65N75 | ||||||||
Record Number: | CaltechAUTHORS:20170612-123329512 | ||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20170612-123329512 | ||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
ID Code: | 78106 | ||||||||
Collection: | CaltechAUTHORS | ||||||||
Deposited By: | Tony Diaz | ||||||||
Deposited On: | 12 Jun 2017 20:58 | ||||||||
Last Modified: | 03 Oct 2019 18:05 |
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