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Sequential Inverse Problems Bayesian Principles and the Logistic Map Example

Abstract:

Bayesian statistics provides a general framework for solving inverse problems, but is not without interpretation and implementation problems. This paper discusses difficulties arising from the fact that forward models are always in error to some extent. Using a simple example based on the one-dimensional logistic map, we argue that, when implementation problems are minimal, the Bayesian framework is quite adequate. In this paper the Bayesian Filter is shown to be able to recover excellent sta...

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Publisher copy:
10.1063/1.3497821

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Institution:
University of Oxford
Department:
Oxford, MPLS, Mathematical Inst
Role:
Author

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Role:
Editor
Role:
Editor
Role:
Editor
Publisher:
AMER INST PHYSICS
Volume:
1281
Pages:
1071-1074
Publication date:
2010-01-01
DOI:
EISSN:
1551-7616
ISSN:
0094-243X
URN:
uuid:9d5fe85a-8dd3-4285-941d-a7d6d060de41
Source identifiers:
132356
Local pid:
pubs:132356
ISBN:
978-0-7354-0834-0

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