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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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Publication date:
2010-01-01
URN:
uuid:959f1092-c2d0-48ab-a3dd-8c4db0d8828d
Local pid:
oai:eprints.maths.ox.ac.uk:1005

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