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Thesis

Parameter uncertainty in stochastic filtering

Abstract:

In standard treatments of stochastic filtering one first requires the various parameters of the model. Simply running a filter with estimated parameters without considering the reliability of this estimate does not take into account this additional source of statistical uncertainty. We propose a novel approach to address this problem by making evaluations via a nonlinear expectation. We show how our approach may be reformulated as a pathwise stochastic optimal control problem, where the op...

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Division:
MPLS
Department:
Mathematical Institute
Role:
Author

Contributors

Institution:
University of Oxford
Role:
Supervisor
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/L015811/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
UUID:
uuid:69333fe3-06b8-4788-946f-057a7dc0d491
Deposit date:
2020-03-19

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