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An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Abstract:

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more ...

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
MacIejowski, JM More by this author
Journal:
IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume:
15
Issue:
PART 1
Pages:
774-785
Publication date:
2009
DOI:
ISSN:
1474-6670
URN:
uuid:49f27d68-44f3-451f-be67-e4e7f622b6e9
Source identifiers:
190647
Local pid:
pubs:190647

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