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Particle approximations of the score and observed information matrix in state space models with application to parameter estimation

Abstract:

Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score vector and observed information matrix recursively. The first algorithm is implemented with computational complexity O(N) and the second with complexity O(N2), where N is the number of particles. Although cheaper, the performance of the method O(N) degrades quickly, as it relies on the approximat...

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Publication status:
Published

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Publisher copy:
10.1093/biomet/asq062

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
Journal:
BIOMETRIKA
Volume:
98
Issue:
1
Pages:
65-80
Publication date:
2011-03-05
DOI:
EISSN:
1464-3510
ISSN:
0006-3444
URN:
uuid:7c2e5a95-0e62-4293-b036-6662a9a484ba
Source identifiers:
172666
Local pid:
pubs:172666

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