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Permanent link (DOI): https://doi.org/10.7939/R39S1KN7W

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Resampled Branching Particle Filters Open Access

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Author or creator
Kouritzin, Michael A.
Additional contributors
Subject/Keyword
McKean-Vlasov
Coupling
Particle Filter
Central Limit Theorem
Exchangeable Random Variables
Branching Process
Bayesian Model Selection
Type of item
Research Material
Language
English
Place
Time
Description
A large class of discrete-time branching particle filters with Bayesian model selection ca-pabilities and effective resampling is introduced in algorithmic form, shown empirically to outperform the popular bootstrap algorithm and analyzed mathematically. The particles interact weakly in the resampling procedure. The weighted particle filter, which has no resampling, and the fully-resampled branching particle filter are included in the class as extreme points. Each particle filter in the class is coupled to a McKean-Vlasov particle system, corresponding to a reduced, unimplementable particle filter, for which Marcinkiewicz strong laws of large numbers (Mllns) and the central limit theorem (clt) can be written down. Coupling arguments are used to show the reduced system can be used to predict performance of the particle filter and to transfer the Mllns to the original weakly-interacting particle filter. This clt is also shown transferable when extra particles are used.
Date created
2015/04/20
DOI
doi:10.7939/R39S1KN7W
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© 2015 Michael A. Kouritzin. This version of this article is open access and can be downloaded and shared. The original author and source must be cited.
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Last modified: 2015:10:12 15:38:58-06:00
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