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Sequential auxiliary particle belief propagation

Abstract:

This paper discloses a novel algorithm for efficient inference in undirected graphical models using Sequential Monte Carlo (SMC) based numerical approximation techniques. The methodology developed, titled "Auxiliary Particle Belief Propagation", extends the applicability of the much celebrated (Loopy) Belief Propagation (LBP) algorithm to non-linear, non-Gaussian models, whilst retaining a computational cost that is linear in the number of sample points (or particles). Furthermore, we provide...

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Publication status:
Published

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Publisher copy:
10.1109/ICIF.2005.1591923

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
2005 7th International Conference on Information Fusion (FUSION), Vols 1 and 2
Volume:
1
Pages:
705-711
Publication date:
2005-01-01
DOI:
Source identifiers:
172724
Language:
English
Keywords:
Pubs id:
pubs:172724
UUID:
uuid:fcc2473b-1967-4e67-8aa1-e763a7c605dd
Local pid:
pubs:172724
Deposit date:
2012-12-19

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