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Particle methods for maximum likelihood estimation in latent variable models

Abstract:

Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing; that is we build a sequence of artificial distributions whose support concentrates itself on the set of maximum likelihood estimates. We sample from these distributions using a sequential Monte Carlo approach. We demonstrate state-of-the-art p...

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Publication status:
Published

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Publisher copy:
10.1007/s11222-007-9037-8

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
STATISTICS AND COMPUTING More from this journal
Volume:
18
Issue:
1
Pages:
47-57
Publication date:
2008-03-01
DOI:
EISSN:
1573-1375
ISSN:
0960-3174
Language:
English
Keywords:
Pubs id:
pubs:172685
UUID:
uuid:acb09c4c-3d7c-406a-9008-2c6353319131
Local pid:
pubs:172685
Source identifiers:
172685
Deposit date:
2012-12-19

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