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Particle methods for maximum likelihood estimation in latent variable models

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Johansen, Adam M., Doucet, Arnaud and Davy, Manuel (2008) Particle methods for maximum likelihood estimation in latent variable models. Statistics and Computing, Vol.18 (No.1). pp. 47-57. doi:10.1007/s11222-007-9037-8 ISSN 0960-3174.

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Official URL: http://dx.doi.org/10.1007/s11222-007-9037-8

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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 performance for several applications of the proposed approach.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Parameter estimation, Latent variables
Journal or Publication Title: Statistics and Computing
Publisher: Springer
ISSN: 0960-3174
Official Date: 2008
Dates:
DateEvent
2008Published
Volume: Vol.18
Number: No.1
Page Range: pp. 47-57
DOI: 10.1007/s11222-007-9037-8
Status: Peer Reviewed
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 17 December 2015
Date of first compliant Open Access: 17 December 2015

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