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

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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 > 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:	Date Event 2008 Published
Date of first compliant deposit:	17 December 2015
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

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