Statistics and Computing

, Volume 18, Issue 1, pp 47–57

Particle methods for maximum likelihood estimation in latent variable models

Article

DOI: 10.1007/s11222-007-9037-8

Cite this article as:
Johansen, A.M., Doucet, A. & Davy, M. Stat Comput (2008) 18: 47. doi:10.1007/s11222-007-9037-8

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.

Keywords

Latent variable models Markov chain Monte Carlo Maximum likelihood Sequential Monte Carlo Simulated annealing 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Mathematics, University WalkUniversity of BristolBristolUK
  2. 2.Department of Statistics & Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  3. 3.LAGIS UMR 8146Villeneuve d’Ascq CedexFrance

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