Abstract
We present a sequential Monte Carlo (SMC) method for maximum likelihood (ML) parameter estimation in latent variable models. Standard methods rely on gradient algorithms such as the Expectation-Maximization (EM) algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing (SA); that is we propose to sample from a sequence of artificial distributions whose support concentrates itself on the set of ML estimates. To achieve this we use SMC methods. We conclude by presenting simulation results on a toy problem and a nonlinear non-Gaussian time series model.
Translated title of the contribution | Maximum likelihood parameter estimation for latent variable models using sequential Monte Carlo |
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Original language | English |
Title of host publication | 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 640 - 643 |
Number of pages | 4 |
ISBN (Print) | 1-4244-0469-X |
DOIs | |
Publication status | Published - May 2006 |
Publication series
Name | |
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ISSN (Print) | 1520-6149 |
Bibliographical note
ISBN: 142440469XPublisher: IEEE
Name and Venue of Conference: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, Toulouse, 14-19 May
Conference Organiser: IEEE