Maximum likelihood parameter estimation for latent variable models using sequential Monte Carlo

AM Johansen, A Doucet, M Davy

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

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 contributionMaximum likelihood parameter estimation for latent variable models using sequential Monte Carlo
Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages640 - 643
Number of pages4
ISBN (Print)1-4244-0469-X
DOIs
Publication statusPublished - May 2006

Publication series

Name
ISSN (Print)1520-6149

Bibliographical note

ISBN: 142440469X
Publisher: IEEE
Name and Venue of Conference: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006, Toulouse, 14-19 May
Conference Organiser: IEEE

Fingerprint Dive into the research topics of 'Maximum likelihood parameter estimation for latent variable models using sequential Monte Carlo'. Together they form a unique fingerprint.

Cite this