| Paper: | SPTM-P10.1 | ||
| Session: | Estimation | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | MAXIMUM LIKELIHOOD PARAMETER ESTIMATION FOR LATENT VARIABLE MODELS USING SEQUENTIAL MONTE CARLO | ||
| Authors: | Adam Johansen, University of Cambridge, United Kingdom; Arnaud Doucet, University of British Columbia, Canada; Manuel Davy, LAGIS, France | ||
| 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 non-linear non-Gaussian time series model. | ||
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