MAXIMUM LIKELIHOOD PARAMETER ESTIMATION FOR LATENT VARIABLE MODELS USING SEQUENTIAL MONTE CARLO - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2006

MAXIMUM LIKELIHOOD PARAMETER ESTIMATION FOR LATENT VARIABLE MODELS USING SEQUENTIAL MONTE CARLO

Résumé

We present a sequential Monte Carlo (SMC) method for maximum likelihood (ML) parameter estimation in latent variable models. Stan- dard 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 sim- ulated 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 con- clude by presenting simulation results on a toy problem and a non- linear non-Gaussian time series model.
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Dates et versions

inria-00119988 , version 1 (12-12-2006)

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  • HAL Id : inria-00119988 , version 1

Citer

Adam Johansen, Arnaud Doucet, Manuel Davy. MAXIMUM LIKELIHOOD PARAMETER ESTIMATION FOR LATENT VARIABLE MODELS USING SEQUENTIAL MONTE CARLO. 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing : conference proceedings, May 14-19, 2006, Toulouse, 2006, Toulouse, France. ⟨inria-00119988⟩
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