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

Adam Johansen 1 Arnaud Doucet 2 Manuel Davy 3, 4
3 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
4 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : 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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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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