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Auteur: Picchini, Umberto, Samson, Adeline
Résumé: We study the class of state-space models (or hidden Markov models) and perform maximum likelihood inference on the model parameters. We consider a stochastic approximation expectation-maximization (SAEM) algorithm to maximize the likelihood function with the novelty of using approximate Bayesian computation (ABC) within SAEM. The task is to provide each iteration of SAEM with a filtered state of the system and this is achieved using ABC-SMC, that is we used an approximate sequential Monte Carlo ...
Auteur: Brée, Sandra, Eggerickx, Thierry, Sanderson, Jean-Paul, Costa, Rafael
Résumé: International audience

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