Seminario 10 novembre 2005 h. 16.30 Aula IMQ, stanza n. 137 Nicolas Chopin, University of Bristol "Sequential Monte Carlo for estimation and state number determination in hidden Markov models" We rewrite the system equation of a hidden Markov model so as to label the components by order of appearance, and make explicit the random behaviour of the number of components m_t. We argue that this reformulation is often the best way to achieve identifiability, as it facilitates the interpretation of the posterior density, and the estimation of the number of components that have appeared in a given sample. We develop a Population Monte Carlo algorithm for estimating the reformulated model, which relies on particle filtering and Gibbs sampling. Our algorithm has a computational cost similar to that of a MCMC sampler, and is much less likely to be affected by label switching, that is the possibility of getting trapped in a local mode of the posterior density. The extension to trans-dimensional priors is also considered. The approach is illustrated by several real data examples.Title: Istituto Metodi Quantitativi - Università L
Istituto
Metodi Quantitativi - Università L. Bocconi
Viale Isonzo, 25 - 20135 Milano Tel. 02-58365629 - Fax 02-58365630 SEMINARIO “Sequential Nicolas
Chopin University
of Bristol Giovedì, 10 novembre 2005 – ore 16.30 Aula IMQ – stanza n.137 _____________________________________________________________________ Abstract: We rewrite the system equation of a hidden Markov model so as to label
the components by order of appearance, and make explicit the random behaviour
of the number of components m_t. We argue that this
reformulation is often the best way to achieve identifiability,
as it facilitates the interpretation of the posterior density, and the
estimation of the number of components that have appeared in a given sample. We
develop a Population |