We develop a general Bayesian semiparametric change-point model in which separate groups of parameters (for example, location and dispersion) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes dened by the various change-points is unknown and given by a Dirichlet process mixture prior. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a multivariate forward-backward algorithm for sampling the various regime indicators.
Peluso, S., Shiddharta, C., Antonietta, M., Bayesian Semiparametric Multivariate Change Point Analysis, Abstract de <<ISBA 2016 World Meeting>>, (Cagliari, 13-17 June 2016 ), non conosciuto, Cagliari 2016: 1-1 [http://hdl.handle.net/10807/118536]
Bayesian Semiparametric Multivariate Change Point Analysis
Peluso, Stefano
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2016
Abstract
We develop a general Bayesian semiparametric change-point model in which separate groups of parameters (for example, location and dispersion) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes dened by the various change-points is unknown and given by a Dirichlet process mixture prior. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a multivariate forward-backward algorithm for sampling the various regime indicators.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.