Bayesian Semiparametric Multivariate Change Point Analysis

Stefano Peluso, Shiddharta Chib, Antonietta Mira

Risultato della ricerca: Contributo in libroContributo a convegno

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of abstracts of ISBA 2016 World Meeting
Pagine1
Numero di pagine1
Stato di pubblicazionePubblicato - 2016
EventoISBA 2016 World Meeting - Cagliari
Durata: 13 giu 201617 giu 2016

Convegno

ConvegnoISBA 2016 World Meeting
CittàCagliari
Periodo13/6/1617/6/16

Keywords

  • Bayesian Nonparametrics
  • Change Point

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