Group-dependent finite mixture model

P. Costa Fontichiari, M. Giuliani, Raffaele Argiento, Lucia Paci

Risultato della ricerca: Contributo in libroContributo a convegno


We present a Bayesian nonparametric group-dependent mixture model for clustering. This is achieved by building a hierarchical structure, where the discreteness of the shared base measure is exploited to cluster the data, between and within groups. We study the properties of the group-dependent clustering structure based on the latent parameters of the model. Furthermore, we obtain the joint distribution of the clustering induced by the hierarchical mixture model and define the complete posterior characterization of interest. We construct a Gibbs sampler to perform Bayesian inference and measure performances on simulated and a real data.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCLADAG 2021 Book of abstracts and short papers
Numero di pagine4
Stato di pubblicazionePubblicato - 2021
Evento13-th Scientific Meeting of Classification and Data Analysis Group - FIRENZE -- ITA
Durata: 9 set 202111 set 2021


Convegno13-th Scientific Meeting of Classification and Data Analysis Group


  • Bayesian analysis
  • EPPF
  • Gibbs sampling
  • clustering


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