Bayesian Mixture Models for Latent Class Analysis

Raffaele Argiento, Bruno Bodin, Maria De Iorio

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

Bayesian mixture models provide rich and flexible class tools which are particularly useful when there is unobserved heterogeneity in the data. When the number of subpopulations, called components, is assumed random, we allow the data to determine the complexity of the model. The latter property allows us to include a finite mixture model with a random number of components into the wider class of Bayesian nonparametric models. In this paper we consider multivariate discrete data, so that the class of mixtures is also referred to as latent class models. In particular, we let the number of latent classes to be random, and resort to Bayesian nonparametric techniques to devise a MCMC algorithm. The model is illustrated on an benchmark application dealing with role conflict
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of short papers SIS 2020
Pagine428-434
Numero di pagine7
Stato di pubblicazionePubblicato - 2020
Evento50th Scientific Meeting of the Italian Statistical Society - Pisa
Durata: 22 giu 202024 giu 2020

Convegno

Convegno50th Scientific Meeting of the Italian Statistical Society
CittàPisa
Periodo22/6/2024/6/20

Keywords

  • Mixture Models, Latent Class Analysis, Bayesian Inference

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