Bayesian density estimation and model selection using nonparametric hierarchical mixtures

Raffaele Argiento, Alessandra Guglielmi, Antonio Pievatolo

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

23 Citazioni (Scopus)


A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This class, namely mixtures of parametric densities on the positive reals with normalized generalized gamma process as mixing measure, is very flexible in the detection of clusters in the data. With an almost sure approximation of the posterior trajectories the mixing process a Markov chain Monte Carlo algorithm is run to estimate linear and nonlinear functionals of the predictive distributions. The best-fitting mixing measure found by minimizing a Bayes factor for parametric against nonparametric alternatives. Simulated and historical data illustrate the method, finding a trade-off between the best- fitting model and the correct identification of the number of components in the mixture.
Lingua originaleEnglish
pagine (da-a)816-832
Numero di pagine17
Stato di pubblicazionePubblicato - 2010


  • NA


Entra nei temi di ricerca di 'Bayesian density estimation and model selection using nonparametric hierarchical mixtures'. Insieme formano una fingerprint unica.

Cita questo