In Bayesian Nonparametrics partial exchangeability is a useful assumption tailored for heterogeneous, though related, groups of observations. Recent contributions in Bayesian literature have focused on the construction of dependent nonparametric priors to accommodate for partially exchangeable sequences of observations. In the present paper we concentrate on vectors of hierarchical Pitman-Yor processes, in which the dependence is created by choosing a common random base measure for each group of observations. These hierarchical processes are then used to define dependent hierarchical mixtures. We finally apply the model to estimate densities arising from multiple groups of observations by performing a suitable Gibbs sampling algorithm.

Camerlenghi, F., Lijoi, A., Pruenster, I. (2018). Density Estimation via Hierarchies of Nonparametric Priors. In JSM Proceedings.

Density Estimation via Hierarchies of Nonparametric Priors

Camerlenghi, F;
2018

Abstract

In Bayesian Nonparametrics partial exchangeability is a useful assumption tailored for heterogeneous, though related, groups of observations. Recent contributions in Bayesian literature have focused on the construction of dependent nonparametric priors to accommodate for partially exchangeable sequences of observations. In the present paper we concentrate on vectors of hierarchical Pitman-Yor processes, in which the dependence is created by choosing a common random base measure for each group of observations. These hierarchical processes are then used to define dependent hierarchical mixtures. We finally apply the model to estimate densities arising from multiple groups of observations by performing a suitable Gibbs sampling algorithm.
No
paper
Bayesian nonparametrics, partial exchangeability, hierarchical process, Pitman-Yor process, density estimation, mixture model
English
Joint Statistical Meeting 2018
9780983937586
Camerlenghi, F., Lijoi, A., Pruenster, I. (2018). Density Estimation via Hierarchies of Nonparametric Priors. In JSM Proceedings.
Camerlenghi, F; Lijoi, A; Pruenster, I
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/216642
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