Title: Hierarchical normalized finite point process: predictive structure and clustering

Speaker: Raffaele Argiento (Università degli Studi di Bergamo)

Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59

Abstract: Almost surely discrete random probability measures have received close attention in the Bayesian nonparametric community. They have been used to model populations of individuals or latent parameters (in the mixture model setting) composed of unfixed species with unknown proportions. In this framework, data are usually assumed to be exchangeable. However, the latter assumption is not appropriate when data are divided in multiple groups which may share the same species. If so, partially exchangeability accommodates the dependence across populations.