Seminario: “Model-Based Clustering: A Bayesian Nonparametric Perspective”

Giovedì 21 Maggio 2026, alle ore 12 in aula 6 (DEI), il Prof. Raffaele Argiento svolgerà il seminario dal titolo: “Model-Based Clustering: A Bayesian Nonparametric Perspective”

 

Abstract: Mixture models are the prototypical tool for clustering. In the Bayesian framework, a traditional distinction has been made between mixtures with a finite number of components and those with an infinite number. Recently, however, the literature has increasingly recognized that both types of mixture models can be addressed using a unified set of tools -- indeed, the clustering problem is inherently nonparametric in nature.
Adopting this perspective, in this talk, I will first present a general definition of a mixture model with a random number of components, framing it within a nonparametric context and highlighting how its probabilistic structure is naturally linked to point process theory.
Building on this connection, I will show how point process theory can be leveraged to construct repulsive or attractive mixtures. I will illustrate these ideas with an example in which the mixture model is designed to cluster categorical data, emphasizing how dependence between atoms (i.e., attractive interactions) can be exploited to study dependence structures in categorical datasets. Finally, I will discuss an application to animal taxonomy in the context of biodiversity studies.