Home

Summary. The use of Bayesian non-parametric (BNP) priors in applied statistical modeling has become increasingly popular in the last few years. From the seminal paper of Ferguson (1973, Annals of Statistics), the Dirichlet Process and its extensions have been increasingly used to address inferential problems in many fields. Examples range from variable selection in genetics to linguistics, psychology, human learning , image segmentation and applications to the neurosciences. The increased interest in non-parametric Bayesian approaches to data analysis is motivated by a number of attractive inferential properties. For example, BNP priors are often used as flexible models to describe th heterogeneity of the population of interest, as they implicitly induce a clustering of the observations into homogeneous groups. In the big data era, there is a growing need of models for describing the main features of large and non trivial datasets that are increasingly available for the easiness of collecting information through the modern networks (for instance Internet).

This proposal wants to provide flexible priors for explaining such datasets, in particular two research lines will be developed:

1. Vectors of Dependent BNP priors for modeling information pooling across unit,

2. Non-exchangeable BNP priors for modeling the heterogeneity of the data .

The successful completion of this research will provide new powerful statistics tools for the analysis of complicated phenomena.

New BNP priors will be proposed as well as the application of some recent BNP priors proposed by the principal investigator