Research

My research lies at the intersection of Bayesian statistics, Bayesian nonparametrics, network modeling, and computational statistics. I develop flexible Bayesian methods for modeling complex and structured data, with a particular focus on heterogeneous populations, dependent structures, and scalable computational approaches.

Research Interests

Bayesian nonparametrics, network modeling, computational statistics, and Bayesian statistics.

Ongoing Research

Barile, F., Borrotti, M., Nipoti, B., Poli, G. & Stingo, F. C.
Data Integration and Missing Imputation for Joint Bayesian Learning under Multiple Nonignorable Missing Mechanisms.

Barile, F. & Nipoti, B.
Dependent Bernstein–Dirichlet Priors for Multiscale Density Estimation.

Barile, F., Nieto-Barajas, L. E. & Nipoti, B.
A Network of Dependent Dirichlet Processes.

Barile, F., Lunagómez, S. & Nemeth, C.
Covariate-dependent Modeling of Hypergraph Data.