Rosario Barone

Short Bio

I hold a PhD in Statistics (2020) from Sapienza University of Rome. I held postdoctoral positions in Statistics, first at Sapienza University of Rome and then at the University of Rome Tor Vergata, where I served as an Assistant Professor from March 2023 to June 2025. Since July 2025, I have been a tenure-track Assistant Professor at the Department of Statistical Sciences, Faculty of Economics, Università Cattolica del Sacro Cuore, Milan. My academic journey also includes international experience as a Visiting PhD Student at the University of Plymouth, UK (2018), and as a Visiting Assistant Professor at the University of California, Los Angeles, USA (2025).

Research

My research focuses on methodological statistics and adopts a Bayesian perspective to develop efficient methods for flexible and interpretable models suited to complex phenomena. In particular, it addresses the following topics:

Bayesian Inference for Latent and Multi-State Processes

I work on Bayesian methods for continuous-time multi-state processes, such as semi-Markov and inhomogeneous Markov models, both under partial or irregular observation and with latent state structures. My research focuses on strategies to improve inference through data augmentation and efficient simulation techniques.

Point Processes and Temporal Dependence

I am interested in modeling recurrent events and time-dependent phenomena using point processes, with a focus on latent dynamics and interactions between events over time.

Bayesian Nonparametrics

I study clustering approaches based on Bayesian nonparametric models, with a particular interest in Random Partition Models (RPMs). These models allow for flexible inference on the number and composition of clusters by placing a prior directly on the space of partitions, rather than fixing the number of clusters a priori. My research is primarily devoted to the development of novel models and implementation of efficient computational strategies for posterior inference.

Publications

Please refer to my profiles for a complete list:

• Google Scholar
• ORCID

Selected Publications

• R. Barone, F. Bartolucci, A. Farcomeni and S. Pandolfi (2026+). Multilayer dynamic stochastic block models for analyzing international arms trade patterns, The Annals of Applied Statistics (forthcoming).
• A. Farcomeni and R. Barone (2026). A selective overview of population size estimation in humanitarian upheavals , Statistical Methods & Applications.
• R. Barone, B. Lucey, A. Palma (2026). Biodiversity Commitment and Market Volatility in Emerging Markets: Evidence from a Bayesian Semiparametric Approach , Quantitative Finance.
• R. Barone and A. Tancredi (2026). Dirichlet process multi-state mixture models , Computational Statistics & Data Analysis.
• R. Barone and A. Farcomeni (2025). Latent class multi-state quantile regression with a cure fraction: application to jail recidivism in the U.S. , Journal of the Royal Statistical Society Series A: Statistics in Society.
• R. Barone, A. Farcomeni and M. Mezzetti (2024). Latent Markov time-interaction processes , Journal of Computational and Graphical Statistics.
• R. Barone and L. Dalla Valle (2023). Bayesian nonparametric modeling of conditional multidimensional dependence structures , Journal of Computational and Graphical Statistics.
• R. Barone and A. Tancredi (2022). Bayesian inference for discretely observed continuous time multi‐state models , Statistics in Medicine.

Under Review

• L. Gai, R. Barone, F. Ielasi and A. Palma. A multi-state analysis of government-backed SME loans: distress, recovery, and default in Italy.

• R. Barone, L. Dalla Valle, F. Leisen and C. Villa. Bayesian Model Selection of Vine Copulas: A Loss-Based Perspective.

• R. Barone. Bayesian path augmentation for latent semi-Markov modulated Poisson processes.

Teaching

A.Y. 2026/2027

• Applied Statistics for Business Analytics (EN) – Master's Degree in Innovation and Technology Management, Università Cattolica del Sacro Cuore (Milan)
• Statistical Modelling (EN) – Master's Degree in Economics, Università Cattolica del Sacro Cuore (Milan)
• Statistical Inference (Exercises) (EN) – Master's Degree in Data Analytics for Business, Università Cattolica del Sacro Cuore (Milan)
• Time Series and Spatial Data Analysis (Exercises and R programming) (EN) – Master's Degree in Data Analytics for Business, Università Cattolica del Sacro Cuore (Milan)

About Ross

I grew up in Nocera — known as Nuvkrinum Alafaternum in Oscan and Nuceria in Latin — a historic city nestled between Naples, Salerno, and the Amalfi Coast. I owe much to Nocera, especially my lifelong friendships and my enduring passion for Nocerina, the local football team that I proudly support.

Beyond statistics, I am passionate about cooking, wine, football (more of an obsession than a passion), nature, and music. But above all, I love dogs — all of them. A special mention goes to Trilli, my little angel wagging her tail among the clouds, and Carlotta, who stole my heart in just a few seconds. I am also deeply committed to civil and social rights, which, sadly, can no longer be taken for granted.