Nicola Torelli

Nicola Torelli is full professor of Statistics at the University of Trieste, Italy. At the University of Trieste he currently teaches Statistical methods for Data Science in the Master of Data Science and Scientific Computing, Statistical and machine learning in the Master of Statistics and Actuarial Sciences and Advanced statistical methods for PhD students in Applied Data Science and Artificial Intelligence. 
He has been President of the Italian Statistical Society and currently is the coordinator of the Statistics and Data Science Group of the Italian Statistical Society.
His recent fields of scientific interest are:
- Supervised and unsupervised classification technique
- Bayesian Hierarchical Models with applications.
 

Selected Publications

• Macrì Demartino, R., Egidi, L., & Torelli, N. (2024). Alternative ranking measures to predict international football results. Computational Statistics, 1-19.
  DOI: https://doi.org/10.1007/s00180-024-01585-z
• Egidi, L., Pappada, R., Pauli, F., & Torelli, N. (2024). pivmet: an R package proposing pivotal methods for consensus clustering and mixture modelling. Journal of Open Source Software, 9(98), 6461.
  DOI: https://doi.org/10.21105/joss.06461
• Egidi, L., Pauli, F., Torelli, N., & Zaccarin, S. (2023). Clustering spatial networks through latent mixture models. Journal of the Royal Statistical Society Series A: Statistics in Society, 186(1), 137-156.
  DOI: https://doi.org/10.1093/jrsssa/qnac002
• De Stefano, D., Pauli, F., & Torelli, N. (2022). Preelectoral polls variability: A hierarchical Bayesian model to assess the role of house effects with application to Italian elections. The Annals of Applied Statistics, 16(1), 460-476.
  DOI: https://doi.org/10.1214/21-AOAS1507
• Egidi, L., Pauli, F., & Torelli, N. (2022). Avoiding prior–data conflict in regression models via mixture priors. Canadian Journal of Statistics, 50(2), 491-510.
  DOI: https://doi.org/10.1002/cjs.11637