Education
PhD in Statistics, Bocconi University, 2006
Research Interests
Bayesian nonparametric statistics, asymptotic theory for posterior inference, large deviations, cluster analysis, stochastic block models
Affiliations
Professor of Statistics, University of Torino
Selected Works
- De Blasi, P., Gil-Leyva, M. F. (2023). Gibbs sampling for mixtures in order of appearance: the ordered allocation sampler. Journal of Computational and Graphical Statistics 32 (4), 1416-1424.
- Catalano, M., De Blasi, P., Lijoi, A., Prünster, I. (2022). Posterior asymptotics for boosted hierarchical Dirichlet process mixtures. Journal of Machine Learning Research 23 (80), 1−23, 2022.
- Arbel, J., De Blasi, P., Prünster, I. (2019). Stochastic approximations to the Pitman-Yor process. Bayesian Analysis 14, 1201-1219.
- De Blasi, P., Lijoi, A., Pruenster, I. (2013). An asymptotic analysis of a class of discrete nonparametric priors. Statistica Sinica 23, 1299-1322.
- De Blasi, P. Peccati, G., Pruenster I. (2009). Asymptotics for posterior hazards. The Annals of Statistics 37, 1906-1945.
