Sonia Petrone

Full Professor
Knowledge groupEconomics, Politics and Decision Sciences
Research domainsBusiness Data Analytics

Biography

Sonia Petrone is a Full Professor in Statistics at Università Bocconi since 2014, where she played an active role in the development of the PhD in Statistics, as Vice-Director and Curricula Responsible (2004-2011) and Director (2011-2018). She is the Director of the Bocconi Summer School in Advanced Statistics and Probability since 2017. She held positions at the University of Pavia (1991-1998) and University of Insubria (1998-2001), before joining Bocconi in 2001. Throughout her career, she conducted extensive research visits in various universities across the USA, Latin America, Europe, India, and Russia. She was President of the International Society for Bayesian Analysis (ISBA) in 2014. 

Her research interests focus on statistical learning and rediction, Bayesian theory and methods, Bayesian non parametrics and statistical machine learning, methods for complex dependence structures and latent variable models, dynamic and evolutionary systems, random graphs. She is the author of numerous books and articles on her topics of interest. Her works have been published in the Journal of the Royal Statistical Society, Journal of Statistical Software and Statistics and Computing, among others. She was the Editor of Statistical Science (2020-2022), and a co-Editor of Bayesian Analysis (2010-2014). She was awarded an IMS Medallion Lecture in 2018 and an ISBA Foundational Lecture in 2016. 

She graduated in Discipline Economiche e Sociali at Università Bocconi and got a PhD in Statistics from Università degli Studi di Trento.  

 

Recent Publications

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  • 2025
    Enriched Pitman–Yor processes
    RIGON, T., S. PETRONE, B. SCARPA, "Enriched Pitman–Yor processes", Scandinavian Journal of Statistics, 2025, vol. 52, no. 2, pp. 631-657
  • 2025
    Exchangeability, Prediction and Predictive Modeling in Bayesian Statistics
    FORTINI, S., S. PETRONE, "Exchangeability, Prediction and Predictive Modeling in Bayesian Statistics", Statistical Science, 2025, vol. 40, no. 1, pp. 40-67
  • 2023
    Prediction-based uncertainty quantification for exchangeable sequences
    FORTINI, S., S. PETRONE, "Prediction-based uncertainty quantification for exchangeable sequences", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023, vol. 381, no. 2247, pp. 20220142
  • 2023
    Infinite-color randomly reinforced urns with dominant colors
    SARIEV, H., S. FORTINI, S. PETRONE, "Infinite-color randomly reinforced urns with dominant colors", Bernoulli, 2023, vol. 29, no. 1, pp. 132-152
  • 2021
    A closed-form filter for binary time series
    FASANO, A., G. REBAUDO, D. DURANTE, S. PETRONE, "A closed-form filter for binary time series", Statistics and Computing, 2021, vol. 31, no. 4, pp. 47
  • 2021
    Predictive Constructions Based on Measure-Valued Pólya Urn Processes
    FORTINI, S., S. PETRONE, H. SARIEV, "Predictive Constructions Based on Measure-Valued Pólya Urn Processes", Mathematics, 2021, vol. 9, no. 22, pp. 2845-2846
  • 2020
    Quasi-Bayes properties of a procedure for sequential learning in mixture models
    FORTINI, S., S. PETRONE, "Quasi-Bayes properties of a procedure for sequential learning in mixture models", Journal of the Royal Statistical Society. Series B - Statistical Methodology, 2020, vol. 82, no. 4, pp. 1087-1114
  • 2018
    On a notion of partially conditionally identically distributed sequences
    FORTINI, S., S. PETRONE, P. SPORYSHEVA, "On a notion of partially conditionally identically distributed sequences", Stochastic Processes and their Applications, 2018, vol. 128, no. 3, pp. 819-846
  • 2017
    Predictive characterization of mixtures of Markov chains
    FORTINI, S., S. PETRONE, "Predictive characterization of mixtures of Markov chains", Bernoulli, 2017, vol. 23, no. 3, pp. 1538-1565
  • 2016
    Predictive Distribution (de Finetti's View)
    FORTINI, S., S. PETRONE, "Predictive Distribution (de Finetti's View)" in Wiley StatsRef: Statistics Reference Online., N. Balakrishnan, Theodore Colton, Brian Everitt, Walter Piegorsch, Fabrizio Ruggeri, Jozef L. Teugels (Eds.), Wiley, pp. 1-9, 2016

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