Full Professor
Department of Decision Sciences

Personal page

Courses a.y. 2015/2016


Biographical note

Laurea cum laude in Economic and Social Sciences from Università Bocconi. PhD in Statistics, Università di Trento (1989).

Academic CV

Sonia Petrone is Full Professor of Statistics at Bocconi University, Milano. She was previously at 
University of Insubria (1998-2001) and University of Pavia (1991-1998). 
She is the Director of the PhD in Statistics at Bocconi.  
She has been member of the Research Board (CORI) at Bocconi in 2012.
She has conducted research in several universities and research institutions abroad, including 
Stanford University, Cornell University, University of Washington, Duke University, 
Institute for Mathematics and its Applications-University of  Minnesota, Indian Statistical Institute, 
Russian Acandemy of Science, Pontificia Universidad Catòlica de Chile. 

Sonia Petrone is President 2014 of the International Society for Bayesian Analysis (ISBA).
She has been member of the Board of Directors of ISBA (2002--2004 and 2008—2010). 
She is an elected member of the Council of the Institute of Mathematical Statistics (IMS) (2011-2014).
She is co-Editor of Bayesian Analysis. 
She has been member of the  scientific and organizing committee of  numerous international 
scientific meetings and research programs, including the series of workshops on 
"Bayesian Nonparametrics" (BNP) and "Bayesian Inference for Stochastic Processes" (BISP).

She obtained the award indennita' di eccellenza nella ricerca, Bocconi, for 2002, 2003, 2008, 2009 
and the Bocconi award for research profile for 2010-2011-2012-2013.

Research areas

Bayesian Statistics -- foundations, methods and models, applications. Bayesian nonparametrics. Mixtures and latent variable models. Prediction. State space models.

Selected publications

Petrone, S., Rousseau, J and Scricciolo, C. (2013) Bayes and Empirical Bayes: do they merge?. Biometrika, to appear.       Wade, S., Dunson, D. Petrone, S. and Trippa, L. (2013). Improving Prediction from Dirichlet Process Mixtures via Enrichment. Journal of Machine Learning Research, to appear.       Wade, S., Walker, G.W. and Petrone, S. (2013). A predictive study of Dirichlet Process Mixture Models for curve fitting. Scandinavian Journal of Statistics, to appear.     Fortini, S. and Petrone, S. (2012). Hierarchical Reinforced Urn Processes. Statistics and Probability Letters, 82, 1521-1529.       Fortini, S. and Petrone, S. (2012). Predictive construction of priors in Bayesian nonparametrics. Brazilian Journal of Probability and Statistics, 26, 423-449.  Petris, G. and Petrone, S. (2011) State space models in R. Journal of Statistical Software.         Wade, S., Mongelluzzo, S. and Petrone, S. (2010). Enriched conjugate priors for Bayesian nonparametric inference. Bayesian Analysis, 6, 359-386.     Petrone, S., Guindani, M. and Gelfand, A.E. (2009) "Hybrid Dirichlet mixture models for functional data", Journal Royal Statistical Society, Ser. B, 71, 755-782.        Petris, G., Petrone, S. and Campagnoli, P. (2009) Dynamic linear models with R, Springer, N.Y.        Trippa, L., Bulla, P. and Petrone, S. (2011) "Extended Bernstein prior via reinforced urn processes" Annals of the Institute of Statistical Mathematicss, 63, 481-469 (online 2009).     Petrone, S. and Veronese, P. (2010) "Feller operators and mixture priors in Bayesian nonparametrics", Statistica Sinica, 20, 379-404.      N.L.Hjort and Petrone, S. (2007) "Nonparametric quantile inference with Dirichlet processes", in: Advances in Statistical Modeling and Inference. Essays in Honor of Kjell A Doksum,  V. Nair Ed., 463-492.     A.E. Gelfand, M. Guindani and Petrone, S. (2007) "Bayesian nonparametric modelling for spatial data using Dirichlet processes" (with discussion), in:Bayesian Statistics 8, J.M. Bernardo, J.O. Berger, Dawid, A.P. and A.F.M. Smith Eds, Oxford University Press. Petrone, S. (2007) Discussion on "Approximating interval hypothesis: p-values and Bayes factors", by J. Russeau; in: Bayesian Statistics 8, J.M. Bernardo, J.O.Berger, Dawid, A.P. and A.F.M. Smith Eds., Oxford University Press.   Petrone, S. (2003) "A predictive point of view  on Bayesian nonparametrics"; in: Highly Structured Stochastic Systems, P. Green, N. Hjort and S. Richardson Eds, Oxford University Press.   Petrone, S. and Wasserman, L. (2002) Consistency of Bernstein Polynomial Density Estimators, Journal of the Royal Statistical Society, Ser. B, 64, 79-100;   Petrone, S. and Veronese, P. (2002) Non Parametric Mixture Priors Based on an Exponential Random Scheme, Statistical Methods and Applications, 11, 1-20.   Campagnoli, P., Muliere,P. and Petrone, S. (2001) Generalized Dynamic Linear Models for Financial Time Series, Applied Stochastic Models in Business and Industry, 17, 27-39;   Petrone, S. and Corielli, F. (2005) Dynamic Regression Using Bernstein Polynomials with Application to Estimation of the Term Structure of Interest Rates Studi Statistici 61, IMQ, Università Bocconi;  Petrone, S. (1999) Random Bernstein Polynomials, Scandinavian Journal of Statistics , 26, 373-393; Petrone, S. (1999)  Bayesian Density Estimation Using Bernstein Polynomials, Canadian Journal of Statistics, 27, 105-126; Petrone, S. (1999) Discussion on "Bayesian nonparametric inference for random distributions and related functions", by Walker, S.G., Damien, P., Laud, P.W., Smith, A.F.M., Journal of the Royal  Statistics Society, Ser. B, 61, 522-523.   Petrone, S., Roberts, G.O. and Rosenthal, J.S. (1999) A Note on Convergence Rates of Gibbs Sampling for Nonparametric Mixtures, Far East Journal of Theoretical Statistics, 3, 213-225;   Petrone, S. and Raftery, A.E. (1997) A Note on the Dirichlet Process Prior in Bayesian Nonparametric Inference with Partial Exchangeability, Statistics and Probability Letters, 36, 69-83.   Mira, A. and Petrone, S. (1996) Bayesian Hierarchical Nonparametric Inference for Change-point Problems; in: J.M. Bernardo, J.O. Berger, A.P. Dawid, A.F.M. Smith (eds.), Bayesian Statistics 5, Oxford University Press, 693-703.   Muliere, P. and Petrone, S. (1993) A Bayesian predictive approach to sequential searching for  an optimal dose: parametric and nonparametric models, Journal of the  Italian Statistical Society, 3, 349-364.   Muliere, P. and Petrone, S. (1992) Generalized Lorenz curve and monotone  dependence orderings, Metron, L, 19-38.