Courses a.y. 2022/2023
40027 STOCHASTIC PROCESSES I
40313 INTRODUCTION TO PROBABILITY
40971 PROBABILITY THEORY I
Courses previous a.y.
Sandra Fortini is Associate Professor of Statistics in the Department of Decision Sciences, at Bocconi University.
He has previously been Research fellow (1990-1994) and Researcher (1994-1995) at the Institute for the Applications of Mathematics and Informatics of the National Council of Research, and then Assistant Professor (1995-2007) at Bocconi University.
He is Fellow of the Institute of Mathematics Statistics and of the International Society of Bayesian Analysis, of the Italian Statistical Society (SIS), and of the Italian Mathematical Society (UMI).
Sandra Fortini has graduated in Mathematics at Milano University in 1989, and has gained a Master of Science in Applied Stochastic Systems in 1993 at University College of London.
Asymptotic properties of Bayesian and Maching Learning procedures. Predictive inference. Bayesian Nonparametric models.
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions
Transactions of Machine Learning Research, 2022
Infinite-color randomly reinforced urns with dominant colors
Approximating the operating characteristics of Bayesian Uncertainty directed trial Designs
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2022
Predictive constructions based on measure-valued Pólya urn processes
Large-width functional asymptotics for deep Gaussian neural networks
International Conference on Learning Representations, 2021, 2021
Stable behaviour of infinitely wide deep neural networks
Proceedings of Machine Learning Research. Volume 108: International Conference on Artificial Intelligence and Statistics, 2020
Quasi‐Bayes properties of a procedure for sequential learning in mixture models
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B STATISTICAL METHODOLOGY, 2020
On a notion of partially conditionally identically distributed sequences
STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 2017
Predictive Characterization of Mixtures of Markov Chains
Bernoulli Journal, 2014
Predictive distribution (de Finetti's view)
Wiley StatsRef: Statistics Reference Online, 2016
Hierarchical reinforced urn processes
STATISTICS & PROBABILITY LETTERS, 2012
Predictive construction of priors in Bayesian nonparametrics
REVISTA BRASILEIRA DE PROBABILIDADE E ESTATÍSTICA, 2012
Central Limit Theorem with Exchangeable Summands and Mixtures of Stable Laws as Limits
BOLLETTINO DELLA UNIONE MATEMATICA ITALIANA, 2012
Recursive equations for the predictive distributions of some determinantal processes
STATISTICS & PROBABILITY LETTERS, 2011
A Poisson Approximation for Coloured Graphs Under Exchangeability
A characterization for mixtures of semi-Markov processes
STATISTICS & PROBABILITY LETTERS, 2002
On mixtures of distributions of Markov chains
STOCHASTIC PROCESSES AND THEIR APPLICATIONS, 2002
On the use of concentration functionin Bayesian robustness
Robusts Bayesian analysis, 2000
Exchangeability, predictive distributions and parametric models
SANKHYA. SERIES A, 2000
On parametric models for invariant probability measures
QUADERNI DI STATISTICA, 2000
Differential properties of the concentration function.
SANKHYA. SERIES A, 1997
A central limit problem for partially exchangeable random variables. 1996.
THEORY OF PROBABILITY AND ITS APPLICATIONS, 1996
Concentration function and sensitivity to the prior
JOURNAL OF THE ITALIAN STATISTICAL SOCIETY, 1995
On defining neighbowrhoods of measures through the concentration function.
SANKHYA. SERIES A, 1994
Concentration functions and Bayesian robustness
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 1994
Concentration function and coefficients of divergence for signed measures
JOURNAL OF THE ITALIAN STATISTICAL SOCIETY, 1993
Deep Stable neural networks: large-width asymptotics and convergence rates
Infinite-channel deep convolutional Stable neural networks
Bayesian Deep Learning NeurIPS workshop, 2021
Prediction-based uncertainty quantification for exchangeable sequences
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES A: MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Forthcoming