Seminar of the Mathematical Statistics and Data Science research group

Laura D'Angelo

Laura D'Angelo
(University of Milano-Bicocca)
Bio Laura D'Angelo is a postdoctoral researcher in Statistics at the Department of Economics, Management and Statistics at the University of Milano-Bicocca. She earned her PhD in Statistical Science from the University of Padova in 2022, with a thesis on *Bayesian modeling of calcium imaging data*. Her research focuses on Bayesian nonparametrics, model-based clustering, and computational statistics. Her work combines methodological developments with practical applications, especially in the development of flexible and interpretable probabilistic models for complex data.

Abstract

We introduce a Bayesian nonparametric framework to improve classical discriminant analysis, particularly in scenarios with limited sample sizes. The proposed method provides a flexible approach that encompasses both linear and quadratic discriminant analysis as special cases. Its key innovation lies in allowing information sharing across classes to improve the estimation of the class-specific covariance matrices. This is accomplished through a scale-only nonparametric mixture model defined on the space of positive definite matrices. A conjugate nonparametric prior ensures remarkable ease of implementation and tractability, allowing the analytical derivation of posterior distributions for several quantities of interest and facilitating the study of their large-sample properties. Applications to both simulated and real datasets demonstrate the adaptability and effectiveness of the proposed methodology.

Schedule

  • Monday, 02 February 2026, 11:30-12:30, Seminar room 1 @ Povo0

Details