Filippo Ascolani

Invited Session
Bayesian contributions to Statistical Learning
Organizer/Chair: Federico Camerlenghi (Università di Milano-Bicocca)
Discussant: Alessandra Guglielmi (Politecnico di Milano)
Room: T32
Floor: ground
Short summary: Bayesian statistical methods include a large variety of effective tools to face prediction, statistical learning and estimation via a principled approach. The session is focused on some recent hierarchical Bayesian models, which are designed for different applied problems arising, e.g., in precision medicine and cancer detection. Both Bayesian parametric and nonparametric methods will be discussed.

Paper
Linear models with assumptions-free residuals: a Bayesian Nonparametric approach. Author(s)  Filippo Ascolani   Valentina Ghidini