4 papers
Causal invariance in graphical models with latent variables
Marco Borriero, Monia Lupparelli, Giovanni M. Marchetti +1
Causal discovery aims to identify causal relationships among variables from observational or interventional data, typically represented by a directed acyclic graph (DAG). The causa…
Bayesian nonparametric Mallows model for clustering preference data
Lorenzo Zuccato, Veronica Vinciotti, Valeria Vitelli
Preference learning refers to the learning of latent patterns from ranking and preference data of different kinds. Typical aims of preference learning are to infer a shared consens…
Loglinear modelling of huge contingency tables
Veronica Vinciotti, Ernst C. Wit
Contingency tables are the canonical representation of multivariate categorical data. As the size of the contingency table grows exponentially with the number of variables, even a…
Gaussian Graphical Models for Partially Observed Multivariate Functional Data
Marco Borriero, Luigi Augugliaro, Gianluca Sottile +1
In many applications, the variables that characterize a stochastic system are measured along a second dimension, such as time. This results in multivariate functional data and the…