1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.CO2026
Optimality in importance sampling: a gentle survey
Fernando Llorente, Luca Martino
The performance of the Monte Carlo sampling methods relies on the crucial choice of a proposal density. The notion of optimality is fundamental to design suitable adaptive procedur…
stat.ME2026★ 1 cited
A note on the area under the likelihood and the fake evidence for model selection
L. Martino, F. Llorente
Improper priors are not allowed for the computation of the Bayesian evidence (a.k.a., marginal likelihood), since in this case is not completely specified due to…
cs.LG2025
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data
Samuel Rey, Ernesto Curbelo, Luca Martino +2
This work addresses the problem of graph learning from data following a Gaussian Graphical Model (GGM) with a time-varying mean. Graphical Lasso (GL), the standard method for estim…