activity
20162026
most citedYou Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks

10 citations · 25 across the 36 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

stat.AP2024

Predicting and understanding shooting performance in professional biathlon: A Bayesian approach

Manuele Leonelli

Biathlon is a unique winter sport that combines precision rifle marksmanship with the endurance demands of cross-country skiing. We develop a Bayesian hierarchical model to predict…

cs.AI2024

bnRep: A repository of Bayesian networks from the academic literature

Manuele Leonelli

Bayesian networks (BNs) are widely used for modeling complex systems with uncertainty, yet repositories of pre-built BNs remain limited. This paper introduces bnRep, an open-source…

stat.ME2024

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks

Manuele Leonelli, Jim Q. Smith, Sophia K. Wright

Bayesian networks are one of the most widely used classes of probabilistic models for risk management and decision support because of their interpretability and flexibility in incl…

cs.AI2024

Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks

Rafael Ballester-Ripoll, Manuele Leonelli

Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT…

stat.ML2024

Learning Staged Trees from Incomplete Data

Jack Storror Carter, Manuele Leonelli, Eva Riccomagno +1

Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices. Several structural learning routines…

stat.ML2024

Context-Specific Refinements of Bayesian Network Classifiers

Manuele Leonelli, Gherardo Varando

Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretabilit…