Manuele Leonelli

Orcid: 0000-0002-2562-5192

According to our database1, Manuele Leonelli authored at least 31 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Structural learning of simple staged trees.
Data Min. Knowl. Discov., May, 2024

bnRep: A repository of Bayesian networks from the academic literature.
CoRR, 2024

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks.
CoRR, 2024

Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks.
CoRR, 2024

Learning Staged Trees from Incomplete Data.
CoRR, 2024

Context-Specific Refinements of Bayesian Network Classifiers.
CoRR, 2024

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear.
Appl. Intell., 2024

2023
Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package.
Knowl. Based Syst., October, 2023

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks.
Int. J. Approx. Reason., August, 2023

A new class of generative classifiers based on staged tree models.
Knowl. Based Syst., 2023

AI and the creative realm: A short review of current and future applications.
CoRR, 2023

Context-Specific Causal Discovery for Categorical Data Using Staged Trees.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Computing Sobol indices in probabilistic graphical models.
Reliab. Eng. Syst. Saf., 2022

The <i>R</i> Package stagedtrees for Structural Learning of Stratified Staged Trees.
J. Stat. Softw., 2022

A geometric characterization of sensitivity analysis in monomial models.
Int. J. Approx. Reason., 2022

Highly Efficient Structural Learning of Sparse Staged Trees.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021
Global sensitivity analysis in probabilistic graphical models.
CoRR, 2021

Staged trees and asymmetry-labeled DAGs.
CoRR, 2021

2020
Semiparametric bivariate modelling with flexible extremal dependence.
Stat. Comput., 2020

Coherent combination of probabilistic outputs for group decision making: an algebraic approach.
OR Spectr., 2020

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions.
J. Mach. Learn. Res., 2020

2019
Sensitivity analysis beyond linearity.
Int. J. Approx. Reason., 2019

A geometric characterisation of sensitivity analysis in monomial models.
CoRR, 2019

2017
Sensitivity analysis in multilinear probabilistic models.
Inf. Sci., 2017

Directed Expected Utility Networks.
Decis. Anal., 2017

A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams.
Ann. Math. Artif. Intell., 2017

2015
Sensitivity analysis, multilinearity and beyond.
CoRR, 2015

Bayesian decision support for complex systems with many distributed experts.
Ann. Oper. Res., 2015

A Differential Approach for Staged Trees.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

2013
Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management.
Proceedings of the Workshops Proceedings of the 29th IEEE International Conference on Data Engineering, 2013


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