Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management. - Manuele Leonelli; James Q. Smith (ICDE Workshops, 2013)

A Differential Approach for Staged Trees. - Christiane Görgen; Manuele Leonelli; James Q. Smith (ECSQARU, 2015)

Bayesian decision support for complex systems with many distributed experts. - Manuele Leonelli; James Q. Smith (Ann. Oper. Res., 2015)

Sensitivity analysis, multilinearity and beyond. - Manuele Leonelli; Christiane Görgen; Jim Q. Smith (CoRR, 2015)

A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams. - Manuele Leonelli; Eva Riccomagno; Jim Q. Smith (Ann. Math. Artif. Intell., 2017)

Directed Expected Utility Networks. - Manuele Leonelli; Jim Q. Smith (Decis. Anal., 2017)

Sensitivity analysis in multilinear probabilistic models. - Manuele Leonelli; Christiane Görgen; Jim Q. Smith (Inf. Sci., 2017)

A geometric characterisation of sensitivity analysis in monomial models. - Manuele Leonelli; Eva Riccomagno (CoRR, 2019)

Sensitivity analysis beyond linearity. - Manuele Leonelli (Int. J. Approx. Reason., 2019)

Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions. - Christiane Görgen; Manuele Leonelli (J. Mach. Learn. Res., 2020)

Coherent combination of probabilistic outputs for group decision making: an algebraic approach. - Manuele Leonelli; Eva Riccomagno; Jim Q. Smith (OR Spectr., 2020)

Semiparametric bivariate modelling with flexible extremal dependence. - Manuele Leonelli; Dani Gamerman (Stat. Comput., 2020)

Staged trees and asymmetry-labeled DAGs. - Gherardo Varando; Federico Carli; Manuele Leonelli (CoRR, 2021)

Global sensitivity analysis in probabilistic graphical models. - Rafael Ballester-Ripoll; Manuele Leonelli (CoRR, 2021)

You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. - Rafael Ballester-Ripoll; Manuele Leonelli (PGM, 2022)

Highly Efficient Structural Learning of Sparse Staged Trees. - Manuele Leonelli; Gherardo Varando (PGM, 2022)

A geometric characterization of sensitivity analysis in monomial models. - Manuele Leonelli; Eva Riccomagno (Int. J. Approx. Reason., 2022)

The R Package stagedtrees for Structural Learning of Stratified Staged Trees. - Federico Carli; Manuele Leonelli; Eva Riccomagno; Gherardo Varando (J. Stat. Softw., 2022)

Computing Sobol indices in probabilistic graphical models. - Rafael Ballester-Ripoll; Manuele Leonelli (Reliab. Eng. Syst. Saf., 2022)

Context-Specific Causal Discovery for Categorical Data Using Staged Trees. - Manuele Leonelli; Gherardo Varando (AISTATS, 2023)

AI and the creative realm: A short review of current and future applications. - Fabio Crimaldi; Manuele Leonelli (CoRR, 2023)

A new class of generative classifiers based on staged tree models. - Federico Carli; Manuele Leonelli; Gherardo Varando (Knowl. Based Syst., 2023)

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. - Rafael Ballester-Ripoll; Manuele Leonelli (Int. J. Approx. Reason., 2023)

Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package. - Manuele Leonelli; Ramsiya Ramanathan; Rachel L. Wilkerson (Knowl. Based Syst., 2023)

Learning Staged Trees from Incomplete Data. - Jack Storror Carter; Manuele Leonelli; Eva Riccomagno; Gherardo Varando (PGM, 2024)

Context-Specific Refinements of Bayesian Network Classifiers. - Manuele Leonelli; Gherardo Varando (PGM, 2024)

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. - Manuele Leonelli; Gherardo Varando (Appl. Intell., 2024)

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks. - Manuele Leonelli; Jim Q. Smith; Sophia K. Wright (CoRR, 2024)

Structural learning of simple staged trees. - Manuele Leonelli; Gherardo Varando (Data Min. Knowl. Discov., 2024)

Global sensitivity analysis of uncertain parameters in Bayesian networks. - Rafael Ballester-Ripoll; Manuele Leonelli (Int. J. Approx. Reason., 2025)

The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks. - Manuele Leonelli; Jim Q. Smith (Int. J. Approx. Reason., 2025)

bnRep: A repository of Bayesian networks from the academic literature. - Manuele Leonelli (Neurocomputing, 2025)

Will AI Take My Job? Evolving Perceptions of Automation and Labor Risk in Latin America. - Andrea Cremaschi; Dae-Jin Lee; Manuele Leonelli (CoRR, 2025)

Understanding support for AI regulation: A Bayesian network perspective. - Andrea Cremaschi; Dae-Jin Lee; Manuele Leonelli (CoRR, 2025)