2024

• Leonelli, M. (2024). Predicting and understanding shooting performance in professional biathlon: A Bayesian approach arXiv preprint arXiv:2411.02000. [arXiv] [bib]
• Leonelli, M. (2024). bnRep: A repository of Bayesian networks from the academic literature arXiv preprint arXiv:2409.19158. [arXiv] [bib]
• Leonelli, M., Smith, J.Q., & Wright, S.K. (2024). The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks. arXiv preprint arXiv:2407.04667. [arXiv] [GitHub] [bib]
• Ballester-Ripoll, R., & Leonelli, M. (2024). Global sensitivity analysis of uncertain parameters in Bayesian networks. arXiv preprint arXiv:2406.05764. [arXiv] [bib]
• Carter, J. S., Leonelli, M., Riccomagno, E., & Varando, G. (2024). Learning staged trees from incomplete data. In Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:231-252. [doi] [arXiv] [bib]
• Leonelli, M., & Varando, G. (2024). Context-specific refinements of Bayesian network classifiers. In Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:182-198. [doi] [arXiv] [bib]
• Gonzalez Soffner, C.R., & Leonelli, M. (2024). An analysis of factors impacting team strengths in the Australian Football League using time-variant Bradley-Terry models. arXiv preprint arXiv:2405.12588. [arXiv] [GitHub] [bib]
• Carter, J. S., Leonelli, M., Riccomagno, E., & Ugolini, A. (2024). Staged trees for discrete longitudinal data. arXiv preprint arXiv:2401.04297. [arXiv] [bib]
• Leonelli, M., & Varando, G. (2024). Robust learning of staged tree models: A case study in evaluating transport services. Socio-Economic Planning Sciences 95, 102030. [doi] [arXiv] [bib]
• Varando, G., Carli, F., & Leonelli, M. (2024). Staged trees and asymmetry-labeled DAGs. Metrika (to appear). [doi] [arXiv] [GitHub] [bib]
• Leonelli, M., & Varando, G. (2024). Learning and interpreting asymmetry-labeled DAGs: A case study on COVID-19 fear. Applied Intelligence, 54(2), 1734-1750. [doi] [arXiv] [bib]
• Leonelli, M., & Varando, G. (2024). Structural learning of simple staged trees. Data Mining and Knowledge Discovery, 38(3), 1520-1544. [doi] [arXiv] [GitHub] [bib]
• Filigheddu, M. T., Leonelli, M., Varando, G., Gómez-Bermejo, M. Á., Ventura-Díaz, S., Gorospe, L., & Fortún, J. (2024). Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi. Computational and Structural Biotechnology Journal, 24, 12-22. [doi] [bib]

2023

• Ballester-Ripoll, R., & Leonelli, M. (2023). The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. International Journal of Approximate Reasoning, 159, 108929. [doi] [arXiv] [GitHub] [bib]
• Carli, F., Leonelli, M., & Varando, G. (2023). A new class of generative classifiers based on staged tree models. Knowledge-Based Systems, 268, 110488. [doi] [arXiv] [GitHub] [bib]
• Crimaldi, F., & Leonelli, M. (2023). AI and the creative realm: A short review of current and future applications. arXiv preprint arXiv:2306.01795. [arXiv] [bib]
• Leonelli, M., & Varando, G. (2023). Context-specific causal discovery for categorical data using staged trees. In International Conference on Artificial Intelligence and Statistics (pp. 8871-8888). PMLR. [doi] [arXiv] [GitHub] [bib]
• Leonelli, M., Ramanathan, R., & Wilkerson, R. L. (2023). Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package. Knowledge-Based Systems, 278, 110882. [doi] [arXiv] [GitHub] [Software] [bib]

2022

• Leonelli, M., & Riccomagno, E. (2022). A geometric characterization of sensitivity analysis in monomial models. International Journal of Approximate Reasoning, 151, 64-84. [doi] [arXiv] [bib]
• Ballester-Ripoll, R., & Leonelli, M. (2022). Computing Sobol indices in probabilistic graphical models. Reliability Engineering & System Safety, 225, 108573. [doi] [arXiv] [GitHub] [bib]
• Ballester-Ripoll, R., & Leonelli, M. (2022). You only derive once (YODO): Automatic differentiation for efficient sensitivity analysis in Bayesian networks. In International Conference on Probabilistic Graphical Models (pp. 169-180). PMLR. [doi] [arXiv] [GitHub] [bib]
• Leonelli, M., & Varando, G. (2022, September). Highly efficient structural learning of sparse staged trees. In International Conference on Probabilistic Graphical Models (pp. 193-204). PMLR. [doi] [arXiv] [bib]
• Görgen, C., Leonelli, M., & Marigliano, O. (2022). The curved exponential family of a staged tree. Electronic Journal of Statistics, 16(1), 2607-2620. [doi] [arXiv] [bib]
• Carli, F., Leonelli, M., Riccomagno, E., & Varando, G. (2022). The R package stagedtrees for structural learning of stratified staged trees. Journal of Statistical Software, 102, 1-30. [doi] [arXiv] [GitHub] [bib]

2021

• Lattanzi, C., & Leonelli, M. (2021). A change-point approach for the identification of financial extreme regimes. Brazilian Journal of Probability and Statistics, 35(4), 811-837. [doi] [arXiv] [bib]

2020

• de Carvalho, M., Leonelli, M., & Rossi, A. (2020). Tracking change-points in multivariate extremes. arXiv preprint arXiv:2011.05067. [arXiv] [bib]
• Leonelli, M., & Gamerman, D. (2020). Semiparametric bivariate modelling with flexible extremal dependence. Statistics and Computing, 30, 221-236. [doi] [arXiv] [GitHub] [bib]
• Görgen, C., & Leonelli, M. (2020). Model-preserving sensitivity analysis for families of Gaussian distributions. The Journal of Machine Learning Research, 21(1), 3257-3288. [doi] [bib]
• Leonelli, M., Riccomagno, E., & Smith, J. Q. (2020). Coherent combination of probabilistic outputs for group decision making: An algebraic approach. OR Spectrum, 42, 499-528. [doi] [arXiv] [bib]

2019

• Leonelli, M. (2019). Sensitivity analysis beyond linearity. International Journal of Approximate Reasoning, 113, 106-118. [doi] [arXiv] [bib]

2017

• Leonelli, M., Görgen, C., & Smith, J. Q. (2017). Sensitivity analysis in multilinear probabilistic models. Information Sciences, 411, 84-97. [doi] [arXiv] [bib]
• Leonelli, M., & Smith, J. Q. (2017). Directed expected utility networks. Decision Analysis, 14(2), 108-125. [doi] [bib]
• Leonelli, M., Riccomagno, E., & Smith, J. Q. (2017). A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams. Annals of Mathematics and Artificial Intelligence, 81, 273-313. [doi] [bib]

2015

• Leonelli, M., & Smith, J. Q. (2015). Bayesian decision support for complex systems with many distributed experts. Annals of Operations Research, 235, 517-542. [doi] [arXiv] [bib]
• Görgen, C., Leonelli, M., & Smith, J. Q. (2015). A differential approach for staged trees. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU (pp. 346-355). [doi] [arXiv] [bib]
• Smith, J. Q., Barons, M. J., & Leonelli, M. (2015). Coherent frameworks for statistical inference serving integrating decision support systems. arXiv preprint arXiv:1507.07394. [arXiv] [bib]

2013

• Leonelli, M., & Smith, J. Q. (2013). Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management. In IEEE 29th International Conference on Data Engineering Workshops (ICDEW) (pp. 181-192). [doi] [bib]

Manuele Leonelli