[Tabia et al., 2020]
Tabia, K., Leray, P., and Benferhat, S. (2020). A Guided Tour of Artificial Intelligence Research, volume II: AI Algorithms, chapter 8:Belief Graphical Models for Uncertainty representation and reasoning, page 209-246 Springer. [ DOI | http ]

[Le Dorze et al., 2015]
Le Dorze, A., Duval, B., Garcia, L., Genest, D., Leray, P., and Loiseau, S. (2015). A probabilistic semantics for cognitive maps. In Duval, B., van den Herik, J., Loiseau, S., and Filipe, J., editors, Agents and Artificial Intelligence (Revised Selected Papers), volume 8946 of Lecture Notes in Computer Science, pages 151-169. Springer International Publishing. [ DOI | http ]

[Phan et al., 2015]
Phan, D.-T., Leray, P., and Sinoquet, C. (2015). BIOSTEC2015, Communication in Computer and Information Science, chapter Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-wide Association Studies. Which clustering should be chosen?, page 17. Springer. [ DOI | http ]

[Benferhat et al., 2014]
Benferhat, S., Leray, P., and Tabia, K. (2014). Panorama de l'Intelligence Artificielle, volume 2: Algorithmes pour l’intelligence artificielle, chapter 8:Modèles graphiques pour l’incertitude : inférence et apprentissage, page 26 pages. Cepadues. [ .html ]

[Sinoquet et al., 2013]
Sinoquet, C., Mourad, R., and Leray, P. (2013). Forests of latent tree models to decipher genotype-phenotype associations. In Gabriel, J., Schier, J., Huffel, S., Conchon, E., Correia, C., Fred, A., and Gamboa, H., editors, Biomedical Engineering Systems and Technologies, volume 357 of Communications in Computer and Information Science, pages 113-134. Springer Berlin Heidelberg. [ DOI | http ]

[Donat et al., 2008]
Donat, R., Bouillaut, L., Aknin, P., and Leray, P. (2008). A dynamic graphical model to represent complex survival distributions. In Bedford, T., Quigley, J., Walls, L., Alkali, B., Daneshkhah, A., and Hardman, G., editors, Advances in Mathematical Modeling for Reliability, pages 17-24, Amsterdam. IOS Press. [ http ]

[Leray et al., 2008]
Leray, P., Meganck, S., Maes, S., and Manderick, B. (2008). Causal graphical models with latent variables : learning and inference. In Holmes, D. E. and Jain, L., editors, Innovations in Bayesian Networks: Theory and Applications, Germany, 33 pages. Springer. [ DOI | http ]

[Maes et al., 2007]
Maes, S., Meganck, S., and Leray, P. (2007). An integral approach to causal inference with latent variables. In Russo, F. and Williamson, J., editors, Causality and Probability in the Sciences, pages 17-41. Texts In Philosophy series, London College Publications. [ http ]

[Naïm et al., 2007]
Naïm, P., Wuillemin, P.-H., Leray, P., Pourret, O., and Becker, A. (2007). Réseaux bayésiens. Eyrolles, Paris, 3 edition.

[Naïm et al., 2004]
Naïm, P., Wuillemin, P.-H., Leray, P., Pourret, O., and Becker, A. (2004). Réseaux bayésiens. Eyrolles, Paris, 2 edition.