L. Enrique Sucar, Probabilistic Graphical Models: Principles and Applications, 2nd Edition, Springer, 2021

L. Enrique Sucar, Probabilistic Graphical Models: Principles and Applications, Springer, 2015

L. E. Sucar, J. Hoey, E. Morales (Eds.). “Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions”, IGI Global Publishing Company, 2011.

Publicaciones recientes:

Jonathan Serrano-Pérez, Raquel Díaz-Hernández and L. Enrique Sucar, “Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies”, Experimental Astronomy, August 2024

SERGIO ARREDONDO-SERRANO, JOSE MARTINEZ-CARRANZA and L. ENRIQUE SUCAR, “Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review”, IEEE Access, July 2024.

Arquímides Mendez, Eduardo Morales, L. Enrique Sucar, “CARL: A Synergistic Framework for Causal Reinforcement Learning”, IEEE Access, Vol. 11, 2023.

Heikel Yervilla-Herrera, Israel Becerra, Rafael Murrieta-Cid, L. Enrique Sucar, Eduardo F. Morales, “Bayesian Probabilistic Stopping Test and Asymptotic Shortest Time Trajectories for Object Re- construction with a Mobile Manipulator Robot”, Journal of Intelligent and Robotic Systems, 105(4), 1–17, 2022.

Verónica Rodríguez-López, Luis Enrique Sucar, “Knowledge transfer for causal discovery”, Inter- national Journal of Approximate Reasoning 143, 1–25, 2022.

M. A. Palacios-Alonso, H. J. Escalante and L. E. Sucar, “Detecting and Identifying Global Visual Novelties in Driving Scenarios,” 2022 IEEE Intelligent Vehicles Symposium (IV), 2022, pp. 1649- 1654.

Serrano, S.A., Martinez-Carranza, J., Sucar, L.E., “Inter-task Similarity Measure for Heterogeneous Tasks”. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds) RoboCup 2021: Robot World Cup XXIV. RoboCup 2021. Lecture Notes in Computer Science, vol 13132. Springer, Cham, 2022.

Mauricio Gonzalez Soto, Ivan Feliciano-Avelino, Luis Enrique Sucar, Hugo Jair Escalante Balderas, “Learning a Causal Structure: a Bayesian Random Graph Approach”, Neural Computing and Applications, 1–13, Sept. 2021.

Jonathan Serrano-P ́erez, Enrique Sucar, “Artificial Datasets for Hierarchical Classification”, Expert Systems With Applications, Vol. 182, 2021.

Sergio Serrano, Elizabeth Santiago, Eduardo Morales, Jos ́e Martínez-Carranza, L. Enrique Sucar, “Knowledge-Based Hierarchical POMDPs for Task Planning”, Journal of Intelligent & Robotic Systems, 101, 82, 2021 .

J. Rivas, L. E. Sucar, F. Orihuela, et al, “Multi-label and Multimodal Classifier for Affective States Recognition in Virtual Rehabilitation”, IEEE Transactions on Affective Computing, 2021

Jonathan Serrano-Pérez, L. Enrique Sucar, “PGM PyLib: A Toolkit for Probabilistic Graphical Models in Python”, The 10th International Conference on Probabilistic Graphical Models, Aalborg, September 23-25, 2020