Versión industria (Etxe-tar y Repsol) + PID2019-109247GB-I00
This work has been partially supported by the Spanish Ministry of Science and Innovation through the PID2019-109247GB-I00 and RTC2019-006871-7 projects, by Etxe-tar Group and Aingura IoT through
the “Research and Development of Methodology in A.I oriented to industrial cases of use of continuous ultra-high speed data DSTREAMS” project, and by Repsol through the “Bayesian Approach for AI” project.
Versión FFBVA + PID2019-109247GB-I00:
This work has been partially supported by the Spanish Ministry of Science and Innovation through the PID2019-109247GB-I00 project, and by the BBVA Foundation´s (2019 Call) through the “Score-based
nonstationary temporal Bayesian networks. Applications in climate and neuroscience” and “Outcome prediction and treatment efficiency in patients hospitalized with Covid-19 in Madrid: A Bayesian network
approach” projects.
Versión SGA3 + PID2019-109247GB-I00:
This work has been partially supported by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific
Grant Agreement N.º 945539 (Human Brain Project SGA3) and partly by the Spanish Ministry of Science and Innovation through the PID2019-109247GB-I00 project.
