TY  - CHAP
AU  - Rosa Maria Arnaldo Valdés
AU  - V. Fernando Gómez Comendador
AU  - Alvaro Rodriguez Sanz
AU  - Eduardo Sanchez Ayra
AU  - Javier Alberto Pérez Castán
AU  - Luis Perez Sanz
ED  - Douglas McNair
Y1  - 2018-11-05
PY  - 2018
T1  - Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation
N2  - Bayesian networks (BN) have recently experienced increased interest and diverse applications in numerous areas, including economics, risk analysis and assets and liabilities management, AI and robotics, transportation systems planning and optimization, political science analytics, law and forensic science assessment of agency and culpability, pharmacology and pharmacogenomics, systems biology and metabolomics, psychology, and policy-making and social programs evaluation. This strong and varied response results not least from the fact that plausibilistic Bayesian models of structures and processes can be robust and stable representations of causal relationships. Additionally, BNs' amenability to incremental or longitudinal improvement through incorporating new data affords extra advantages compared to traditional frequentist statistical methods. Contributors to this volume elucidate various new developments in these aspects of BNs.
BT  - Bayesian Networks
SP  - Ch. 3
UR  - https://doi.org/10.5772/intechopen.79916
DO  - 10.5772/intechopen.79916
SN  - 978-1-83962-323-3
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-23
ER  -