TY  - CHAP
AU  - Luiz Antonio Pereira Silva
AU  - João Batista Nunes Bezerra
AU  - Mirko Barbosa Perkusich
AU  - Kyller Costa Gorgônio
AU  - Hyggo Oliveira de Almeida
AU  - Angelo Perkusich
ED  - Douglas McNair
Y1  - 2018-11-05
PY  - 2018
T1  - Continuous Learning of the Structure of Bayesian Networks: A Mapping Study
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. 5
UR  - https://doi.org/10.5772/intechopen.80064
DO  - 10.5772/intechopen.80064
SN  - 978-1-83962-323-3
PB  - IntechOpen
CY  - Rijeka
Y2  - 2024-04-18
ER  -