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Enabling a Powerful Marine and Offshore Decision‐Support Solution Through Bayesian Network Technique
Author(s) -
EleyeDatubo A. G.,
Wall A.,
Saajedi A.,
Wang J.
Publication year - 2006
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2006.00775.x
Subject(s) - bayes' theorem , bayesian network , influence diagram , computer science , inference , node (physics) , bayesian inference , submarine pipeline , sensitivity (control systems) , data mining , rule of inference , bayesian probability , inference engine , operations research , machine learning , engineering , artificial intelligence , decision tree , geotechnical engineering , structural engineering , electronic engineering
A powerful practical solution is by far the most desired output when making decisions under the realm of uncertainty on any safety‐critical marine or offshore units and their systems. With data and information typically being obtained incrementally, adopting Bayesian network (BN) is shown to realistically deal with the random uncertainties while at the same time making risk assessments easier to build and to check. A well‐matched methodology is proposed to formalize the reasoning in which the focal mechanism of inference processing relies on the sound Bayes's rule/theorem that permits the logic. Expanding one or more influencing nodal parameters with decision and utility node(s) also yields an influence diagram (ID). BN and ID feasibility is shown in a marine evacuation scenario and that of authorized vessels to floating, production, storage, and offloading collision, developed via a commercial computer tool. Sensitivity analysis and validation of the produced results are also presented.