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A Flexible Hierarchical Bayesian Modeling Technique for Risk Analysis of Major Accidents
Author(s) -
Yu Hongyang,
Khan Faisal,
Veitch Brian
Publication year - 2017
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/risa.12736
Subject(s) - event tree , fault tree analysis , event (particle physics) , causation , computer science , event tree analysis , data mining , rare events , risk analysis (engineering) , bayesian probability , probabilistic logic , engineering , reliability engineering , artificial intelligence , statistics , mathematics , medicine , physics , quantum mechanics , political science , law
Safety analysis of rare events with potentially catastrophic consequences is challenged by data scarcity and uncertainty. Traditional causation‐based approaches, such as fault tree and event tree (used to model rare event), suffer from a number of weaknesses. These include the static structure of the event causation, lack of event occurrence data, and need for reliable prior information. In this study, a new hierarchical Bayesian modeling based technique is proposed to overcome these drawbacks. The proposed technique can be used as a flexible technique for risk analysis of major accidents. It enables both forward and backward analysis in quantitative reasoning and the treatment of interdependence among the model parameters. Source‐to‐source variability in data sources is also taken into account through a robust probabilistic safety analysis. The applicability of the proposed technique has been demonstrated through a case study in marine and offshore industry.