 Generalizing event trees using Bayesian networksD W R Marsh and G Bearfield Journal of Risk and Reliability, 2008, vol. 222, issue 2, 105-114 Abstract: Bayesian networks comprise a probabilistic modelling technique representing influences between uncertain variables. Event trees are a popular technique for modelling the probability of occurrence of accidents in system safety analyses. In this paper it is shown how event trees can be generalized using Bayesian networks. This approach is applied to generalize an existing analysis of train derailment accidents at different track locations on a commuter railway. A number of separate event trees were used in the original analysis; it is shown how these can be replaced with a single generalized model. The generalized derailment model allows new locations to be analysed by selecting the state of the influencing factors appropriate to the location. Moreover, these factors are explicit in the generalized model, whereas the original event trees only included event probabilities that varied by location, with no explicit representation of the causes of these variations. The behaviour of the factors as parameters in an accident model is described, allowing more flexible system-wide risk analysis. Keywords: Bayesian networks; event trees; risk assessment; risk modelling; railway safety; derailment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:222:y:2008:i:2:p:105-114 Access Statistics for this article More articles in Journal of Risk and Reliability |
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