It is often the case that data used for the systems reliability assessment comes from more than one information source. Whether they are power plants at different geographical locations, gas transmission pipelines operating in different environment or power transmission networks deployed within various areas. Therefore, different operating conditions, varying maintenance programs and efficiencies have its share in influencing the vulnerability and variability of reliability data. However, in practice it is usually the case that this heterogeneity is neglected leading to the underestimation of underlying uncertainty of the data. Bayesian models are capable of dealing with this kind of uncertainty as opposed
to the frequentists statistical methods. Hierarchical Bayesian modelling technique provides means to quantify not only within-source, but also between-source uncertainties. Even in the case of small data samples it performs well, unlike for example the classical likelihood method which may provide degenerate estimates. In this paper authors investigate the possibility to incorporate this kind of uncertainty into the systems reliability and vulnerability assessment through the Bayesian framework in several cases: gas transmission networks and power transmission grids.
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