Bayesian networks implementation of the Dempster Shafer theory to model reliability uncertainty

In many reliability studies based on data, reliability engineers face incompleteness and incoherency problems in the data. Probabilistic tools badly handle these kinds of problems thus, it is better to use formalism from the evidence theory. From our knowledge, there is a lack of industrial tools that implement this theory. In this paper, the implementation of the Dempster Shafer theory in a Bayesian network tool is proposed in order to compute system reliability and manage epistemic uncertainty propagation. The basic concepts used are presented and some numerical experiments are made to show how uncertainty is propagated.

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