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Benefits of Bayesian Network Models by Christophe Simon, Philippe Weber

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1Bayesian Networks: a Modeling Formalism for System Dependability

For complex systems, it is assumed that the system and the components have a finite number of states or operating levels. If the number of states is reduced to 2, then a binary hypothesis is used; otherwise the system and its components are multi-state. In this case, the evaluation of the reliability of the system becomes difficult, as it must take into account the effects of combinations of failures that are not independent of the multi-state nature of the system components. The result is the development of numerous modeling scenarios that become tedious for the analyst. In such cases, standard modeling procedures are insufficient, mainly due to their basis in Boolean logic or their need for (computationally expensive) randomized simulations.

As mentioned in [BOU 99], the modeling methods that come from artificial intelligence such as Bayesian networks (BN) can provide an effective support in control or maintenance areas, or in risk reduction for industrial systems. BN have powerful modeling and analysis capabilities. They provide a formal framework to handle or process probabilistic events by representing them using discrete random variables [PEA 88, JEN 96]. The relationships between them are represented by conditional probabilities. BN models are based on a powerful formalism of expressing complex dependence and independence between multi-state random variables. This formalism is, therefore, well suited to ...

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