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Benefits of Bayesian Network Models by Christophe Simon, Philippe Weber

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Foreword by L. Portinale

Probabilistic graphical models and Bayesian belief networks, in particular, have definitely become a reference formalism in dependability modeling and assessment. The graphical structure, together with the compact representation of the joint distribution of the system variables of interest, provides the reliability engineer with a powerful tool at both the modeling and analytical levels.

The dependency structure, induced by the graph component of the formalism, allows the modeler to make explicit a set of reasonable independence assumptions that may lead to huge simplification at the computational level, as well as with respect to the problem of probability elicitation, without compromising the suitability of the model produced to the actual real-world application.

Standard dependability models usually fit into two categories: 1) combinatorial models (as fault trees or reliability block diagrams) – they determine the occurrence of an undesired event through a combinatorial composition of sub-events; this class of model is very easy to analyze, but it cannot model situations involving complex dependencies among system components and sub-systems; 2) state–space models (such as Markov chains or petri nets) – they allow complex interactions among system parts to be modeled, but they may incur the “state-explosion” problem; this usually means that the analysis has to be performed by considering the cross-product of the system variables, producing a potentially ...

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