Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Coverage Modeling for Dependability Analysis of Fault-Tolerant Systems
IEEE Transactions on Computers
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Real-world applications of Bayesian networks
Communications of the ACM
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Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks and their suitability for dependability analysis is now considered by several researchers. In the present paper, we aim at defining a formal comparison between BN and one of the most popular techniques for dependability analysis: Fault Trees (FT). We will show that any FT can be easily mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed using the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc...). Moreover, we will discuss how, by using BN, some additional power can be obtained, both at the modeling and at the analysis level. In particular, dependency among components and noisy gates can be easily accommodated in the BN framework, together with the possibility of performing general diagnostic analysis. The comparison of the two methodologies is carried on through the analysis of an example that consists of a redundant multiprocessor system, with local and shared memories, local mirrored disks and a single bus.