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martedì, 12 dicembre 2017


Dettagli Pubblicazione
Autori:Andrea Bobbio
Stefania Montani
Luigi Portinale
Area Scientifica:Uncertain Reasoning
Probabilistic Graphical Models
Dependability and Reliability
Titolo:Dynamic Bayesian Networks for Modeling Advanced Fault Tree Features in Dependability Analisys
Apparso su:TR-INF-2004-03-04-UNIPMN
Editore:DiSIT, Computer Science Institute, UPO
Tipo Pubblicazione:Technical Report
Sommario:Fault Trees (FT) are one of the most popular techniques for dependability analysis of large, safety critical systems. It has been shown that FT can be directly mapped into Bayesian Networks (BN) and that the basic inference techniques on the latter may be used to obtain classical parameters computed from the former. In this paper, we show how BN can provide a unified framework in which also Dynamic FT (DFT), a recent extensions able to treat complex types of dependencies, can be represented. In particular, we propose to characterize dynamic gates within the Dynamic Bayesian Network framework (DBN), by translating all the basic dynamic gates into the corresponding DBN model. The approach has been tested on a complex example taken from the literature. Our experimental results testify how DBN can be safely resorted to if a quantitative analysis of the system is required. Moreover, they are able to enhance both the modeling and the analysis capabilities of classical FT approaches, by representing local dependencies and by performing general inference on the resulting model.