Modeling and Analysis of Dependable Systems through Generalized Continuous Time Bayesian Networks

We discuss the main features of Generalized Continuous Time Bayesian Networks (GCTBN) as a dependability formalism: we resort to a specific case study adapted from the literature, and we discuss modeling choices, analysis results and advantages with respect to other formalisms. From the modeling point of view, GTCBNs allow the introduction of general probabilistic dependencies and conditional dependencies in state transition rates of system components. From the analysis point of view, any task ascribable to a posterior probability computation can be implemented, among which the computation of system unreliability, importance indices, system monitoring, prediction and diagnosis. Future works will concentrate on the modeling of more general dependencies in the framework, as well as on the definition of flexible inference algorithms in addition to existing ones.