Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems

We discuss the main features of Generalized Continuous Time Bayesian Networks (GCTBN) as a dependability formalism: we resort to two specific case studies adapted from the literature, and we discuss modelling choices, analysis results and advantages with respect to other formalisms. From the modelling point of view, GTCBN 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.