This book focuses on modeling several forms of uncertainty (epistemic and aleatory) and dealing with parameter uncertainty in dependability analysis. For this purpose, we model these forms of uncertainty through additive and non-additive theories. Several theories or modeling languages are used.

After presenting some context on uncertainty sources and several theoretical frameworks for modeling the different forms of uncertainty, the book applies them to the assessment of the performance of system reliability or dependability with usual dependability models. Industrial systems or toy systems are used for the sake of illustration. Beyond the usual models in dependability, the concept of evidential networks is introduced. Similar to Bayesian networks but considering non-additive theories, the modeling principle is explained and applied to several forms of uncertainty and on several systems. This modeling tool is also used to compute importance measures which are necessary to improve systems or test the robustness of the assessment even in the context of several parameter uncertainties.