Abstract
Weighted dependency trees (WDTs) are used in a multitude of approaches to system analysis, such as fault tree analysis or event tree analysis. In fact, any acyclic graph can be transformed to a WDT. Important decisions are often based on WDT analysis. Common for all WDT-based approaches is the inherent uncertainty due to lack or inaccuracy of the input data. In order to indicate credibility of such WDT analysis, uncertainty handling is essential. There is however, to our knowledge, no comprehensive evaluation of the uncertainty handling approaches in the context of the WDTs. This chapter aims to rectify this. We concentrate on approaches applicable for epistemic uncertainty related to empirical input. The existing and the potentially useful approaches are identified through a systematic literature review. The approaches are then outlined and evaluated at a high-level, before a restricted set undergoes a more detailed evaluation based on a set of pre-defined evaluation criteria. We argue that the epistemic uncertainty is better suited for possibilistic uncertainty representations than the probabilistic ones. The results indicate that precision, expressiveness, predictive accuracy, scalability on real-life systems, and comprehensibility are among the properties which differentiate the approaches. The selection of a preferred approach should depend on the degree of need for certain properties relative to others, given the context. The right trade off is particularly important when the input is based on both expert judgments and measurements. The chapter may serve as a roadmap for examining the uncertainty handling approaches, or as a resource for identifying the adequate one.