Argumentation networks and Bayesian networks are formalisms used in AI that serve different purposes. However they do share some conceptual similarities: they are both based on directed graphs, and edges represent relationships of influence among variables. In this talk I explore these similarities. Based on this, I propose a unifying perspective that forms the basis for a new approach to probabilistic argumentation.