Computing Sciences

Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Rea-soning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when in-dependencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of sta-tistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the no-tion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the con-text of legal reasoning, the correct treatment of statistical evidence is important. 1