 The idea that Bayesian networks can be used for probabilistic reasoning in legal fact-finding started gaining traction in late eighties (Friedman 1986) and early nineties (Edwards 1991). Two recent books on the topic with an emphasis on legal applications are Fenton and Neil 2013 [2018] and Taroni et al. 2014. A Bayesian network comprises two components: first, a directed acyclic graph of relations of dependence (represented by arrows) between variables (represented by nodes); second, conditional probability tables. Consider the graphical component first. The graph is acyclic because the arrows connecting the nodes do not form loops. As an illustration, let \(H\) be the claim that the suspect committed the murder, BT the presence of a blood type B match with a crime scene stain, and \(W\) the fact that an eyewitness observed the suspect near the scene around the time of the crime. The graphical component of the Bayesian network would look like this: