Inferential evidence is largely governed by Bayes’ theorem—an eighteenth-century theorem for the calculation of probabilities under changing or uncertain conditions. Its role in the presentation of forensic evidence has been highlighted recently by appeal cases which have thrown into relief the question of probability versus “hard evidence.” Bayesian networks are algorithmically extended ways of calculating or formulating relationships between different kinds of variables—inferential, probabilistic relations as distinct from causal ones. They are fundamental to the understanding of the probabilistic nature of all forensic evidence. Bayes is used as a portrait of reasoning, or of rationality as a decision-making process, and hence as a modeling process for “learning” algorithms, and has been utilized as a core model for understanding phenomena ranging from financial markets, to counterinsurgency, to forms of design as a means of crime or terror prevention. Evidence ceases to become located purely within a juridical framework and is taken to an organizational and managerial scale where we can begin to see relationships between police, military, finance, management, and so on.