In this chapter I will tell the story of Bayesian networks from their roots in the eighteenth century to their development in the 1980s, and I will give some more examples of how they are used today. They are related to causal diagrams in a simple way: a causal diagram is a Bayesian network in which every arrow signifies a direct causal relation, or at least the possibility of one, in the direction of that arrow. Not all Bayesian networks are causal, and in many applications it does not matter. However, if you ever want to ask a rung-two or rung-three query about your Bayesian network, you
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