 The influence of Bayesian networks has been far more widespread, both across disciplines and in technological application—application made possible only with computers. Grounded in the work of Judea Pearl (1988, 2000; Pearl &amp; Mackenzie 2018), Bayesian networks are directed acyclic graphs in which nodes represent variables that can be read as either probabilities or degrees of belief and directed edges as conditional probabilities from “parent” to “child”. By the Markov convention, the value of a node is independent of all other nodes that are not its descendants, conditional on its parents. A standard textbook example is shown in  Figure 6.