 Changes of values at the nodes of a Bayesian network (in response to evidence, for example) are updated through belief propagation algorithms applied at every node. The update of a response to input from a parent uses the conditional probabilities of the link. A parent’s response to input from a child uses the related likelihood ratio (see also the supplement on Bayesian networks in Bringsjord &amp; Govindarajulu 2018 [2019]). Reading some variables as hypotheses and others as pieces of evidence, simple instances of core scientific concepts can easily be read off such a structure. Simple explanation amounts to showing how the value of a variable “downstream” depends on the pattern “upstream”. Simple confirmation amounts to an increase in the probability or degree of belief of a node h upstream given a piece of evidence e downstream. Evaluating competing hypotheses consists in calculating the comparative probability of different patterns upstream. One clear reading of networks is as causal graphs.