Bayes' Theorem
P(H|E) = P(E|H)·P(H)/P(E)
P(E) = P(E|H)P(H)+P(E|¬H)P(¬H)
LR = P(E|H)/P(E|¬H)
Posterior odds = LR × Prior odds
Sequential evidence updates: each observation multiplies the prior odds by the likelihood ratio. The network propagates beliefs through conditional dependencies.