Discussion
Which failure follows from double-counting correlated evidence?
Answer: D
Treating correlated observations as independent multiplies their ratios as though each carried fresh information, so the posterior moves much further than it should. The ranking usually survives, normalisation is unaffected, and there is no drift back to the prior. This is the practical hazard behind naive Bayes' poor calibration, and it is also why a network with a missing edge is worse than one with a spurious edge in most applications.
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