Discussion
Why does the ordering of variables affect the network's size?
Answer: B
Listing causes before effects usually lets each node depend on few predecessors, whereas the reverse ordering forces dense connections that encode the same distribution far less compactly. Every ordering is exact, and observation is unrelated. That the same distribution may have a sparse or a dense representation, according to how it is written, is why structure learning and expert elicitation both begin by seeking the causal direction. Most errors here come from carrying a result across from a neighbouring case where it does not hold.
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