Discussion
Assertion (A): Bayesian networks make inference over many variables feasible.
Reason (R): Conditional independence lets a joint distribution factorise into small local tables.
Select the correct answer from the options below.
Answer: B
Both are true and the second is the reason. A joint over twenty binary variables needs a million numbers, while a sparse network needs a few dozen, and the reduction comes entirely from the conditional independences the structure asserts. Inference remains hard in the worst case, so representation and computation must be considered separately. That a structural assumption converts an impossible representation into a routine one is the central result of the topic.
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