A is correct because conditional probability measures A given B. B is the definition formula P(A|B) = P(A ∩ B)/P(B) (when P(B) > 0). C is true because Bayes’ theorem is used to update probabilities of hypotheses/causes when evidence is known. D is true for independent events since B does not change the probability of A. E is false because P(A|B) and P(not A|B) are complementary conditional probabilities and must sum to 1, not exceed 1. Therefore, A, B, C and D only are correct.
Option A:
Option A is incomplete because it omits D, which is a key property of independent events (conditioning does not change probability).
Option B:
Option B is correct because it includes all true statements (A, B, C, D) and excludes E, which wrongly claims the sum of complementary conditional probabilities is greater than 1.
Option C:
Option C is incorrect because it includes E (false) and also omits A and D, both of which are true statements about conditional probability and independence.
Option D:
Option D is incorrect because it includes E (false) and excludes B and C, which are fundamental definitions/uses of conditional probability and Bayes’ theorem.
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