Step 1: Recall what Bayes theorem states. For events \(A\) and \(B\) with \(P(B) > 0\), \[P(A|B) = \frac{P(B|A)P(A)}{P(B)}\] This is a formula that converts a conditional probability given one direction (\(B\) given \(A\)) into the conditional probability in the reverse direction (\(A\) given \(B\)). So statement (i) is true, since Bayes theorem is precisely a rule for computing a conditional probability from other known conditional and marginal probabilities.
Step 2: Check statement (ii). The posterior probability \(P(A|B)\) is an updated probability of \(A\) once the evidence \(B\) has been observed. It does not "supersede" the prior \(P(A)\) in any absolute sense; the prior remains valid before the evidence is known, and the posterior is only the revised belief conditional on that evidence. Calling the posterior a replacement or override of the prior is not correct usage, so statement (ii) is false.
Step 3: Check statement (iii). Bayes theorem is commonly used to reason backward: given that some later event (evidence) has occurred, we compute the probability of an earlier event (a cause) that could have produced it. For example, given a positive test result (a subsequent event), Bayes theorem gives the probability that the disease (an earlier condition) is actually present. So statement (iii) is true.
Step 4: Combining the results, statements (i) and (iii) are true while (ii) is false. This matches option (C).
The correct answer is Only (i) and (iii) are true.