D Question 3 0.5 pts Both the Rule of Conditional Probabilities and Bayes' Theorem allow us to solve for the conditional probability P(a y). However, the Rule of Conditional Probabilities only requires knowledge of two probabilities for this solution, P(x, y) and P(y). In contrast, Bayes' Theorem requires knowledge of four probabilities, P(yla), P(x), P(yla), and P(x). Why would we use Bayes' Theorem when the Rule of Conditional Probabilities is easier to use? O In many circumstances, the two probabilities we require for the Rule of Conditional Probabilities are not directly available, but we can put them together based on prior knowledge and likelihoods of evidence using Bayes' Theorem. O Variables X and Y are sometimes mutually exclusive, so Bayes' Theorem is more appropriate in these cases than the Rule of Conditional Probabilities. O Variables X and Y are sometimes independent, so Bayes' Theorem is more appropriate in these cases than the Rule of Conditional Probabilities. O There is no reason to use Bayes' Theorem instead of the Rule of Conditional Probabilities; researchers who use Bayes' Theorem are just showing off