Using the terminology of Bayes' Theorem, a posterior probability can also be defined as:
A) a conditional probability
B) a joint probability
C) 1
D) 0

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A posterior probability is a conditional probability that is updated based on new information or data using Bayes' Theorem. It represents the probability of a particular hypothesis or event given the prior probability and new evidence or data. The formula for calculating a posterior probability is P(H|E) = P(E|H) P(H) / P(E), where P(H|E) is the posterior probability, P(E|H) is the likelihood, P(H) is the prior probability, and P(E) is the marginal likelihood.

Using the Terminology of Bayes' Theorem, a Posterior Probability Can