Revised probabilities of events based on additional information are posterior probabilities. The correct option is A.
Given the data, a posterior probability is a likelihood of assigning observations to groups. A prior probability is a likelihood that an observation will belong to a group before the data is collected.
In Bayesian statistics, a posterior probability is the revised or updated probability of an event occurring after new information has been considered. Using Bayes' theorem, the posterior probability is computed by updating the prior probability.
Thus, the ideal selection is option A.
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