Word of the Week

Week #3 - Prior and posterior

Bayesian statistics typically incorporates new information (e.g. from a diagnostic test, or a recently drawn sample) to answer a question of the form "What is the probability that..."

The answer to this question is referred to as the "posterior" probability, arrived at by modifying a "prior" probability in light of the new information.  For example, suppose the general incidence of a disease is 1% (the prior probability).  Now suppose  a subject tests positive on a diagnostic test, and you want to know the "posterior" probability of having the disease.  Information from the prior probability and the test results is combined to arrive at that posterior probability.

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