In Bayesian statistics, the **posterior probability** is the conditional probability of an event, sometimes called a *parameter,* given evidence or an observation of that event, the *data*.

Consider an event *A *and evidence of that event, another event itself, *B*. The posterior probability is *P(A|B)*. It is called posterior, because event *A* actually occurs before event *B*.

In contrast, the **likelihood function** is the conditional probability of the evidence or observation, *B*, given the event *A*, *P(B|A)*.

The posterior probability and the likelihood function are related through **Bayes’ Theorem**:

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