In Bayesian statistics, the **likelihood function** is the conditional probability of an event representing evidence or an observation, ie., *data*, given a second event, sometimes called a *parameter*.

Consider an event *A *and evidence of that event, another event itself, *B*. The likelihood function is the conditional probability *P(B|A)*.

In contrast, the **posterior probability** is the conditional probability of the event *A* given the evidence of the event, *B*, *P(A|B)*.

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