I'm reviewing
Bayes' theorem and related topics for the upcoming
GDAT class. In its simplest form, Bayes' theorem is a statement about conditional probabilities. The probability of A, given that B has occurred, is expressed as:
\begin{equation}
\Pr(A|B) = \dfrac{\Pr(B|A)\times\Pr(A)}{\Pr(B)} \label{eqn:bayes}
\end{equation}
In Bayesian language, $\Pr(A|B)$ is called the
posterior probability, $\Pr(A)$ the
prior probability, and $\Pr(B|A)$ the likelihood (essentially a normalization factor).
Source: Wikipedia