# Bayes Theorem

‘In probability theory and applications, **Bayes' theorem** shows the relation between a **conditional probability** and its reverse form. For example, the probability of a hypothesis given some observed pieces of evidence and the probability of that evidence given the hypothesis.

The equation used is:

Where:

- P(
*A*) is the prior probability or marginal probability of*A*. It is "prior" in the sense that it does not take into account any information about*B*. - P(
*A*|*B*) is the conditional probability of*A*, given*B*. It is also called the posterior probability because it is derived from or depends upon the specified value of*B*. - P(
*B*|*A*) is the conditional probability of*B*given*A*. It is also called the likelihood. - P(
*B*) is the prior or marginal probability of*B*, and acts as a normalizing constant ».

Courtesy of Wikipedia