The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter values), given prior knowledge and a mathematical model describing the observations available at a particular time. After the arrival of new information, the current posterior probability may serve as the prior in another round of Bayesian updating.

In the context of Bayesian statistics, the posterior probability distribution usually describes the epistemic uncertainty about statistical parameters conditional on a collection of observed data. From a given posterior distribution, various point and interval estimates can be derived, such as the maximum a posteriori (MAP) or the highest posterior density interval (HPDI). But while conceptually simple, the posterior distribution is generally not tractable and therefore needs to be either analytically or numerically approximated.

In Bayesian statistics, the posterior probability is the probability of the parameters ${\displaystyle \theta }$ given the evidence ${\displaystyle X}$, and is denoted ${\displaystyle p(\theta |X)}$.

It contrasts with the likelihood function, which is the probability of the evidence given the parameters: ${\displaystyle p(X|\theta )}$.

The two are related as follows:

Given a prior belief that a probability distribution function is ${\displaystyle p(\theta )}$ and that the observations ${\displaystyle x}$ have a likelihood ${\displaystyle p(x|\theta )}$, then the posterior probability is defined as

where ${\displaystyle p(x)}$ is the normalizing constant and is calculated as

for continuous ${\displaystyle \theta }$, or by summing ${\displaystyle p(x|\theta )p(\theta )}$ over all possible values of ${\displaystyle \theta }$ for discrete ${\displaystyle \theta }$.