In Bayesian statistics, the term 'posterior' refers to the probability distribution of a parameter conditioned on the observed data.
In Bayesian statistics, 'posterior' refers to the probability distribution $p(\theta|d)$ of a parameter $\theta$ conditioned on data $d$. It is the result of the knowledge-updating procedure of Bayes' theorem:
$$p(\theta|d) = \frac{p(d|\theta) p(\theta)}{p(d)},$$ where $p(d|\theta)$ is the likelihood function, $p(\theta)$ is the prior, and $p(d)$ is the normalization constant, also called the marginal likelihood.
