The posterior distribution is a key concept in Bayesian statistics. It represents the updated probability of a parameter after observing new data. This distribution combines prior beliefs about the parameter, represented by the prior distribution, with the likelihood of the observed data, using Bayes' theorem. In practical terms, the posterior distribution provides a way to quantify uncertainty about a parameter. It allows statisticians to make inferences and predictions based on both prior knowledge and new evidence. This approach is widely used in various fields, including machine learning, economics, and medical research.