The posterior distribution is the "solution" to our Bayesian problem given by $\pi(\theta|x)$. It is our belief over our hypotheses or parameters given the data. While the posterior is sufficient in itself to describe our solution, there are options for [[summarizing the posterior]] to get a [[point estimate]] or [[interval estimate]]. Contrast with the [[posterior predictive distribution]], which is a distribution of possible values for the prediction.