Posteriorization
Posteriorization is a term used in Bayesian statistics to describe the process of updating prior beliefs about parameters in light of new evidence or data. This update results in a posterior distribution, which represents the revised beliefs after considering the observed data. The core of posteriorization lies in Bayes' theorem, a fundamental equation that formalizes this update. Bayes' theorem states that the posterior probability of a hypothesis is proportional to the likelihood of observing the data given the hypothesis, multiplied by the prior probability of the hypothesis.
In practice, posteriorization involves combining a prior distribution, which quantifies initial uncertainty about parameters before any