Posterior mean

Common sense says that what you believe after seeing new data should be some sort of compromise between what you believed before and what the new data says. You don’t want to ignore previous information or new information. How much should new data change your prior beliefs? When prior judgment and new information are in conflict, which one should be given the benefit of the doubt? Bayesian data models provide a framework for making such decisions quantitative and objective. The choice of a data model is somewhat subjective—whether it’s a Bayesian model or not—but given a Bayesian model, the rules for updating the representation of your beliefs are objective. As some put it, you “turn the Bayesian crank.” A likelihood model and a prior on parameters together specify how new data changes the prior distribution into a posterior distribution. We will make this more concrete with three examples. Normal-normal model Suppose that data X has a normal distribution with unknown mean μ and known…