Bayesian inference involves three steps that go beyond classical estimation. 1\. data and model are combined to form a posterior distribution, which we typically summarize by a set of simulations of the parameters in the model. 2\. can propagate uncertainty in this distribution—that is, we can get simulation-based predictions for unobserved or future outcomes that accounts for uncertainty in the model parameters. 3\. can include additional information into the model using a prior distribution.