The Bayesian approach to modeling provides a natural framework for making inferences from incomplete data. Bayesian inference is characterized by specifying a model, then specifying prior distributions for the parameters of the models, and then lastly updating the prior information on the parameters using the model and the data to obtain the posterior distribution of the parameters. Analyses with missing data involve assumptions that cannot be verified; assumptions about the missing data (and our uncertainty about them) can be made explicit through prior distributions.