ABSTRACT

This chapter reviews generalized linear models from a Bayesian perspective. We discuss prior distributions and hierarchical models. We demonstrate how to approximate the likelihood by a normal distribution, as an approximation and as a step in more general computations. Finally, we discuss the class of loglinear models, a subclass of Poisson generalized linear models that is commonly used for missing data imputation and discrete multivariate outcomes. This chapter is not intended to be exhaustive, but rather to provide enough guidance so that the reader can combine generalized linear models with the ideas of hierarchical models, posterior simulation, prediction, model checking, and sensitivity analysis that we have already presented for Bayesian methods in general, and linear models in particular.