ABSTRACT
In this chapter, we shall introduce Bayesian models with a non-informative prior for some of the generalized linear mixed-effects models described in previous chapters and show the similarity of the estimates. Bayesian alternatives are presented here in preparation for the situation where frequentist approaches including the generalized linear mixed models face difficulty in estimation. Bayesian models are especially useful for complex models such as generalized linear mixed-effects models with hierarchical random effects, latent class (profile) models, and handling measurement error and missing data.
