I. General overview -- 1. Generalized linear models: A Bayesian view -- 2. Random effects in generalized linear mixed models (GLMMs) -- 3. Prior elicitation and variable selection for generalized linear mixed models -- II. Extending the GLMs -- 4. Dynamic generalized linear models -- 5. Bayesian approaches for overdispersion in generalized linear models -- 6. Bayesian generalized linear models for inference about small areas -- III. Categorical and longitudinal data -- 7. Bayesian methods for correlated binary data -- 8. Bayesian analysis for correlated ordinal data models -- 9. Bayesian methods for time series count data -- 10. Item response modeling -- 11. Developing and applying medical practice guidelines following acute myocardial infarction: A case study using Bayesian probit and logit models -- IV. Semiparametric approaches -- 12. Semiparametric generalized linear models: Bayesian approaches -- 13. Binary response regression with normal scale mixture links -- 14. Binary regression using data adaptive robust link functions -- 15. A mixture-model approach to the analysis of survival data -- V. Model diagnostics and variable selection in GLMs -- 16. Bayesian variable selection using the Gibbs sampler -- 17. Bayesian methods for variable selection in the Cox model -- 18. Bayesian model diagnostics for correlated binary data -- VI. Challenging approaches in GLMs -- 19. Bayesian errors-in-variables modeling -- 20. Bayesian analysis of compositional data -- 21. Classification trees -- 22. Modeling and inference for point-referenced binary spatial data -- 23. Bayesian graphical models and software for GLMs.