part I General Overview -- chapter 1 Generalized Linear Models: A Bayesian View -- chapter 2 Random Effects in Generalized Linear Mixed Models (GLMMs) -- chapter 3 Prior Elicitation and Variable Selection for Generalized Linear Mixed Models -- part II Extending the GLMs -- chapter 4 Dynamic Generalized Linear Models -- chapter 5 Bayesian Approaches for Overdispersion in Generalized Linear Models -- chapter 6 Bayesian Generalized Linear Models for Inference About Small Areas -- part III Categorical and Longitudinal Data -- chapter 7 Bayesian Methods for Correlated Binary Data -- chapter 8 Bayesian Analysis for Correlated Ordinal Data Models -- chapter 9 Bayesian Methods for Time Series Count Data -- chapter 10 Item Response Modeling -- chapter 11 Developing and Applying Medical Practice Guidelines Following Acute Myocardial Infarction: A Case Study Using Bayesian Probit and Logit Models -- part IV Semiparametric Approaches -- chapter 12 Semiparametric Generalized Linear Models: Bayesian Approaches -- chapter 13 Binary Response Regression with Normal Scale Mixture Links -- chapter 14 Binary Regression Using Data Adaptive Robust Link Functions -- chapter 15 A Mixture-Model Approach to the Analysis of Survival Data -- part V Model Diagnostics and Variable Selection in GLMs -- chapter 16 Bayesian Variable Selection Using the Gibbs Sampler -- chapter 17 Bayesian Methods for Variable Selection in the Cox Model -- chapter 18 Bayesian Model Diagnostics for Correlated Binary Data -- part VI Challenging Approaches in GLMs -- chapter 19 Bayesian Errors-in-Variables Modeling -- chapter 20 Bayesian Analysis of Compositional Data -- chapter 21 Classification Trees -- chapter 22 Modeling and Inference for Point-Referenced Binary Spatial Data -- chapter 23 Bayesian Graphical Models and Software for GLMs.

This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.