Bayesian inference for heterogeneous event counts

This article presents an integrated set of Bayesian tools one can use to model heterogeneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women's rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explanations, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost.