Pitfalls in the implementation of Bayesian hierarchical modeling of areal count data. An illustration using BYM and Leroux models

Abstract

Several different hierarchical Bayesian models can be used for the estimation of spatial risk patterns based on spatially aggregated count data. Typically, the resulting posterior distributions of the model parameters cannot be expressed in closed forms, and MCMC approaches are required for inference. However, implementations of hierarchical Bayesian models for such areal data are error-prone. Also, different implementation methods exist, and a surprisingly large variability may develop between the methods as well as between the diffe Show more