Fast Bayesian Inference for Spatial Mean-Parameterized Conway-Maxwell-Poisson Models.

ABSTRACT: Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features-the mode-parameterized Conway-Maxwell-Poisson (COMP) distribution and the generalized Poisson model. However both require the use of either constraints on the parameter space or a parameterization that leads to challenges in interpretability. We propose spatial mean-parameterized COMP models that retain the flexibility of these models while resolving the above issues. We use a Bayesian spatial filtering approach in order to efficiently handle high-dimensional spatial data and we use reversible-j

SUBMITTER: Kang B 

PROVIDER: S-EPMC12233211 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature