RSTr: Gibbs Samplers for Discrete Bayesian Spatiotemporal Models
Abstract
Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" .
Fields of Study & Subjects
Spatial and Panel Data Analysis
Point processes and geometric inequalities
Markov Chains and Monte Carlo Methods
Publication details
- Publication date
- 17 January 2026
- Document type
- Dataset