A spatially discrete approximation to log‐Gaussian Cox processes for modelling aggregated disease count data
A spatially discrete approximation to log‐Gaussian Cox processes for modelling aggregated disease count data
In this paper, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely that each such model is tied to a specific partition of …