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stat.ML2019
Spatial Analysis Made Easy with Linear Regression and Kernels
Philip Milton, Emanuele Giorgi, Samir Bhatt
Kernel methods are an incredibly popular technique for extending linear models to non-linear problems via a mapping to an implicit, high-dimensional feature space. While kernel met…
stat.ME2019
A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data
Olatunji Johnson, Peter Diggle, Emanuele Giorgi
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.…