SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
and Yang, Shu and Guan, Yawen and Giffin, Andrew B
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Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
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Modeling Spatial Heterogeneity in Exposure Buffers and Risk: A Hierarchical Bayesian Approach
SVBR is a new hierarchical Bayesian method that treats buffer radii as unknown spatially varying parameters, improves parameter recovery in simulations, and reveals spatial heterogeneity in healthcare access effects on antenatal care in Madagascar.
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Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.