An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
and Datta, Abhirup and Finley, Andrew O
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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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Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
An autoregressive Gaussian process transport-map construction factors spatio-temporal joint densities into conditional distributions with data-dependent sparsity to enable scalable generative modeling of non-Gaussian fields.
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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.