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Scalable Bayesian Spatiotemporal Tensor Modeling for Censored and Missing Areal Data
We propose a new Bayesian approach for spatiotemporal areal data with censored and missing observations, where the response is represented as a tensor indexed by spatial units and time points. The method introduces a flexible random effect that combines the spatial dependence structures of the Simul
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