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Spatial compositional data with exact zeros arise in many applicationsbut remain challenging for models that often assume strictly positivecomponents. We develop a Bayesian spatial model that combines theDirichlet Composition Distribution, which accommodates exact zeros throughcomponent-specific zero probabilities, with a Gaussian Markov random field representationof the latent compositional field
