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We propose a geometric framework to assess global sensitivity in Bayesian nonparametric models for density estimation. We study sensitivity of nonparametric Bayesian models for density estimation, based on Dirichlet-type priors, to perturbations of either the precision parameter or the base probability measure. To quantify the different effects of the perturbations of the parameters and hyperparameters in these models on the posterior, we define three geometrically-motivated global sensitivitydoi:10.1007/s13171-018-0145-7 pmid:34054249 pmcid:PMC8157310 arXiv:1810.03671v1 fatcat:ccbq243tc5fu7o4xu6fg27peue