On a prior based on the Wasserstein information matrix
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Publication:6390374
DOI10.1016/J.SPL.2022.109645arXiv2202.03217WikidataQ114130438 ScholiaQ114130438MaRDI QIDQ6390374
Publication date: 7 February 2022
Abstract: We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties.
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