  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.
