摘要：MAP estimators and HPD credible sets are often criticized in the
literature because of paradoxical behaviour due to a lack of invariance under
reparametrization. In this paper, we propose a new version of MAP estimators
and HPD credible sets that avoid this undesirable feature. Moreover, in the special
case of non-informative prior, the new MAP estimators coincide with the invari-
ant frequentist ML estimators. We also propose several adaptations in the case of
nuisance parameters.
