  Objective prior distributions represent an important tool that allows one to
have the advantages of using the Bayesian framework even when information about
the parameters of a model is not available. The usual objective approaches work
off the chosen statistical model and in the majority of cases the resulting
prior is improper, which can pose limitations to a practical implementation,
even when the complexity of the model is moderate. In this paper we propose to
take a novel look at the construction of objective prior distributions, where
the connection with a chosen sampling distribution model is removed. We explore
the notion of defining objective prior distributions which allow one to have
some degree of flexibility, in particular in exhibiting some desirable
features, such as being proper, or centered on specific values which would be
of interest in nested model comparisons. The basic tool we use are proper
scoring rules and the main result is a class of objective prior distributions
that can be employed in scenarios where the usual model based priors fail, such
as mixture models and model selection via Bayes factors. In addition, we show
that the proposed class of priors is the result of minimising the information
it contains, providing solid interpretation to the method.
