  Inferences that arise from loss functions determined by the prior are
considered and it is shown that these lead to limiting Bayes rules that are
closely connected with likelihood. The procedures obtained via these loss
functions are invariant under reparameterizations and are Bayesian unbiased or
limits of Bayesian unbiased inferences. These inferences serve as
well-supported alternatives to MAP-based inferences.
