There is neither need nor space to go over all of that ground here, but our views are in line with the comments of Fienberg (2006), Kadane (2006),...

There is neither need nor space to go over all of that ground here, but our views are in line with the comments of Fienberg (2006), Kadane (2006), Lad (2006), and OHagan (2006). Over the years, much time and eort has been spent pointing out basic differences between frequentist and Bayesian methods and indicating why Bayesian methods are fundamentally more sound (they condition appropriately, they address the right questions, they provide fully probabilistic statements about both parameters and observables, etc.). Jim Berger has participated actively in this endeavor. Given this eort, why would we want to unify the Bayesian and frequentist approaches? Why should we be interested in the prior that most frequently yields exact frequentist inference, which just leads us to the same place as using frequentist methods, many of which have been found lacking from a Bayesian viewpoint? Why should we care about the frequentist performance of Bayesian methods? Is it not preferable to focus