  Starting with the neo-Bayesian revival of the 1950s, many statisticians
argued that it was inappropriate to use Bayesian methods, and in particular
subjective Bayesian methods in governmental and public policy settings because
of their reliance upon prior distributions. But the Bayesian framework often
provides the primary way to respond to questions raised in these settings and
the numbers and diversity of Bayesian applications have grown dramatically in
recent years. Through a series of examples, both historical and recent, we
argue that Bayesian approaches with formal and informal assessments of priors
AND likelihood functions are well accepted and should become the norm in public
settings. Our examples include census-taking and small area estimation, US
election night forecasting, studies reported to the US Food and Drug
Administration, assessing global climate change, and measuring potential
declines in disability among the elderly.
