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May 2011 Bayesian Models and Methods in Public Policy and Government Settings
Stephen E. Fienberg
Statist. Sci. 26(2): 212-226 (May 2011). DOI: 10.1214/10-STS331


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.


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Stephen E. Fienberg. "Bayesian Models and Methods in Public Policy and Government Settings." Statist. Sci. 26 (2) 212 - 226, May 2011.


Published: May 2011
First available in Project Euclid: 1 August 2011

zbMATH: 1246.62037
MathSciNet: MR2858384
Digital Object Identifier: 10.1214/10-STS331

Keywords: Bayesian clinical drug studies , Census adjustment , Confidentiality , disability measurement , election night forecasting , Global warming , small area estimation

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.26 • No. 2 • May 2011
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