公共政策和政府环境中的贝叶斯模型和方法
Bayesian Models and Methods in Public Policy and Government Settings
摘要：
从20世纪50年代的新贝叶斯复兴开始，许多统计学家认为在政府和公共政策环境中使用贝叶斯方法是不合适的，特别是主观贝叶斯方法，因为它们依赖于先验分布。但是贝叶斯框架通常提供了回答这些设置中提出的问题的主要方法，并且贝叶斯应用的数量和多样性近年来急剧增长。通过一系列的例子，历史和最近的，我们认为，贝叶斯方法与正式和非正式的评估先验和似然函数被广泛接受，应该成为公共场合的规范。我们的例子包括人口普查和小面积估计，美国大选之夜预测，向美国食品和药物管理局报告的研究，评估全球气候变化，以及测量老年人残疾的潜在下降。
英文摘要：
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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