Econ Papers Daily / 期刊论文
通过近似后验分布传达科学不确定性
Communicating Scientific Uncertainty via Approximate Posteriors
摘要
We cast the problem of communicating scientific uncertainty as one of reporting a posterior distribution on an unknown parameter to an audience of Bayesian decision‐makers. We establish novel bounds on the audience's regret when the analyst reports an approximation to a posterior that the audience treats as exact. Under a palatable restriction on the audience's decision problems, the bounds take an especially convenient form. Under a further restriction on the audience's priors, a bootstrap distribution can be used as a stand‐in posterior. We propose a practical recipe for checking whether a conventional statistical report (say, a normal parameterized by a point estimate and standard error) is a good approximation, and for improving the report if it is not. We illustrate our proposals using the articles in the 2021 American Economic Review that use a bootstrap for inference.
中文摘要
我们将科学不确定性沟通问题视为向贝叶斯决策者受众报告未知参数后验分布的过程。我们为当分析师报告一个受众视为精确的后验近似值时，受众的遗憾建立了新的界限。在对受众决策问题施加一个可接受的限制条件下，这些界限呈现出特别便捷的形式。进一步限制受众的先验分布时，自助法分布可作为后验的替代。我们提出了一种实用方法，用于检查常规统计报告（例如，由点估计和标准误参数化的正态分布）是否构成良好近似，并在非近似时改进报告。我们利用2021年《美国经济评论》中使用自助法进行推断的文章来阐述我们的建议。