In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate. The same simulations required for the point estimate also produce the standard deviation. Exponential family models make the calculations particularly simple, and bring in a connection to the parametric bootstrap.

中文翻译：

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贝叶斯估计的频率论准确性。


在缺乏相关先验经验的情况下，流行的贝叶斯估计技术通常从某种形式的“无信息”先验分布开始，旨在具有最小的推理影响。贝叶斯规则仍然会产生漂亮的估计和可信的区间，但这些缺乏基于经验的先验的逻辑力量，需要进一步的论证。本文涉及贝叶斯估计的频率论评估。一个简单的公式给出了贝叶斯点估计的频率标准差。点估计所需的相同模拟也会产生标准偏差。指数族模型使计算特别简单，并引入了与参数引导程序的连接。