Non-Parametric Bayesian Models for Samples from Finite Populations
在有限总体贝叶斯分析中使用非参数先验，证明简单随机抽样和分层随机抽样中总体均值的常用估计和置信区间在渐近意义上有贝叶斯合理性，并开发了分层抽样中百分位数估计和区间估计的新方法。
Summary Using Ferguson's (1973) non-parametric priors in a Bayesian analysis of finite populations, we show that asymptotically, at least, the usual estimates and confidence intervals for the population mean in simple and stratified random samples can be justified in Bayesian terms. We then apply these models for estimating population percentiles, and a new procedure for interval estimates in stratified sampling is developed.