Minimally Informative Prior Distributions for Non-Parametric Bayesian Analysis
非参数贝叶斯分析中的最小信息先验分布
Summary We address the problem of how to conduct a minimally informative, non-parametric Bayesian analysis. The central question is how to devise a model so that the posterior distribution satisfies a few basic properties. The concept of ‘local mass’ provides the key to the development of the limiting Dirichlet process model. This model is then used to provide an engine for inference in the compound decision problem and for multiple-comparisons inference in a one-way…