Predictive Updating Methods with Application to Bayesian Classification
提出基于未知量预测分布的随机抽样算法，包括迭代和顺序两种变体，可轻松更新后验分布，并展示了在贝叶斯分类、层次模型和变量选择中的应用。
SUMMARY We propose algorithms based on random draws from predictive distributions of unknown quantities (missing values, for instance). This procedure can either be iterative, which is a special variation of the Gibbs sampler, or be sequential, which is a variation of sequential imputation. In the latter case one can update the posterior distribution with new observations easily. The methods proposed have intuitive statistical implications and can be generalized to accommodate other Bayesian-like procedures. We display some applications of the method in connection with the Bayesian bootstrap, classification, hierarchical models and selection of variables. In particular, as an application of the method, we present a unified treatment of switching regression models driven by a general binary process, and we develop a Bayesian testing procedure. Some simulations and a real example are used to illustrate the methods proposed.