Estimating Normal Means with a Dirichlet Process Prior
本文用狄利克雷过程先验做非参数贝叶斯估计，开发吉布斯抽样算法解决计算难题，蒙特卡洛模拟显示该估计在性能上接近参数或非参数经验贝叶斯估计中较好的那个。
Abstract In this article, the Dirichlet process prior is used to provide a nonparametric Bayesian estimate of a vector of normal means. In the past there have been computational difficulties with this model. This article solves the computational difficulties by developing a "Gibbs sampler" algorithm. The estimator developed in this article is then compared to parametric empirical Bayes estimators (PEB) and nonparametric empirical Bayes estimators (NPEB) in a Monte Carlo study. The Monte Carlo study demonstrates that in some conditions the PEB is better than the NPEB and in other conditions the NPEB is better than the PEB. The Monte Carlo study also shows that the estimator developed in this article produces estimates that are about as good as the PEB when the PEB is better and produces estimates that are as good as the NPEB estimator when that method is better. Key Words: Empirical BayesGibbs samplerImportance samplingMixtures of Dirichlet processesNonparametric Bayes