Bayesian Density Estimation and Inference Using Mixtures
本文描述了使用狄利克雷过程混合模型进行密度估计的贝叶斯推断方法，通过高效模拟近似先验、后验和预测分布，解决局部与全局平滑、模态数量等实际问题，并建立了正态混合模型的收敛性结果。
Abstract We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates, assessment of modality, and the inference on the numbers of components. Also, convergence results are established for a general class of normal mixture models.