BAYESIAN DENSITY-ESTIMATION AND INFERENCE USING MIXTURES
BAYESIAN DENSITY-ESTIMATION AND INFERENCE USING MIXTURES
复制标题
DOI：
10.2307/2291069
复制
发表时间：
1995-06-01
影响因子：
3.7
通讯作者：
WEST, M
中科院分区：
文献类型：
--
作者：
ESCOBAR, MD;WEST, M
我们描述和说明贝叶斯推断模型的密度估计使用混合Dirichlet过程。这些模型为密度估计提供了自然的设置，并通过特殊情况来举例说明，其中数据被建模为正态分布混合物的样本。有效的模拟方法用于近似各种先验，后验和预测分布。这允许直接推断各种实际问题，包括局部与全局平滑的问题，密度估计的不确定性，评估的方式，和推断的组件的数量。此外，收敛性结果建立了一般类的正常混合模型。
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 eases 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.