Modeling with Normalized Random Measure Mixture Models
Modeling with Normalized Random Measure Mixture Models
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DOI：
10.1214/13-sts416
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发表时间：
2013-08-01
影响因子：
5.7
通讯作者：
Prunster, Igor
中科院分区：
文献类型：
--
作者：
Barrios, Ernesto;Lijoi, Antonio;Prunster, Igor
Dirichlet过程混合模型和基于离散随机概率测度的更一般的混合模型已被证明是密度估计和聚类的灵活和准确的模型。本文的目的是说明在非参数分层混合模型中使用归一化随机度量作为混合度量，并指出如何成功地解决可能的计算问题。为此，我们首先提供了具有独立增量的规范化随机测度的简明易懂的介绍。然后，我们详细解释了使用弗格森类表示从后验抽样的一种特殊方法。我们对位置尺度混合物进行了全面的比较分析，考虑了混合核和非参数成分的一组替代方案。仿真结果表明，归一化随机测量混合可能是密度估计问题的有效默认选择。作为这项研究的副产品，一个适合这些模型的R包被制作出来，并在综合R档案网络（CRAN）中可用。
The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in nonparametric hierarchical mixture models and point out how possible computational issues can be successfully addressed. To this end, we first provide a concise and accessible introduction to normalized random measures with independent increments. Then, we explain in detail a particular way of sampling from the posterior using the Ferguson Klass representation. We develop a thorough comparative analysis for location-scale mixtures that considers a set of alternatives for the mixture kernel and for the nonparametric component. Simulation results indicate that normalized random measure mixtures potentially represent a valid default choice for density estimation problems. As a byproduct of this study an R package to fit these models was produced and is available in the Comprehensive R Archive Network (CRAN).