Pitman-Yor混合物的重要性条件抽样
Importance conditional sampling for Pitman-Yor mixtures
摘要：
基于Pitman-Yor过程的非参数混合模型为密度估计和聚类提供了一种灵活的工具。Dirichlet过程混合模型的流行类的自然推广，它们允许对表征数据分布的分量的数量进行更鲁棒的推断。我们提出了一种新的采样策略，这种模型，命名为重要性条件采样（ICS），它结合了现有的方法，包括易于解释性和迭代内并行结构的吸引人的属性。一个广泛的仿真研究突出了所提出的方法的效率，不同于其他条件采样器，表现出稳定的性能为不同规格的参数特征的Pitman-Yor过程。我们进一步表明，ICS方法可以自然地扩展到其他类别的计算要求高的模型，如部分可交换数据的非参数混合模型。
英文摘要：
Nonparametric mixture models based on the Pitman-Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the number of components characterizing the distribution of the data. We propose a new sampling strategy for such models, named importance conditional sampling (ICS), which combines appealing properties of existing methods, including easy interpretability and a within-iteration parallelizable structure. An extensive simulation study highlights the efficiency of the proposed method which, unlike other conditional samplers, shows stable performances for different specifications of the parameters characterizing the Pitman-Yor process. We further show that the ICS approach can be naturally extended to other classes of computationally demanding models, such as nonparametric mixture models for partially exchangeable data.
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