Bayesian finite mixtures with an unknown number of components: The allocation sampler
Bayesian finite mixtures with an unknown number of components: The allocation sampler
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DOI：
10.1007/s11222-006-9014-7
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发表时间：
2007-06-01
影响因子：
2.2
通讯作者：
Fearnside, Alastair T.
中科院分区：
文献类型：
--
作者：
Nobile, Agostino;Fearnside, Alastair T.
本文提出了一种新的马尔可夫链蒙特卡罗方法，用于未知分量数的有限混合分布的贝叶斯分析。采样器的特点是由一个状态空间只包括组件的数量和潜在的分配变量。它的主要优点是，它可以使用，以最小的变化，从任何参数族的组件的混合物，假设组件参数可以被集成的模型分析。人工和真实的数据集被用来说明的方法和混合物的单变量和多变量的法线被明确考虑。标签切换的问题，当参数推断是感兴趣的，在后处理阶段解决。
A new Markov chain Monte Carlo method for the Bayesian analysis of finite mixture distributions with an unknown number of components is presented. The sampler is characterized by a state space consisting only of the number of components and the latent allocation variables. Its main advantage is that it can be used, with minimal changes, for mixtures of components from any parametric family, under the assumption that the component parameters can be integrated out of the model analytically. Artificial and real data sets are used to illustrate the method and mixtures of univariate and of multivariate normals are explicitly considered. The problem of label switching, when parameter inference is of interest, is addressed in a post-processing stage.