Slice sampling mixture models
Slice sampling mixture models
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DOI：
10.1007/s11222-009-9150-y
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发表时间：
2011-01-01
影响因子：
2.2
通讯作者：
Walker, Stephen G.
中科院分区：
文献类型：
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作者：
Kalli, Maria;Griffin, Jim E.;Walker, Stephen G.
我们提出了一种更有效的切片采样器版本，用于由Walker （common）描述的狄利克雷过程混合模型。同时统计。计算机。36:45-54,2007)。这种新的采样器允许无限混合模型的拟合与广泛的事先规格。为了说明这种灵活性，我们考虑由独立的正随机变量的无穷序列定义的先验。考虑了两种应用：使用混合模型的密度估计和危险函数估计。在每种情况下，我们都展示了如何应用切片高效采样器在模型中进行推理。在混合情况下，对两个子模型进行了详细的研究。第一个假设正随机变量是伽马分布第二个假设它们是反高斯分布。两个先验都有两个超参数，我们考虑它们对样本中占用簇数量的先验分布的影响。广泛的计算比较与替代“条件”模拟技术的混合模型使用标准狄利克雷过程先验和我们的新先验。在一个密度估计问题上说明了新先验的性质。
We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (Commun. Stat., Simul. Comput. 36:45-54, 2007). This new sampler allows for the fitting of infinite mixture models with a wide-range of prior specifications. To illustrate this flexibility we consider priors defined through infinite sequences of independent positive random variables. Two applications are considered: density estimation using mixture models and hazard function estimation. In each case we show how the slice efficient sampler can be applied to make inference in the models. In the mixture case, two submodels are studied in detail. The first one assumes that the positive random variables are Gamma distributed and the second assumes that they are inverse-Gaussian distributed. Both priors have two hyperparameters and we consider their effect on the prior distribution of the number of occupied clusters in a sample. Extensive computational comparisons with alternative "conditional" simulation techniques for mixture models using the standard Dirichlet process prior and our new priors are made. The properties of the new priors are illustrated on a density estimation problem.