Modelling heterogeneity with and without the Dirichlet process
Modelling heterogeneity with and without the Dirichlet process
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DOI：
10.1111/1467-9469.00242
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发表时间：
2001-06-01
影响因子：
1
通讯作者：
Richardson, S
中科院分区：
文献类型：
--
作者：
Green, PJ;Richardson, S
我们调查狄利克雷过程（DP）为基础的模型和分配模型的可变数量的组件，可交换分布的基础上之间的关系。本文证明了DP分配分布是Dirichlet-多项分配模型的一个极限情形，并在贝叶斯范式下比较了DP分配模型和分配模型的后验性能，特别是在单变量混合模型的背景下，证明了在先验DP模型中存在的分配分布的不平衡性是后验持续的。介绍了一种用于基于DP的一般模型的新MCMC采样器，它在可逆跳跃框架中使用分裂/合并移动。这种新的采样器的性能相对于一些传统的采样器的DP过程青少年探索。
We investigate the relationships between Dirichlet process (DP) based models and allocation models for a variable number of components, based on exchangeable distributions. It is shown that the DP partition distribution is a Limiting case of a Dirichlet-multinomial allocation model, Comparisons of posterior performance of DP and allocation models are made in the Bayesian paradigm and illustrated in the context of univariate mixture models, It is shown in particular that the unbalancedness of the allocation distribution, present in the prior DP model, persists aposteriori, Exploiting the model connections, a new MCMC sampler for general DP based models is introduced, which uses split/merge moves in a reversible jump framework. Performance of this new sampler relative to that of some traditional samplers for DP processes is teen explored.