Discussion of 'Sampling schemes for generalized DP random effects models'

Peter Miiller

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Discussion of 'Sampling schemes for generalized DP random effects models'

机译：关于“广义DP随机效应模型的采样方案”的讨论

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I congratulate the authors on a stimulating and interesting paper. They take a refreshingly different perspective of DP mixture models and see different aspects from what is traditionally discussed in the non-parametric Bayesian literature. I would like to highlight some features of the discussed models and implementations that are stated in the paper, but might not be noticed by a casual reader who is not familiar with these models. First, the paper introduces the GLMDM as a special case of the popular DP mixture models, but then proceeds discussing it (correctly, of course) as a semi-parametric extension of GLMMs. The posterior MCMC for the random partition A, the precision m and other features related to the DP model do not exploit the nature of the mixing kernel as a GLM.

机译：我祝贺作者发表了一篇有趣而有趣的论文。他们对DP混合模型采用了令人耳目一新的不同观点，并且从非参数贝叶斯文献中传统讨论的内容中看到了不同的方面。我想强调本文中讨论的模型和实现的某些功能，但那些不熟悉这些模型的随便的读者可能不会注意到。首先，本文介绍了GLMDM作为流行的DP混合模型的特例，但随后（当然正确）将其作为GLMM的半参数扩展进行了讨论。随机分区A的后MCMC，精度m和与DP模型相关的其他特征未利用混合内核作为GLM的性质。

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《Statistical Methods and Applications》 |2011年第3期|p.299-301|共3页
作者
Peter Miiller;
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Department of Mathematics, University of Texas, Austin, TX, USA;

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Discussion of 'Sampling schemes for generalized DP random effects models'

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