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摘要翻译：
在本文中，我们将定心和非定心方法作为补充技术，用于广泛的层次模型的参数化，以期构造有效的MCMC算法来探索这些模型的后验分布。我们对定心和非定心工作时给出了清晰的定性认识，并介绍了利用定心和非定心参数的Gibbs采样器收敛时间复杂度的理论。我们给出了构造非中心参数的一般方法，包括一种称为状态空间展开技术的辅助变量技术。我们还描述了部分非中心方法，并演示了它们在构造鲁棒Gibbs采样算法中的应用，这些算法的收敛性对数据不太敏感。
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英文标题：
《A General Framework for the Parametrization of Hierarchical Models》
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作者：
Omiros Papaspiliopoulos, Gareth O. Roberts, Martin Sk\"old
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models. We give a clear qualitative understanding as to when centering and noncentering work well, and introduce theory concerning the convergence time complexity of Gibbs samplers using centered and noncentered parametrizations. We give general recipes for the construction of noncentered parametrizations, including an auxiliary variable technique called the state-space expansion technique. We also describe partially noncentered methods, and demonstrate their use in constructing robust Gibbs sampler algorithms whose convergence properties are not overly sensitive to the data.
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PDF链接：
https://arxiv.org/pdf/708.3797
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