A general framework for the parametrization of hierarchical models
A general framework for the parametrization of hierarchical models
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DOI：
10.1214/088342307000000014
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发表时间：
2007-02-01
影响因子：
5.7
通讯作者：
Skold, Martin
中科院分区：
文献类型：
--
作者：
Papaspiliopoulos, Omiros;Roberts, Gareth O.;Skold, Martin
在本文中，我们将定心和非定心方法描述为用于广泛类别分层模型参数化的互补技术，以期构建有效的MCMC算法来探索这些模型的后验分布。我们对定心和非定心何时工作良好给出了清晰的定性理解，并介绍了有关Gibbs采样器使用中心参数化和非中心参数化的收敛时间复杂度的理论。我们给出了构造非中心参数化的一般方法，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采样器算法中的应用，该算法的收敛性质对数据不过于敏感。
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.