A method for combining inference across related nonparametric Bayesian models
A method for combining inference across related nonparametric Bayesian models
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DOI：
10.1111/j.1467-9868.2004.05564.x
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发表时间：
2004-01-01
影响因子：
5.8
通讯作者：
Rosner, G
中科院分区：
文献类型：
--
作者：
Müller, P;Quintana, F;Rosner, G
我们考虑相关的非参数贝叶斯模型中的组合推理问题。类似于参数层次模型，层次扩展形式化了相关子模型之间的借用强度。在非参数的情况下，建模是复杂的事实，我们定义的层次的随机量是无限维的。我们讨论了这样一个层次模型的正式定义。该方法包括在非参数模型水平上的回归。对于Dirichlet过程混合物的特殊情况下，我们开发了一个马尔可夫链蒙特卡罗计划，允许在给定的模型中有效地实现全后验推理。
We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparametric model. For the special case of Dirichlet process mixtures, we develop a Markov chain Monte Carlo scheme to allow efficient implementation of full posterior inference in the given model.