Estimating mixture of Dirichlet process models
Estimating mixture of Dirichlet process models
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DOI：
10.2307/1390815
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发表时间：
1998-06-01
影响因子：
2.4
通讯作者：
Muller, P
中科院分区：
文献类型：
--
作者：
MacEachern, SN;Muller, P
目前的吉布斯抽样方案的混合Dirichlet过程（MDP）模型被限制到使用“共轭”的基础措施，允许解析评估的转移概率时，重新配置，或者需要依赖于近似的数值评估的一些转移概率。Gibbs抽样在更一般的MDP模型中的实现是一个开放的和重要的问题，因为大多数应用程序要求使用非共轭基措施，在这篇文章中，我们提出了一个概念框架的计算策略。这个框架提供了一个角度对当前的方法，促进它们之间的比较，并导致几个新的方法，扩大范围的MDP模型的非共轭的情况。我们详细讨论一个。基本策略是基于扩展参数向量，并适用于MDP模型的任意基测度和似然。对于一类重要的正态-正态MDP模型和具有固定或少量超参数的问题，本文也给出了相应的策略。
Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted to using "conjugate" base measures that allow analytic evaluation of the transition probabilities when resampling configurations, or alternatively need to rely on approximate numeric evaluations of some transition probabilities. Implementation of Gibbs sampling in more general MDP models is an open and important problem because most applications call for the use of nonconjugate base measures, In this article we propose a conceptual framework for computational strategies. This framework provides a perspective on current methods, facilitates comparisons between them, and leads to several new methods that expand the scope of MDP models to nonconjugate situations. We discuss one in detail. The basic strategy is based on expanding the parameter vector, and is applicable for MDP models with arbitrary base measure and likelihood. Strategies are also presented for the important class of normal-normal MDP models and for problems with fixed or few hyperparameters, The proposed algorithms are easily implemented and illustrated with an application.