Markov chain sampling methods for Dirichlet process mixture
Markov chain sampling methods for Dirichlet process mixture
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DOI：
10.2307/1390653
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发表时间：
2000-06-01
影响因子：
2.4
通讯作者：
Neal, RM
中科院分区：
文献类型：
--
作者：
Neal, RM
本文回顾了从狄利克雷过程混合模型的后验分布中进行采样的马尔可夫链方法，并提出了两类新方法。一种新方法是对指标进行 Metropolis-Hastings 更新，指定与每个观测值相关的混合成分，也许辅以部分形式的吉布斯抽样。另一种新方法通过使用一组辅助参数来扩展这些指标的吉布斯抽样。这些方法易于实现，并且比以前处理具有非共轭先验的一般狄利克雷过程混合模型的方法更有效。
This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis-Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling, The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors.