• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America >A Marginal Sampler for sigma-Stable Poisson-Kingman MixtureModels
【24h】

A Marginal Sampler for sigma-Stable Poisson-Kingman MixtureModels

机译：Sigma-stable Poisson-Kingman MixtureModels的边缘采样器

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We investigate the class of sigma-stable Poisson-Kingman random probability measures (RPMs) in the context of Bayesian nonparametric mixture modeling. This is a large class of discrete RPMs, which encompasses most of the popular discrete RPMs used in Bayesian nonparametrics, such as the Dirichlet process, PitmanYor process, the normalized inverse Gaussian process, and the normalized generalized Gamma process. We show how certain sampling properties and marginal characterizations of sigma-stable Poisson-Kingman RPMs can be usefully exploited for devising aMarkov chainMonte Carlo (MCMC) algorithm for performing posterior inferencewith a Bayesian nonparametric mixture model. Specifically, we introduce a novel and efficient MCMC sampling scheme in an augmented space that has a small number of auxiliary variables per iteration. We apply our sampling scheme to a density estimation and clustering tasks with unidimensional and multidimensional datasets, and compare it against competing MCMC sampling schemes. Supplementary materials for this article are available online.
机译：我们在贝叶斯非参数混合物建模的背景下调查Sigma-Sably Poisson-Kingman随机概率措施（RPMS）的课程。这是一类大类离散的RPM，它包括贝叶斯非参数学中使用的大多数流行的离散RPM，例如Dirichlet工艺，PitManyor工艺，标准化的逆高斯过程和标准化的广义伽马过程。我们展示了Sigma-stable泊松 - 金曼RPMS的某些采样性质和边缘特征是如何利用用于设计Amarkov Chainmonte Carlo（MCMC）算法的用于对贝叶斯非参数混合模型进行后续推断进行设计。具体地，我们在增强空间中介绍了一种新颖的和高效的MCMC采样方案，其迭代具有少量辅助变量。我们将采样方案应用于密度估计和聚类任务，具有单向和多维数据集，并将其与竞争MCMC采样方案进行比较。本文的补充材料可在线获得。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号