• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistical science >From EM to Data Augmentation: The Emergence of MCMC Bayesian Computation in the 1980s
【24h】

From EM to Data Augmentation: The Emergence of MCMC Bayesian Computation in the 1980s

机译：从EM到数据增强：1980年代MCMC贝叶斯计算的出现

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

It was known from Metropolis et al. [J. Chem. Phys. 21 (1953) 1087-1092] that one can sample from a distribution by performing Monte Carlo simulation from a Markov chain whose equilibrium distribution is equal to the target distribution. However, it took several decades before the statistical community embraced Markov chain Monte Carlo (MCMC) as a general computational tool in Bayesian inference. The usual reasons that are advanced to explain why statisticians were slow to catch on to the method include lack of computing power and unfamiliarity with the early dynamic Monte Carlo papers in the statistical physics literature. We argue that there was a deeper reason, namely, that the structure of problems in the statisti-cal mechanics and those in the standard statistical literature are different. To make the methods usable in standard Bayesian problems, one had to exploit the power that comes from the introduction of judiciously chosen auxiliary variables and collective moves. This paper examines the development in the critical period 1980-1990, when the ideas of Markov chain simulation from the statistical physics literature and the latent variable formulation in maxi-mum likelihood computation (i.e., EM algorithm) came together to spark the widespread application of MCMC methods in Bayesian computation.
机译：从Metropolis等人知道。 [J.化学物理[J.Am.Chem.Soc.21（1953）1087-1092]指出，可以通过从平衡分布等于目标分布的马尔可夫链上进行蒙特卡罗模拟来从分布中采样。但是，统计界花了几十年的时间才将马尔可夫链蒙特卡罗（MCMC）用作贝叶斯推理中的通用计算工具。解释统计学家为何迟迟不能采用该方法的常见原因包括缺乏计算能力以及对统计物理学文献中早期的动态蒙特卡洛论文不熟悉。我们认为存在更深层次的原因，即统计力学中的问题结构与标准统计文献中的问题结构不同。为了使这些方法在标准贝叶斯问题中可用，必须利用明智引入的辅助变量和集体运动的引入所产生的影响。本文考察了1980年至1990年关键时期的发展，当时统计物理学文献中的马尔可夫链模拟思想和最大似然计算中的潜在变量公式（即EM算法）共同引发了大规模应用。贝叶斯计算中的MCMC方法。

著录项

  • 来源
    《Statistical science》 |2010年第4期|p.506-516|共11页
  • 作者

    Martin A. Tanner; Wing H. Wong;

  • 作者单位

    Department of Statistics,Northwestern University, Evanston, Illinois 60208, USA;

    rnDepartments of Statistics and Health Research and Policy, Stanford University, Stanford, California 94305,USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    data augmentation; EM algorithm; MCMC;

    机译：数据扩充;EM算法;MCMC;

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