期刊
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
卷 23, 期 6, 页码 445-466出版社
KOREAN STATISTICAL SOC
DOI: 10.5351/CSAM.2016.23.6.445
关键词
admissibility; dependent Dirichlet process; Dirichlet process; false consistency; Markov chain Monte Carlo; mixed model; shrinkage estimation
资金
- United States National Science Foundation [DMS-1613110]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1613110] Funding Source: National Science Foundation
Nonparametric Bayesian methods have seen rapid and sustained growth over the past 25 years. We present a gentle introduction to the methods, motivating the methods through the twin perspectives of consistency and false consistency. We then step through the various constructions of the Dirichlet process, outline a number of the basic properties of this process and move on to the mixture of Dirichlet processes model, including a quick discussion of the computational methods used to fit the model. We touch on the main philosophies for nonparametric Bayesian data analysis and then reanalyze a famous data set. The reanalysis illustrates the concept of admissibility through a novel perturbation of the problem and data, showing the benefit of shrinkage estimation and the much greater benefit of nonparametric Bayesian modelling. We conclude with a too-brief survey of fancier nonparametric Bayesian methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据