A Review of Bayesian Model Averaging

摘要：

Bayesian Model averaging is a weighted averaging method based on posterior distribution. It considers comprehensively the prior and sample information of model and parameter reduces the model uncertainty. Bayesian Model Averaging improves statistical inference accuracy and provides improved out-of-sample predictive performance. In this paper, we outlines the development of Bayesian model averaging and present research status, details of the Bayesian model averaging principle, the specific algorithm, and compares the Bayesian method with the traditional P -values.

关键词： bayesian model averaging model uncertainty

作者: HUA Peng ZHAO Xuemin

作者单位: Center for Applied Statistics, Renmin University of P.R.China, Beijing, 100872 College of Informatio School of Economics, Peking University of Beijing, P.R.China, 100871

会议类型: 国际会议

会议名称: The 3rd International Institute of Statistics & Management Engineering Symposium(2010 国际统计与管理工程研讨会 IISMES)

会议地点: 威海

会议语种:英文

页码: 32-37

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）