THE BOOTSTRAP AND MARKOV-CHAIN MONTE CARLO

被引：39
|
作者：
Efron, Bradley [1 ]
机构：
[1] Stanford Univ, Stanford, CA 94305 USA
来源：
JOURNAL OF BIOPHARMACEUTICAL STATISTICS | 2011年 / 21卷 / 06期
基金：
美国国家科学基金会;
关键词：
Bayes credible intervals; Gibbs sampling; Importance sampling; MCMC; BAYESIAN-INFERENCE;
D O I：
10.1080/10543406.2011.607736
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
This note concerns the use of parametric bootstrap sampling to carry out Bayesian inference calculations. This is only possible in a subset of those problems amenable to Markov-Chain Monte Carlo (MCMC) analysis, but when feasible the bootstrap approach offers both computational and theoretical advantages. The discussion here is in terms of a simple example, with no attempt at a general analysis.
引用
收藏
页码：1052 / 1062
页数：11
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