Bayesian inference and the parametric bootstrap.
Bayesian inference and the parametric bootstrap.
复制标题
贝叶斯推理和参数引导程序。
DOI：
10.1214/12-aoas571
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发表时间：
2012-10-01
期刊：
影响因子：
--
通讯作者：
Efron B
中科院分区：
文献类型：
--
作者：
Efron B
参数自举法可用于贝叶斯后验分布的有效计算。重要抽样公式在指数族中具有与偏差相关的简单形式，并且从Jeffreys不变先验出发特别简单。因为身份证。自举抽样的性质，熟悉的公式描述贝叶斯估计的计算精度。除了计算方法，该理论提供了贝叶斯和频率分析之间的联系。贝叶斯推理的频率论精度的有效算法的开发和示范模型选择的例子。
The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families, and are particularly simple starting from Jeffreys invariant prior. Because of the i.i.d. nature of bootstrap sampling, familiar formulas describe the computational accuracy of the Bayes estimates. Besides computational methods, the theory provides a connection between Bayesian and frequentist analysis. Efficient algorithms for the frequentist accuracy of Bayesian inferences are developed and demonstrated in a model selection example.