Theory & Methods: An Empirical Bayes Inference for the von Mises Distribution
Theory & Methods: An Empirical Bayes Inference for the von Mises Distribution
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理论
DOI：
10.1111/1467-842x.00140
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发表时间：
2000
影响因子：
1.1
通讯作者：
L. Milan
中科院分区：
文献类型：
--
作者：
J. Rodrigues;José Galvão Leite;L. Milan
von Mises分布是角数据统计推断中最有用的分布，本文对其进行了经验贝叶斯分析。提出了一种两阶段信息先验，其中超参数从其中一个阶段的数据中获得。这种经验的或近似的贝叶斯推理是在最大熵的基础上证明的，它消除了修正的贝塞尔函数。通过Metropolis - within - Gibbs算法考虑了一个具有真实数据和真实先验分布的回归系数的例子。
This paper develops an empirical Bayesian analysis for the von Mises distribution, which is the most useful distribution for statistical inference of angular data. A two‐stage informative prior is proposed, in which the hyperparameter is obtained from the data in one of the stages. This empirical or approximate Bayes inference is justified on the basis of maximum entropy, and it eliminates the modified Bessel functions. An example with real data and a realistic prior distribution for the regression coefficients is considered via a Metropolis‐within‐Gibbs algorithm.