贝叶斯概率
贝叶斯分层建模
贝叶斯估计量
贝叶斯平均
计算机科学
估计
马尔科夫蒙特卡洛
变阶贝叶斯网络
贝叶斯线性回归
贝叶斯统计
分层数据库模型
泊松分布
贝叶斯推理
多级模型
人工智能
机器学习
数据挖掘
统计
数学
工程类
系统工程
标识
DOI:10.1080/03610926.2022.2056752
摘要
The hierarchical Bayesian method has been paid more and more attention mainly because of its good performance in application. In this paper, we introduced hierarchical Bayesian estimation of parameters from several different angles, mainly including two parts: (i) by traditional method and MCMC method (use OpenBUGS) obtains hierarchical Bayesian estimation; (ii) E-Bayesian estimation (expected Bayesian estimation) and hierarchical Bayesian estimation (the failure data of shared memory processors of supercomputer obey the Poisson distribution). In addition, combined with the data in the two above parts are performed for calculation and analysis.
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