Take a look at the hierarchical Bayesian estimation of parameters from several different angles

Abstract

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.

Keywords:

• Hierarchical Bayesian models
• hierarchical prior density
• hierarchical Bayesian estimation
• E-Bayesian estimation
• MCMC method

Acknowledgments

The author wish to thank Professor Xizhi Wu, who checked the paper and gave author very helpful suggestions. The author are very grateful to the anonymous reviewers for their insightful and constructive comments and suggestions that have led to an improved version of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was partially supported by the Natural Science Foundation of Zhejiang Province [No. LY18A010026].