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.
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).