2022Communication in Statistics- Theory and MethodsRequires access

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

Han Ming

Open publisher page 5 citations

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.

About this research paper

What this paper is about

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.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available 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.

Key concepts: Bayesian probability, Bayesian hierarchical modeling, Bayes estimator, Bayesian average, Computer science, Estimation, Markov chain Monte Carlo, Variable-order Bayesian network

Related papers

Back to paper searchBrowse research topicsOriginal source
Take a look at the hierarchical Bayesian estimation of parameters from several different angles — Research Paper | ScholarLens