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Software Reliability Data Analysis with Marshall-Olkin Extended Weibull Model using MCMC Method for Non-Informative Set of Priors

by Ashwini K. Srivastava, Vijay Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 18 - Number 4
Year of Publication: 2011
Authors: Ashwini K. Srivastava, Vijay Kumar
10.5120/2271-2926

Ashwini K. Srivastava, Vijay Kumar . Software Reliability Data Analysis with Marshall-Olkin Extended Weibull Model using MCMC Method for Non-Informative Set of Priors. International Journal of Computer Applications. 18, 4 ( March 2011), 31-39. DOI=10.5120/2271-2926

@article{ 10.5120/2271-2926,
author = { Ashwini K. Srivastava, Vijay Kumar },
title = { Software Reliability Data Analysis with Marshall-Olkin Extended Weibull Model using MCMC Method for Non-Informative Set of Priors },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 18 },
number = { 4 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 31-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume18/number4/2271-2926/ },
doi = { 10.5120/2271-2926 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:05:26.506759+05:30
%A Ashwini K. Srivastava
%A Vijay Kumar
%T Software Reliability Data Analysis with Marshall-Olkin Extended Weibull Model using MCMC Method for Non-Informative Set of Priors
%J International Journal of Computer Applications
%@ 0975-8887
%V 18
%N 4
%P 31-39
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the two-parameter Marshall-Olkin Extended Weibull (MOEW) model is considered to analyze the software reliability data. The Markov Chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of the model parameters. In this paper, it is assumed that the parameters have non-informative set of priors and they are independently distributed. Under the above priors, we use Gibbs algorithm in OpenBUGS to generate MCMC samples from the posterior density function. Based on the generated samples, we can compute the Bayes estimates of the unknown parameters and also can construct highest posterior density credible intervals. We also compute the maximum likelihood estimate and associated confidence intervals to compare the performances of the Bayes estimators with the classical estimators. One data analysis is performed for illustrative purposes.

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Index Terms

Computer Science
Information Sciences

Keywords

Marshall-Olkin Extended Weibull (MOEW) model Parameter estimation Maximum likelihood estimate (MLE) Bayes estimates Markov Chain Monte Carlo (MCMC)

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