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http://dx.doi.org/10.5351/KJAS.2008.21.5.835

Bayesian Inferences for Software Reliability Models Based on Beta-Mixture Mean Value Functions  

Nam, Seung-Min (Samsung Fire & Marine Insurance Co.)
Kim, Ki-Woong (Samsung Fire & Marine Insurance Co.)
Cho, Sin-Sup (Dept. of Statistics, Seoul National University)
Yeo, In-Kwon (Dept. of Statistics, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.21, no.5, 2008 , pp. 835-843 More about this Journal
Abstract
In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.
Keywords
Beta-mixture; MCMC; mean value function; nonhomogeneous Poisson processes;
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