A Study on Bayesian Approach of Software Stochastic Reliability Superposition Model using General Order Statistics
Byeong-Su Lee, Hui-Cheol Kim, Su-Gi Baek, Gwan-Hui Jeong, Ju-Yong Yun · The Transactions of the Korea Information Processing Society · 1999
The complicate software failure system is defined to the superposition of the points of failure from several component point process. Because the likelihood function is difficulty in computing, we consider Gibbs sampler using iteration sampling based method. For each observed failure epoch, we applied to latent variables that indicates with component of the superposition mode. For model selection, we explored the posterior Bayesian criterion and the sum of relative errors for the comparison simple pattern with superposition model. A numerical example with NHPP simulated data set applies the thinning method proposed by Lewis and Shedler[25] is given, we consider Goel-Okumoto model and Weibull model with GOS, inference of parameter is studied. Using the posterior Bayesian criterion and the sum of relative errors, as we would expect, the superposition model is best on model under diffuse priors.