Bayesian Inference and Model Selection for Software Growth Reliability Models using Gibbs Sampler

몬테칼로 깁스방법을 적용한 소프트웨어 신뢰도 성장모형에 대한 베이지안 추론과 모형선택에 관한 연구

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.