Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Bayesian parameter estimation and prediction in NHPP software reliability growth model

NHPP소프트웨어 신뢰도 성장모형에서 베이지안 모수추정과 예측

  • Chang, Inhong (Department of Computer Science and Statistics, Chosun University) ;
  • Jung, Deokhwan (Department of Computer Science and Statistics, Chosun University) ;
  • Lee, Seungwoo (Department of Computer Science and Statistics, Chosun University) ;
  • Song, Kwangyoon (Department of Computer Science and Statistics, Chosun University)
  • 장인홍 (조선대학교 컴퓨터통계학과) ;
  • 정덕환 (조선대학교 전산통계학과) ;
  • 이승우 (조선대학교 전산통계학과) ;
  • 송광윤 (조선대학교 전산통계학과)
  • Received : 2013.06.03
  • Accepted : 2013.06.24
  • Published : 2013.07.31
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Abstract

In this paper we consider the NHPP software reliability model. And we deal with the maximum likelihood estimation and the Bayesian estimation with conjugate prior for parameter inference in the mean value function of Goel-Okumoto model (1979). The parameter estimates for the proposed model is presented by MLE and Bayes estimator in data set. We compare the predicted number of faults with the actual data set using the proposed mean value function.

본 논문은 NHPP 소프트웨어 신뢰성모형에서 모수추정과 고장시간에 대한 예측을 다루고자 한다. 소프트웨어 신뢰성모형 Goel-Okumoto모형에서 평균값 함수에 대한 최우추정과 경험적 사전분포를 가정한 공액사전분포에서 베이지안 추정을 다루었다. 실제 자료에서 두 가지 추정법에 의한 모수 추정값을 제공하였으며, 모형의 적합성을 판정하고, 고장수에 대한 예측값을 비교하였다.

Keywords

References

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