Bayesian Approach for Software Reliability Models

소프트웨어 신뢰모형에 대한 베이지안 접근

  • Choi, Ki-Heon (Department of Statistics, Duksung Women's University)
  • 최기헌 (덕성여자대학교 통계학과)
  • Published : 1999.04.30

Abstract

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

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