Bayesian Inference for Mixture Failure Model of Rayleigh and Erlang Pattern

RAYLEIGH와 ERLANG 추세를 가진 혼합 고장모형에 대한 베이지안 추론에 관한 연구

  • 김희철 (송호대학 정보산업계열,) ;
  • 이승주 (청주대학교 자연과학부)
  • Published : 2000.09.01


A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.


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