The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Bayesian Inference for Mixture Failure Model of Rayleigh and Erlang Pattern

RAYLEIGH와 ERLANG 추세를 가진 혼합 고장모형에 대한 베이지안 추론에 관한 연구

  • Published : 2000.09.01
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Abstract

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.

마코브체인 몬테칼로방법중에서 깁스 추출방법을 혼합 고장모형에 이용하였다. 베이자안 추론에서 조건부분포를 가지고 사후 분포를 결정하는데 있어서 계산 문제와 이론적인 정당성을 고려하여 감마족인 Rayleigh와 Erlang추세를 가진 혼합모형에 대하여 깁스샘플링 알고리즘을 이용하여 베이지안 계산과 신뢰도 추이를 알아보고 모의실험자료를 이용하여 수치적인 계산을 시행하고 그 결과를 제시하였다.

Keywords

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