Maximum likelihood and Bayesian inference for common-cause of failure model

被引：13

作者：

Nguyen, H. D. ^{[1
]}

Gouno, E. ^{[2
]}

机构：

[1] Dong A Univ, Div Artificial Intelligence, Da Nang, Vietnam

[2] Univ South Brittany, LMBA, Campus Tohann, F-56017 Vannes, France

来源：

RELIABILITY ENGINEERING & SYSTEM SAFETY | 2019年 / 182卷

关键词：

Binomial failure-rate model; Common-cause failure; Poisson process; Maximum likelihood; Bayesian estimation; MIXTURE-MODEL;

D O I：

10.1016/j.ress.2018.10.003

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper considers the statistical analysis of the Binomial Failure Rate (BFR) common-cause model in detail. Computational aspects of maximum likelihood and Bayesian methods are investigated. An Expectation-maximization (EM) algorithm to obtain maximum likelihood estimates is suggested to deal with missing data inherent for common-cause failures. A Bayesian approach is developed and the modified-Beta distribution is defined to characterize the posterior distribution for one of the model parameters. The different methods are applied and compared on both simulated and real data.

引用

页码：56 / 62

页数：7

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