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

Nguyễn Hữu Du - Dong A University, Division of Artificial Intelligence, Da Nang, Vietnam

Reliability Engineering & System Safety, SCIE, Q1

Abstract

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.

DOI: https://doi.org/10.1016/j.ress.2018.10.003