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Bayesian Analysis of the Brown–Proschan Model
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Dinh Tuan Nguyen
,
Yann Dijoux
Published/Copyright:
May 20, 2015
Abstract
The paper presents a Bayesian approach of the Brown–Proschan imperfect maintenance model. The initial failure rate is assumed to follow a Weibull distribution. A discussion of the choice of informative and non-informative prior distributions is provided. The implementation of the posterior distributions requires the Metropolis-within-Gibbs algorithm. A study on the quality of the estimators of the model obtained from Bayesian and frequentist inference is proposed. An application to real data is finally developed.
Keywords: Imperfect Repair; Maintenance Efficiency; Bayesian Inference; Metropolis-Within-Gibbs Algorithm
Received: 2014-11-3
Revised: 2014-11-26
Accepted: 2014-12-8
Published Online: 2015-5-20
Published in Print: 2015-6-1
© 2015 by De Gruyter
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Articles in the same Issue
- Frontmatter
- Failure Rate Estimation in a Dynamic Environment
- Bayesian Analysis of the Brown–Proschan Model
- Forecasting Stock Market Trends
- Life in Bridgetown, Barbados, According to the Westbury Cemetery Records 1877–1976
- Numerical Methods of Karhunen–Loève Expansion for Spatial Data
Keywords for this article
Imperfect Repair;
Maintenance Efficiency;
Bayesian Inference;
Metropolis-Within-Gibbs Algorithm
Articles in the same Issue
- Frontmatter
- Failure Rate Estimation in a Dynamic Environment
- Bayesian Analysis of the Brown–Proschan Model
- Forecasting Stock Market Trends
- Life in Bridgetown, Barbados, According to the Westbury Cemetery Records 1877–1976
- Numerical Methods of Karhunen–Loève Expansion for Spatial Data
