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An Integrated Procedure for Bayesian Reliability Inference Using MCMC.
- Source :
- Journal of Quality & Reliability Engineering; 2014, p1-16, 16p
- Publication Year :
- 2014
-
Abstract
- The recent proliferation of Markov chain Monte Carlo (MCMC) approaches has led to the use of the Bayesian inference in a wide variety of fields. To facilitate MCMC applications, this paper proposes an integrated procedure for Bayesian inference using MCMC methods, from a reliability perspective. The goal is to build a framework for related academic research and engineering applications to implement modern computational-based Bayesian approaches, especially for reliability inferences. The procedure developed here is a continuous improvement process with four stages (Plan, Do, Study, and Action) and 11 steps, including: (1) data preparation; (2) prior inspection and integration; (3) prior selection; (4) model selection; (5) posterior sampling; (6) MCMC convergence diagnostic; (7) Monte Carlo error diagnostic; (8) model improvement; (9) model comparison; (10) inference making; (11) data updating and inference improvement. The paper illustrates the proposed procedure using a case study. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23148055
- Database :
- Complementary Index
- Journal :
- Journal of Quality & Reliability Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 100502760
- Full Text :
- https://doi.org/10.1155/2014/264920