Table of Contents
Journal of Quality and Reliability Engineering
Volume 2014, Article ID 264920, 16 pages
Research Article

An Integrated Procedure for Bayesian Reliability Inference Using MCMC

1Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden
2Luleå Railway Research Centre (JVTC), 97187 Luleå, Sweden

Received 5 August 2013; Revised 27 November 2013; Accepted 28 November 2013; Published 14 January 2014

Academic Editor: Luigi Portinale

Copyright © 2014 Jing Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.