An Integrated Procedure for Bayesian Reliability Inference Using MCMC. (14th January 2014)

• Record Type: Journal Article
• Authors: Lin, Jing
• Other Names: Portinale Luigi Academic Editor.
• Is Part Of: Journal of quality and reliability engineering. Volume 2014(2014)
• Publication Date: 2014-01-14
• Subjects: Reliability (Engineering) -- Periodicals; Engineering -- Quality control -- Periodicals; Failure analysis (Engineering) -- Periodicals; Engineering -- Quality control; Failure analysis (Engineering); Reliability (Engineering); Electronic journals; Periodicals; 620.0045
• DOI: 10.1155/2014/264920
• Languages: English
• ISSNs: 2314-8055

Abstract

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.