An Integrated Procedure for Bayesian Reliability Inference using Markov Chain Monte Carlo Methods
2014 (English)In: Journal of Quality and Reliability Engineering, ISSN 2314-8055, E-ISSN 2314-8047, Vol. 2014, 264920Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2014. Vol. 2014, 264920
Research subject Operation and Maintenance
IdentifiersURN: urn:nbn:se:ltu:diva-13061DOI: 10.1155/2014/264920Local ID: c37a2f00-ddf1-44a8-be21-124ba93d29b5OAI: oai:DiVA.org:ltu-13061DiVA: diva2:986012
Godkänd; 2014; 20140108 (linjan)2016-09-292016-09-29Bibliographically approved