An Integrated Procedure for Bayesian Reliability Inference using Markov Chain Monte Carlo Methods

Projekt:

JVTC

Sammanfattning:
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.

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Författare: Jing Lin
Utgivare: Journal of Quality and Reliability Engineering
Utgivningsdatum: 2014
Diarienummer: TRV 2011/58769
ISSN: 2314-8055
Antal sidor: 16
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs