The recent proliferation of Markov chain Monte Carlo (MCMC) approaches has led to the use of the Bayesian inference in a wide\r\nvariety of fields. To facilitateMCMC applications, this paper proposes an integrated procedure for Bayesian inference usingMCMC\r\nmethods, froma reliability perspective.Thegoal is to build a framework for related academic research and engineering applications\r\nto implementmodern computational-based Bayesian approaches, especially for reliability inferences.Theprocedure developed here\r\nis a continuous improvement process with four stages (Plan, Do, Study, and Action) and 11 steps, including: (1) data preparation; (2)\r\nprior inspection and integration; (3) prior selection; (4)model selection; (5) posterior sampling; (6)MCMCconvergence diagnostic;\r\n(7) Monte Carlo error diagnostic; (8) model improvement; (9) model comparison; (10) inference making; (11) data updating and\r\ninference improvement. The paper illustrates the proposed procedure using a case study.
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