Bayesian Modeling of Time Trends in Component Reliability Data Via Markov Chain Monte Carlo Simulation
نویسنده
چکیده
Markov chain Monte Carlo (MCMC) techniques represent an extremely flexible and powerful approach to Bayesian modeling. This work illustrates the application of such techniques to time-dependent reliability of components with repair. The WinBUGS package is used to illustrate, via examples, how Bayesian techniques can be used for parametric statistical modeling of time-dependent component reliability. Additionally, the crucial, but often overlooked subject of model validation is discussed, and summary statistics for judging the model’s ability to replicate the observed data are developed, based on the posterior predictive distribution for the parameters of interest. The functional form of λ(t) must be specified in order for parametric analysis to proceed. Common forms for λ(t) include the power-law process, 1 −
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