Parameter Identification in Degradation Modeling by Reversible-Jump Markov Chain Monte Carlo
In this work, the reversible-jump Markov chain Monte Carlo technique is applied for identifying the parameters governing stochastic processes of component degradation. Two case studies are examined concerning the evolution of deteriorating systems whose parameters undergo step changes in time. The method turns out to be capable of identifying the instances of change in behavior, and of estimating the parameter values. A Bayesian updating strategy is proposed to refine the parameter estimates as new data are made available.
reversible-jump markov chain monte carlo bayesian inference parameter changes estimation