The Bayesian methods provide information about the meaningful parameters in a statistical analysis obtained by combining the prior and sampling distributions to form the posterior distribution of theparameters.The desired inferences are obtained from this joint bushranger awning posterior.An estimation strategy for hierarchical models, where the resulting joint distribution of the associated model parameters cannotbe evaluated analytically, is to use sampling algorithms, known as Markov Chain Monte Carlo (MCMC) methods, from which approximate solutions can be obtained.Both serial and parallel configurations of subcomponents are permitted.
The capability of time-dependent method to describe a multi-state system is based on a case study, assessingthe operatial situation of studied system.The rationality and validity of the presented model are demonstrated via a case of 220.10.38.20.02.001 study.The effect of randomness of the structural parameters is alsoexamined.