  Usually, methods evaluating system reliability require engineers to quantify
the reliability of each of the system components. For series and parallel
systems, there are some options to handle the estimation of each component's
reliability. We will treat the reliability estimation of complex problems of
two classes of coherent systems: series-parallel, and parallel-series. In both
of the cases, the component reliabilities may be unknown. We will present
estimators for reliability functions at all levels of the system (component and
system reliabilities). Nonparametric Bayesian estimators of all
sub-distribution and distribution functions are derived, and a Dirichlet
multivariate process as a prior distribution is presented. Parametric estimator
of the component's reliability based on Weibull model is presented for any kind
of system. Also, some ideas in systems with masked data are discussed.
