  We present a Bayesian nonparametric system reliability model which scales
well and provides a great deal of flexibility in modeling. The Bayesian
approach naturally handles the disparate amounts of component and subsystem
data that may exist. However, traditional Bayesian reliability models are quite
computationally complex, relying on MCMC techniques. Our approach utilizes the
conjugate properties of the beta-Stacy process, which is the fundamental
building block of our model. These individual models are linked together using
a method of moments estimation approach. This model is computationally fast,
allows for right-censored data, and is used for estimating and predicting
system reliability.
