Abstract
Abstract In the analysis for masked system lifetime data, the exact component that causes the system's failure is often unknown. For each series system at test, we observe its system's failure time and a set of components that includes the component actually causing the system to fail. The objective is to make inferences for the reliability of the components and their diagnostic probabilities. Basic Bayesian models are reviewed. Typically, both Expectation and Maximization (EM) and Markov Chain Monte Carlo (MCMC) algorithms are employed for inferences. Both exponential distributions and Weibull distributions for the component lifetimes and some masking probability models are used here to illustrate these techniques.
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