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This paper summarizes a methodology for reliability prediction of new products where field data are sparse, and the allowed number & length of experiments are limited. The methodology relies on estimating a set where the unknown parameters are most likely to be found, calculation of an upper bound for the reliability metric of interest conditioned that the parameters reside in the estimated set, and tightening the bounds via design of experiments. Models of failure propagation, failure acceleration, system operations, and time/cycle to failure at various levels of fidelity & expert elicited information may be incorporated to enhance the accuracy of the predictions. The application of the model is illustrated through numerical studies.