A Demonstration of Modern Bayesian Methods for Assessing System Reliability with Multilevel Data and for Allocating Resources

Abstract

Good estimates of the reliability of a system make use of test data and expert knowledge at all available levels. Furthermore, by integrating all these information sources, one can determine how best to allocate scarce testing resources to reduce uncertainty. Both of these goals are facilitated by modern Bayesian computational methods. We demonstrate these tools using examples that were previously solvable only through the use of ingenious approximations, and employ genetic algorithms to guide resource allocation.