This course covers advanced topics in system reliability modeling. These topics include modeling and solution methods for complex logic diagrams (reliability block diagrams, fault and event trees) along with influence diagrams such as Bayesian belief networks. The course will expand on these modeling methodologies to cover static, dynamic, and repairable systems. This includes Markov models, Petri Nets, and simulation techniques. Also covered are modeling of complex interdependencies including dependent failure analysis, and methods for reliability analysis of “X-ware systems” (systems of interacting hardware, software and human operators). The course also covers advanced techniques for reliability parameter estimation based on different types of information, and methods of uncertainty assessment for reliability models and parameters.