The world of commercial aviation is extremely important to the infrastructure of countless industries within and outside of the United States. Beginning with former Vice President Al Gore's "Safer Skies Program" in 1997, the safety of domestic air travel has become an important area of research and investigation. As a result, NASA has been developing numerous new technological products to aid in the improvement of airline safety. Under contract from NASA's Aviation Safety and Security Program (AvSSP), Rutgers University has been modeling aircraft accident cases with Bayesian Belief Networks (BBNs) to identify accident precursors and to assess the potential risk mitigating effects of new technology products in these types of accidents. BBNs develop mathematical models through graphical representation of the inter-dependency of events or system components. This methodology has become increasingly more established for the representation of knowledge and reasoning under uncertainty. Systems in which data cannot be collected or do not exist (e.g. human factors) provide a natural fit for the Bayesian approach. In addition, calculations, both forward and backward, of how events influence each other and combinatorial effects of components are made efficient with this methodology. This paper begins with validation of Rutgers' existing model structure through external audit and then presents advanced BBN analysis with both sensitivity and importance aspects to identify the most influential accident precursors. The preliminary findings are used to propose and explore new technology and system concepts to improve aviation safety.