Session 2A: Using Bayesian Network of subsystem statistical models to assess system behavior
Dr. James Theimer is a Scientific Test and Analysis Techniques Expert employed by Huntington Ingles Industries Technical Solutions and working to support the Homeland Security Center of Best Practices.
Dr. Theimer worked for Air Force Research Laboratory and predecessor organizations for more than 35 years. He worked on modeling and simulation of sensors systems and supporting devices. His doctoral research was on modeling pulse formation in fiber lasers. He worked with a semiconductor reliability team as a reliability statistician and led a team which studied statistical validation of models of automatic sensor exploitation systems. This team also worked with programs to evaluate these systems.
Dr. Theimer has a PhD in Electrical Engineering from Rensselaer Polytechnic Institute, and MS in Applied Statistics from Wright State University, and MS in Atmospheric Science from SUNY Albany and a BS in Physics from University of Rochester.
Situations exists when a system-level test is rarely accomplished or simply not feasible. When subsystem testing is available, to include creating a subsystem statistical model, an approach is required to combine these models. A Bayesian Network (BN) is an approach to address this problem. A BN models system behavior using subsystem statistical models. The system is decomposed into a network of subsystems and the interactions between the subsystems are described. Each subsystem is in turn described by a statistical model which determines the subjective probability distribution of the outputs given a set of inputs. Previous methods have been developed for validating performance of the subsystem models and subsequently what can be known about system performance. This work defined a notional system, created the subsystem statistical models, generated synthetic data, and developed the Bayesian Network.
Then, subsystem models are validated followed by a discussion on how system level information is derived from the Bayesian Network.
Session Materials: https://dataworks.testscience.org/wp-...