Date of Award
2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science : Modeling and Simulation
Committee Chair
Mikel D. Petty
Committee Member
Harry Delugach
Committee Member
David Moody
Subject(s)
Bayesian statistical decision theory, Computer simulation, Sequential analysis
Abstract
Testing for large and complex defense systems can be extremely expensive. A testing strategy is developed to leverage Bayesian Experimental Design concepts within the Model-Test-Model paradigm. Two utility functions are implemented to search the input space and select the test which maximizes the expected information gain per test. A priority-based strategy focuses on individual performance measures in order of importance. A weighted-sum strategy maximizes the information gained across all performance measures, with emphasis placed on higher priority measures which are further from their accuracy specifications. Monte Carlo simulations show that both strategies provide a statistically significant improvement over the baseline random selection strategy. The weighted-sum strategy is found to be more computationally intensive but is still recommended over the alternative due to its better performance in certain cases.
Recommended Citation
Paul, Geoffrey W., "A Bayesian testing strategy for the model-test-model paradigm" (2017). Theses. 213.
https://louis.uah.edu/uah-theses/213