@article{hollis_moore_wilson_clark_2024, title={From FMECA to Decision: A Fully Bayesian Reliability Process}, volume={29}, ISSN={["2163-2758"]}, DOI={10.5711/1082598329145}, abstractNote={Reliability testing of complex military systems is often hindered by the difficulty or cost of the testing. Therefore, conventional methodologies that rely on repeated measurements under differing conditions cannot be used. However, these systems are often designed using subcomponents that do have testing data available. In this manuscript, we demonstrate how Bayesian methodologies can be used throughout the testing process to combine subcomponent reliability score, under various levels of certainty, in order to inform, and maximize the impact of, a small number of full system tests. We show how this can be used for the U.S. Army’s long-range precision fires lines of effort and conclude that Bayesian inference should be incorporated into future experimental design for these complex systems.}, number={1}, journal={MILITARY OPERATIONS RESEARCH}, author={Hollis, Andrew N. and Moore, Timothy A. and Wilson, Alyson G. and Clark, Nicholas J.}, year={2024} }
 @article{hollis_smith_wilson_2021, title={SURROGATE BASED MUTUAL INFORMATION APPROXIMATION AND OPTIMIZATION FOR URBAN SOURCE LOCALIZATION}, volume={11}, ISSN={["2152-5099"]}, url={http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2021034400}, DOI={10.1615/Int.J.UncertaintyQuantification.2021034400}, abstractNote={The ability to efficiently and accurately localize potentially threatening nuclear radiation sources in urban environments is of critical importance to national security. Techniques to infer the location and intensity of a source using data from a configuration of radiation detectors, and the effectiveness of the source localization depends critically on how the detectors are configured. In this paper, we introduce a framework that uses surrogate models to efficiently compare and optimize different detector configurations. We compare our technique to others and demonstrate its effectiveness for selecting optimal detector configurations in the context of urban source localization.}, number={5}, journal={INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION}, publisher={Begell House}, author={Hollis, Andrew N. and Smith, Ralph C. and Wilson, Alyson G.}, year={2021}, pages={39–55} }