Detection of biological and chemical threats is an important consideration in the modern national defense policy. Much of the testing and evaluation of threat detection technologies is performed without appropriate uncertainty quantification. This paper proposes an approach to analyzing the effect of threat concentration on the probability of detecting chemical and biological threats. The approach uses a probit semi-parametric formulation between threat concentration level and the probability of instrument detection. It also utilizes a bayesian adaptive design to determine at which threat concentrations the tests should be performed. The approach offers unique advantages, namely, the flexibility to model non-monotone curves and the ability to test in a more informative way. We compare the performance of this approach to current threat detection models and designs via a simulation study.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Ferguson, Bradley Thomas, "Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models" (2011). Theses and Dissertations. 2728.
Gaussian process, bayesian, adaptive design