Estimate the True Pass Probability for Near-Real-Time Monitor Challenge Data Using Bayesian Analysis
Date of Award
12-4-2006
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
First Advisor
Yu-Sheng Hsu - Chair
Second Advisor
Pulak Ghosh
Third Advisor
Tambra Dunams
Abstract
The U.S. Army¡¯s Chemical Demilitarization are designed to store, treat and destroy the nation¡¯s aging chemical weapons. It operates Near-Real-Time Monitors and Deport Area Monitoring Systems to detect chemical agent at concentrations before they become dangerous to workers, public health and the environment. CDC recommends that the sampling and analytical methods measure within 25% of the true concentration 95% of the time, and if this criterion is not met the alarm set point or reportable level should be adjusted. Two methods were provided by Army¡¯s Programmatic Laboratory and Monitoring Quality Assurance Plan to evaluate the monitoring systems based on CDC recommendations. This thesis addresses the potential problems associated with these two methods and proposes the Bayesian method in an effort to improve the assessment. Comparison of simulation results indicates that Bayesian method produces a relatively better estimate for verifying monitoring system performance as long as the prior given is correct.
DOI
https://doi.org/10.57709/1059676
Recommended Citation
Xiao, Yuqing, "Estimate the True Pass Probability for Near-Real-Time Monitor Challenge Data Using Bayesian Analysis." Thesis, Georgia State University, 2006.
doi: https://doi.org/10.57709/1059676