  Given the potential for illicit nuclear material being used for terrorism,
most ports now inspect a large number of goods entering national borders for
radioactive cargo. The U.S. Department of Homeland Security is moving toward
one hundred percent inspection of all containers entering the U.S. at various
ports of entry for nuclear material. We propose a Bayesian classification
approach for the real-time data collected by the inline Polyvinyl Toluene
radiation portal monitors. We study the computational and asymptotic properties
of the proposed method and demonstrate its efficacy in simulations. Given data
available to the authorities, it should be feasible to implement this approach
in practice.
