The Annals of Applied Statistics

Detection of radioactive material entering national ports: A Bayesian approach to radiation portal data

Siddhartha R. Dalal and Bing Han

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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.

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Ann. Appl. Stat., Volume 4, Number 3 (2010), 1256-1271.

First available in Project Euclid: 18 October 2010

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Bayesian classifier Poisson model nuclear detection terrorism machine learning


Dalal, Siddhartha R.; Han, Bing. Detection of radioactive material entering national ports: A Bayesian approach to radiation portal data. Ann. Appl. Stat. 4 (2010), no. 3, 1256--1271. doi:10.1214/10-AOAS334.

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