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

Full-text: Open access

Abstract

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.

Article information

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

Dates
First available in Project Euclid: 18 October 2010

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1287409372

Digital Object Identifier
doi:10.1214/10-AOAS334

Mathematical Reviews number (MathSciNet)
MR2758327

Zentralblatt MATH identifier
1202.62184

Keywords
Bayesian classifier Poisson model nuclear detection terrorism machine learning

Citation

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. https://projecteuclid.org/euclid.aoas/1287409372


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References

  • Dalal, S. and Hall, W. J. (1983). Approximating priors by mixtures of natural conjugate priors. J. Roy. Statist. Soc. Ser. B 45 278–286.
  • Diaconis, P. and Ylvisaker, D. (1985). Quantifying prior opinion. In Bayesian Statistics 2 (J. Bernardo, M. DeGroot, D. Lindley and A. Smith, eds.) 133–156. North-Holland, Amsterdam.
  • Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero–one loss. Mach. Learn. 29 103–130.
  • Ely, J., Kouzes, R., Geelhood, B., Schweppe, J. and Warner, R. (2004). Discrimination of naturally occurring radioactive material in plastic scintillator material. IEEE Transactions on Nuclear Science 51 1672–1676.
  • Ely, J., Kouzes, R., Schweppe, J., Siciliano, E., Strachan, D. and Weier, D. (2006). The use of energy windowing to discriminate SNM from NORM in radiation portal monitors. Nuclear Instrument and Methods in Physics Research Section A 560 373–387.
  • Ferguson, T. S. (1967). Mathematical Statistics: A Decision Theoretic Approach. Academic Press, New York.
  • Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, New York.
  • Karlin, S. and Taylor, H. (1998). An Introduction to Stochastic Modeling. Academic Press, San Diego, CA.
  • Klosgen, W. and Zytkow, J. (2002). Handbook of Data Mining and Knowledge Discovery. Oxford Univ. Press, New York.
  • Martonosi, S., Oritz, D. and Willis, H. (2006). Evaluating the viability of 100 percent container inspection at America’s ports. In The Economic Impact of Terrorist Attacks (H. W. Richardson, P. Gordon and J. Moore II, eds.) 218–241. Edward Elgar Publishing, Northampton, MA.
  • Wein, L., Wilkins, A., Maven, M. and Flynn, S. (2006). Preventing the importation of illicit nuclear materials in shipping containers. Risk Analysis 26 1377–1393.
  • Ye, N. (2003). The Handbook of Data Mining. LRA, Mawah, NY.
  • Zhang, H. (2004). The optimality of naive Bayes. In Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (V. Barr and Z. Markov, eds.) 133–156. AAAI Press, Menlo Park, CA.