Table of Contents
International Scholarly Research Notices
Volume 2014 (2014), Article ID 507634, 12 pages
http://dx.doi.org/10.1155/2014/507634
Research Article

Bayesian Inference for Source Reconstruction: A Real-World Application

1Defence Research and Development Canada, Suffield Research Centre, P.O. Box 4000 Stn Main, Medicine Hat, AB, Canada T1A 8K6
2Health Canada, Radiation Protection Bureau, 775 Brookfield Road, A.L. 6302A, Ottawa, ON, Canada K1A 1C1

Received 9 May 2014; Revised 13 June 2014; Accepted 14 June 2014; Published 25 September 2014

Academic Editor: Ka-Veng Yuen

Copyright © 2014 Her Majesty the Queen in Right of Canada. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. A. Shea and S. A. Lister, “The BioWatch program: detection of bioterrorism (online),” Congressional Research Service Report RL 35152, 2003, http://fas.org/sgp/crs/terror/RL32152.html. View at Google Scholar
  2. CTBTO Preparatory Commission, The Global Verification Regime and the International Monitoring System, Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), 2001.
  3. L. Robertson and C. Persson, “Attempts to apply four dimensional data assimilation of radiological data using the adjoint technique,” Radiation Protection Dosimetry, vol. 50, no. 2–4, pp. 333–337, 1993. View at Google Scholar · View at Scopus
  4. L. Robertson and J. Langner, “Source function estimate by means of variational data assimilation applied to the ETEX-I tracer experiment,” Atmospheric Environment, vol. 32, no. 24, pp. 4219–4225, 1998. View at Publisher · View at Google Scholar · View at Scopus
  5. L. C. Thomson, B. Hirst, G. Gibson et al., “An improved algorithm for locating a gas source using inverse methods,” Atmospheric Environment, vol. 41, no. 6, pp. 1128–1134, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Bocquet, “Reconstruction of an atmospheric tracer source using the principle of maximum entropy—I: theory,” Quarterly Journal of the Royal Meteorological Society, vol. 131, no. 610, pp. 2191–2208, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. C. T. Allen, G. S. Young, and S. E. Haupt, “Improving pollutant source characterization by better estimating wind direction with a genetic algorithm,” Atmospheric Environment, vol. 41, no. 11, pp. 2283–2289, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Keats, E. Yee, and F.-S. Lien, “Bayesian inference for source determination with applications to a complex urban environment,” Atmospheric Environment, vol. 41, no. 3, pp. 465–479, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. F. K. Chow, B. Kosović, and S. Chan, “Source inversion for contaminant plume dispersion in urban environments using building-resolving simulations,” Journal of Applied Meteorology and Climatology, vol. 47, no. 6, pp. 1533–1572, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. I. Senocak, N. W. Hengartner, M. B. Short, and W. B. Daniel, “Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference,” Atmospheric Environment, vol. 42, no. 33, pp. 7718–7727, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. E. Yee, F.-S. Lien, A. Keats, and R. D'Amours, “Bayesian inversion of concentration data: source reconstruction in the adjoint representation of atmospheric diffusion,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 10-11, pp. 1805–1816, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Yee, “Theory for reconstruction of an unknown number of contaminant sources using probabilistic inference,” Boundary-Layer Meteorology, vol. 127, no. 3, pp. 359–394, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. E. Yee, “Probability theory as logic: data assimilation for multiple source reconstruction,” Pure and Applied Geophysics, vol. 169, no. 3, pp. 499–517, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Yee, “Source reconstruction: a statistical mechanics perspective,” International Journal of Environment and Pollution, vol. 48, no. 1–4, pp. 203–213, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Yee, “Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis,” ISRN Applied Mathematics, vol. 2012, Article ID 465320, 20 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  16. R. Cox, “Probability, frequency, and reasonable expectation,” American Journal of Physics, vol. 14, no. 1, pp. 1–13, 1946. View at Publisher · View at Google Scholar
  17. E. T. Jaynes, Probability Theory: The Logic of Science, Cambridge University Press, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  18. G. I. Marchuk, “Formulation of the theory of perturbations for complicated models,” Applied Mathematics and Optimization, vol. 2, no. 1, pp. 1–33, 1975. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. F. X. Le Dimet and O. Talagrand, “Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects,” Tellus A, vol. 38, no. 2, pp. 97–110, 1986. View at Google Scholar · View at Scopus
  20. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, CRC Press (Chapman and Hall), 1991.
  21. A. Gelman, J. B. Carlin, H. S. Stern, and B. R. Rubin, Bayesian Data Analysis, CRC Press, 2nd edition, 2004. View at MathSciNet
  22. V. Savchuk and C. P. Tsokos, Bayesian Theory and Methods, Springer, 2014.
  23. K. -V. Yuen, Bayesian Methods for Structural Dynamics and Civil Engineering, John Wiley and Sons, 2010.
  24. E. Laloy and J. A. Vrugt, “High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing,” Water Resources Research, vol. 48, no. 1, Article ID W01526, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. J. S. Liu, F. Liang, and W. H. Wong, “The multiple-try method and local optimization in Metropolis sampling,” Journal of the American Statistical Association, vol. 95, no. 449, pp. 121–134, 2000. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. A. G. Gelman and D. B. Rubin, “Inference from iterative simulation using multiple sequences,” Statistical Science, vol. 7, no. 4, pp. 457–472, 1992. View at Publisher · View at Google Scholar