Tom T. Little

Using exact Poisson likelihood functions in Bayesian interpretation of counting measurements.

A technique for computing the exact marginalized (integrated) Poisson likelihood function for counting measurement processes involving a background subtraction is described. An empirical Bayesian method for determining the prior probability distribution of background count rates from population data is recommended and would seem to have important practical advantages. The exact marginalized Poisson likelihood function may be used instead of the commonly used Gaussian approximation. Differences occur in some cases of small numbers of measured counts, which are discussed. Optional use of exact likelihood functions in our Bayesian internal dosimetry codes has been implemented using an interpolation-table approach, which means that there is no computation time penalty except for the initial setup of the interpolation tables.

METHODS USED TO CALCULATE DOSES RESULTING FROM INHALATION OF CAPSTONE DEPLETED URANIUM AEROSOLS

The methods used to calculate radiological and toxicological doses to hypothetical persons inside either a U.S. Army Abrams tank or Bradley Fighting Vehicle that has been perforated by depleted uranium munitions are described. Data from time- and particle-size-resolved measurements of depleted uranium aerosol as well as particle-size-resolved measurements of aerosol solubility in lung fluids for aerosol produced in the breathing zones of the hypothetical occupants were used. The aerosol was approximated as a mixture of nine monodisperse (single particle size) components corresponding to particle size increments measured by the eight stages plus the backup filter of the cascade impactors used. A Markov Chain Monte Carlo Bayesian analysis technique was employed, which straightforwardly calculates the uncertainties in doses. Extensive quality control checking of the various computer codes used is described.