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Bayesian Updating of Atmospheric Dispersion Models for Use After an Accidental Release of Radioactivity

Jim Smith and Simon French
Journal of the Royal Statistical Society. Series D (The Statistician)
Vol. 42, No. 5, Special Issue: Conference on Practical Bayesian Statistics, 1992 (2) (1993), pp. 501-511
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2348675
Stable URL: http://www.jstor.org/stable/2348675
Page Count: 11
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Bayesian Updating of Atmospheric Dispersion Models for Use After an Accidental Release of Radioactivity
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Abstract

We report on the development of the Bayesian forecasting and uncertainty handling components of a decision support for emergency management in the event of an accidental release of radioactivity. In particular, we focus on the forecasting of the spread of the contamination. We describe a simple but novel form of stochastic Bayes linear model. This closely mirrors well-developed (Gaussian) puff atmospheric dispersion models, but admits effective Bayesian learning procedures on the values of uncertain variables. Several features of the model are highlighted and its workings illustrated on (partially simulated) test data.

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