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This paper describes and compares two approaches for the problem of determining the number of radioactive point sources that potentially exist in a designated area and estimating the parameters of these sources (their locations and strengths) using a small number of noisy radiological measurements provided by a radiation sensor. Both approaches use the Bayesian inferential methodology but sample the posterior distribution differently: one approach uses importance sampling with progressive correction and the other a reversible-jump Markov chain Monte Carlo sampling. The two approaches also use different measurement models for the radiation data. The first approach assumes a perfect knowledge of the data model and the average background radiation level, whereas the second approach quantifies explicitly the uncertainties in the model specification and in the average background radiation level. The performances of the two approaches are compared using experimental data acquired during a recent radiological field trial.

Comparison of Two Approaches for Detection and Estimation of Radioactive Sources