Bayesian Monte Carlo Analysis of mcDESPOT

Stochastic region contraction (SRC) has been proposed as an efficient approach for extracting system parameters from mcDESPOT data. However, the SRC algorithm exhibits a high degree of sensitivity to initial parameter conditions, especially at low-to-moderate signal-to-noise ratios. In this study, we investigated the accuracy and precision of component fraction determination in a bicomponent mcDESPOT model using two Bayesian methods, based respectively on maximum posterior probability and means, and compared the results with those derived using the SRC algorithm. Results show that the estimation of component fractions was markedly improved through use of Bayesian analysis.

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