Bayesian Monte Carlo Analysis of mcDESPOT
Mustapha Bouhrara 1 and Richard G. Spencer 1
National Institute on Aging, NIH, BALTIMORE,
Maryland, United States
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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