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dc.contributor.authorSutharot Lueabunchongen_US
dc.contributor.authorYongwimon Lenburyen_US
dc.contributor.authorSimona Panunzien_US
dc.contributor.authorAlice Matoneen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherCentre of Excellence in Mathematicsen_US
dc.contributor.otherUniversita Cattolica del Sacro Cuore, Romeen_US
dc.identifier.citationInternational Journal of Mathematics and Computers in Simulation. Vol.6, No.3 (2012), 341-350en_US
dc.description.abstractIn this paper, the performances of Markov Chain Monte Carlo (MCMC) method and Generalized Least Square (GLS) method are compared when they are used to estimate the parameters in a nonlinear differential model of glucose/insulin metabolism with GLP1-DPP4 interaction. The model is used to generate the data that consists of the time-concentration measurements of plasma glucose and of insulin, which are important in Diabetes Mellitus (DM) treatment. We show the results from three different runs to obtain parameter estimations by both MCMC and GLS. The true values (TV), point estimates (PM), standard deviation (SD) and 95% credible intervals (CI) of population parameters based on the two methods are presented. Our results suggest that MCMC is better able to estimate the parameters based upon smaller bias and standard deviation. Although MCMC requires more calculation time than GLS, it offers a more appropriate method, in our opinion, for nonlinear model parameter estimations without knowledge of the distribution of the data and when heterogeneity of variance is evident.en_US
dc.rightsMahidol Universityen_US
dc.subjectComputer Scienceen_US
dc.titleComparison of Markov chain Monte Carlo and generalized least square methods on a model of glucose / insulin dynamics with GLP1-DPP4 interactionen_US
Appears in Collections:Scopus 2011-2015

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