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Gamerman, D. (1997) Markov chain monte carlo: Stochastic simulation for bayesian inference. Chapman & Hall.

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• TITLE: Bayesian analysis of minimal model under the insulin-modified IVGTT

  AUTHORS: Yi Wang, Kent M. Eskridge, Andrzej T. Galecki

  KEYWORDS: Minimal Model; Bayesian Analysis; IVGTT; Nonlinear Mixed-Effects Modeling; ODE

  JOURNAL NAME: Health, Vol.2 No.3, March 23, 2010

  ABSTRACT: A Bayesian analysis of the minimal model was proposed where both glucose and insulin were analyzed simultaneously under the insulin-modified intravenous glucose tolerance test (IVGTT). The resulting model was implemented with a nonlinear mixed-effects modeling setup using ordinary differential equations (ODEs), which leads to precise estimation of population parameters by separating the inter- and intra-individual variability. The results indicated that the Bayesian method applied to the glucose-insulin minimal model provided a satisfactory solution with accurate parameter estimates which were numerically stable since the Bayesian method did not require approximation by linearization.