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• W2921974338 abstract "A mixed-effects ordinary differential equation (ODE) model is proposed to describe complex dynamical systems. In order to make the inference of ODE parameters robust against the outlying observations and subjects, a class of heavy-tailed distributions is applied to model the random effects of ODE parameters and measurement errors in the data. The heavy-tailed distributions are so flexible that they include the conventional normal distribution as a special case. An MCMC method is proposed to make inferences on ODE parameters within a Bayesian hierarchical framework. The proposed method is demonstrated by estimating a pharmacokinetic mixed-effects ODE model. The finite sample performance of the proposed method is evaluated using some simulation studies." @default.
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• W2921974338 date "2019-09-01" @default.
• W2921974338 modified "2023-09-24" @default.
• W2921974338 title "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions" @default.
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• W2921974338 doi "https://doi.org/10.1016/j.csda.2019.03.001" @default.
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