After more than 20 years, minimal model analysis of intravenous glucose tolerance test glucose and insulin concentrations continues to be widely employed in studies of insulin sensitivity and insulin resistance. Moreover, problems encountered in solving the minimal model equations continue to find new solutions. Bayesian techniques enable prior knowledge to be incorporated into parameter estimation routines. They offer particular advantages in the measurement of insulin sensitivity with the minimal model, and provide an elegant means of improving model identification success rates and parameter precision. This comment describes the study by Agbaje and colleagues in this issue of Clinical Science that exemplifies a new phase in the evolution of minimal model practice.

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