Nonparametric identification of population pharmacokinetic models: an MCMC approach

The paper deals with the nonparametric identification of population models, that is models that explain jointly the behaviour of different subjects drawn from population, e.g. responses of different patients to a drug. The average response of the population and the individual responses are modelled as continuous-time Gauss processes with unknown hyperparameters. The posterior expectation and variance of both the average and individual curves are computed by means of a Markov Chain Monte Carlo scheme. The model and the estimation procedure are tested on xenobiotics pharmacokinetic data.