title: Nonparametric identification of population models: an MCMC approach.
abstract: The paper deals with the nonparametric identification of population models, that is models that explain jointly the behavior of different subjects drawn from a population, e.g., responses of different patients to a drug. The average response of the population and the individual responses are modeled as continuous-time Gaussian processes with unknown hyperparameters. Within a Bayesian paradigm, 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 both simulated and experimental pharmacokinetic data.
authors.affiliation: Clinical Pharmacokinetics, Modelling and Simulation Department, GlaxoSmithKline Research Centre, Verona 37100, Italy.
authors.forename: Marta
authors.initials: M
authors.lastname: Neve
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
authors.forename: Laura
authors.initials: L
authors.lastname: Marchesi
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011157:Population Dynamics