Title :
Nonparametric Identification of Population Models: An MCMC Approach
Author :
Neve, Marta ; De Nicolao, Giuseppe ; Marchesi, Laura
Author_Institution :
GlaxoSmithKline Res. Centre, Verona
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.
Keywords :
Bayes methods; Gaussian processes; Markov processes; Monte Carlo methods; data analysis; drugs; expectation-maximisation algorithm; medical computing; nonparametric statistics; patient treatment; Bayesian paradigm; Markov Chain Monte Carlo scheme; biomedical data analysis; continuous-time Gaussian processes; drug; nonparametric identification; pharmacokinetic data; population models; posterior expectation; Bayesian methods; Computational modeling; Data analysis; Drugs; Gaussian processes; Monte Carlo methods; Neural networks; Parametric statistics; Performance analysis; Testing; Bayesian estimation; Markov Chain Monte Carlo; neural networks; nonparametric identification; pharmacokinetic data; regularization; splines; Algorithms; Animals; Computer Simulation; Data Interpretation, Statistical; Humans; Markov Chains; Models, Biological; Models, Statistical; Monte Carlo Method; Population Dynamics;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2007.902240
