Nonparametric Identification of Population Models: An MCMC Approach
Biomedical Engineering, IEEE Transactions（2008）
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.
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Key words
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 estimation,Markov Chain Monte Carlo,neural networks,nonparametric identification,pharmacokinetic data,regularization,splines
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