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Nonparametric identification of population models: an MCMC approach.
IEEE Trans Biomed Eng. 2008 Jan; 55(1):41-50.IT

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+Show Affiliations

Clinical Pharmacokinetics, Modelling and Simulation Department, GlaxoSmithKline Research Centre, Verona 37100, Italy.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18232345

Citation

Neve, Marta, et al. "Nonparametric Identification of Population Models: an MCMC Approach." IEEE Transactions On Bio-medical Engineering, vol. 55, no. 1, 2008, pp. 41-50.
Neve M, De Nicolao G, Marchesi L. Nonparametric identification of population models: an MCMC approach. IEEE Trans Biomed Eng. 2008;55(1):41-50.
Neve, M., De Nicolao, G., & Marchesi, L. (2008). Nonparametric identification of population models: an MCMC approach. IEEE Transactions On Bio-medical Engineering, 55(1), 41-50. https://doi.org/10.1109/TBME.2007.902240
Neve M, De Nicolao G, Marchesi L. Nonparametric Identification of Population Models: an MCMC Approach. IEEE Trans Biomed Eng. 2008;55(1):41-50. PubMed PMID: 18232345.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Nonparametric identification of population models: an MCMC approach. AU - Neve,Marta, AU - De Nicolao,Giuseppe, AU - Marchesi,Laura, PY - 2008/2/1/pubmed PY - 2008/2/27/medline PY - 2008/2/1/entrez SP - 41 EP - 50 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 55 IS - 1 N2 - 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. SN - 0018-9294 UR - https://www.unboundmedicine.com/medline/citation/18232345/Nonparametric_identification_of_population_models:_an_MCMC_approach_ L2 - https://dx.doi.org/10.1109/TBME.2007.902240 DB - PRIME DP - Unbound Medicine ER -